https://wiki.communitydata.science/api.php?action=feedcontributions&user=Nickmvincent&feedformat=atomCommunityData - User contributions [en]2024-03-28T16:10:54ZUser contributionsMediaWiki 1.38.4https://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=266860User:Aaronshaw/AdvisingOH2023-04-21T16:55:55Z<p>Nickmvincent: /* April 24 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me at least 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Spring 2023) quarter signups ==<br />
<br />
=== April 17 ===<br />
* 1500-1530: floor<br />
* 1530-1600: Emily Zou<br />
* 1600-1630: <blocked for another mtg><br />
* 1630-1700: Mandi<br />
<br />
=== April 19 ===<br />
* 1400-1430: kevin<br />
<br />
=== April 24 ===<br />
* 1500-1530: Nick V!<br />
* 1530-1600: carolyn<br />
* 1600-1630: Tommy<br />
<br />
=== April 26 ===<br />
* 1400-1430: kevin or carl<br />
<br />
=== May 1 ===<br />
* 1500-1530: floor<br />
* 1530-1600: floor<br />
* 1600-1630: Mandi<br />
<br />
=== May 3 ===<br />
* 1400-1430: kevin<br />
<br />
=== May 8 ===<br />
* 1500-1530: sohyeon<br />
* 1530-1600: sohyeon<br />
* 1600-1630: Tommy<br />
<br />
=== May 10 ===<br />
* 1400-1430: kevin or carl<br />
<br />
=== May 15 ===<br />
* 1500-1530: Mandi<br />
* 1530-1600: Mandi<br />
* 1600-1630: Jaelle<br />
<br />
=== May 17 ===<br />
* 1400-1430: kevin<br />
<br />
=== May 22 ===<br />
* 1500-1530: floor<br />
* 1530-1600: sohyeon<br />
* 1600-1630: sohyeon<br />
<br />
=== May 24 ===<br />
* 1400-1430: kevin or carl<br />
<br />
=== May 29 ===<br />
'''Cancelled to attend ICA'''<br />
<br />
=== May 31 ===<br />
* 1400-1430: kevin<br />
<br />
=== June 5 ===<br />
* 1500-1530: sohyeon<br />
* 1530-1600: Mandi<br />
* 1600-1630: Tommy<br />
<br />
=== June 7 ===<br />
* 1400-1430: kevin or carl</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=People&diff=266044People2023-02-17T16:48:51Z<p>Nickmvincent: /* Affiliate Researchers */ moving Nick Vincent</p>
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<div>We're an interdisciplinary group at Carleton College, Northwestern University, Purdue University and the University of Washington. Faculty, postdocs, graduate students, affiliates, and alumni are listed below (in alphabetical order within each section) except when we've failed at alphabetizing.<br />
<br />
You can see pictures of all together over at our [[group photos]] page. Pictures of us individually are here.<br />
<br />
We are a friendly group and we welcome new affiliates! If you have been working with us for a while, perhaps it's time to add yourself to this page as an affiliate. Feel free to add yourself (use the Edit tab) in the appropriate subsection organized alphabetically by last name, and please include a sentence on HOW you are related to the group (and a fun picture of yourself!).<br />
<br />
Shorter summaries of [[research interests]] are also available.<br />
<br />
== Faculty ==<br />
<div style="clear:both;"><br />
<br />
=== Jeremy Foote (Purdue University) ===<br />
<br />
[[File:Jeremy.jpg|thumb|200px|Jeremy and his family on a very flat Midwest hike]]<br />
<br />
:'''Pronouns:''' he/him<br />
<br />
I grew up in Nevada, did my undergrad (in English!) at BYU in Utah, and then worked as a practitioner of online collaboration. I was the product manager for a small [https://www.lingotek.com/ collaborative translation company] in Utah. I decided that I cared a lot more about understanding collaboration than designing software, and I came back to school. I did a Master's degree at Purdue, studying with [https://www.cla.purdue.edu/communication/directory/?p=Seungyoon_Lee Seungyoon Lee], and then worked on a PhD at Northwestern, as a member of CDSC. I'm now back at Purdue in the [https://www.cla.purdue.edu/academic/communication/ Brian Lamb School of Communication], this time as a faculty member.<br />
<br />
Most of my current research is focused around understanding how people decide where to participate in online communities--why people start new communities, how community membership influences future behavior, and how communication structures relate to community outcomes. I'm particularly interested in how these decisions scale up into the social construction of understanding, knowledge, and opinion. More about my research is at my [http://www.jeremydfoote.com academic homepage].<br />
<br />
Much of my spare time is spent with my family (my wife and I have 5 kids!) or with my [http://www.mormon.org church community]. I love the Midwest but really miss hiking and skiing in the mountains and try to do both as much as possible.<br />
</div><br />
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<div style="clear:both;"><br />
<br />
=== Benjamin Mako Hill (University of Washington) ===<br />
<br />
[[File:Mako-Meitu-201701.jpg|thumb|200px|Unedited picture of Mako in Berlin (2016).]]<br />
<br />
:'''Pronouns:''' he/him<br />
<br />
After contributing peer production communities in various ways since I was a teenager, I began to realize (the hard way) that peer production rarely works and that getting it to work remained much more art than science. After being talked into the idea that academia was the right place to fix this by [https://evhippel.mit.edu/ Eric von Hippel], I've devoted the last decade of my life to trying to contribute to an emerging science of Internet-based collaborative production. Since starting as an academic, I have published tens of thousand of [https://www.wikidata.org/wiki/Q103184 articles]—nearly all of them are [https://www.wikidata.org/wiki/Lexeme:L2768 the].<br />
<br />
In the more boring accounting (which I've copied and pasted from elsewhere): I am an Associate Professor in the University of Washington Department of Communication and an Adjunct Associate Professor in the departments of Human-Centered Design and Engineering as well as in the Information School and the Paul G. Allen School of Computer Science and Engineering. At UW, I am also Affiliate Faculty in the Center for Statistics and the Social Sciences, the eScience Institute, and the "Design Use Build" (DUB) group that supports research on on human computer interaction. I am also a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University and an affiliate of the Institute of Quantitative Social Science at Harvard.<br />
<br />
Much more information is on [https://mako.cc/academic/ my academic homepage]. If you need to find me, I have put [https://mako.cc/contact/ more detailed contact information online] than I probably should.<br />
<br />
</div><br />
<div style="clear:both;"><br />
<br />
=== Sneha Narayan (Carleton College) ===<br />
<br />
[[File:Snehaphoto.jpg|thumb|200px|Sneha hanging out by Lake Michigan]]<br />
I'm an Assistant Professor of Computer Science at [https://www.carleton.edu/ Carleton College]. Before that, I did my PhD in the Technology and Social Behavior program at Northwestern University, advised by Aaron Shaw (whose bio you can find by scrolling up a couple of sections). I grew up in Bangalore, India, studied mathematics at Oberlin College, and received a masters degree in Sociology and Social Anthropology from Central European University, Budapest.<br />
<br />
I've spent many years living in housing co-ops, and volunteering on the boards of co-operative organizations. My involvement in the co-op movement led to my interest in learning more (and producing knowledge) about participatory, volunteer-run endeavors such as peer production projects and online collaboration communities. My research focuses on understanding how newcomers join and become embedded in volunteer-run organizations, and what kinds of technological interventions might affect their continued participation in these communities. For (slightly) more information about all this, you can check out my [http://www.snehanarayan.com/ homepage].<br />
</div><br />
<br />
<br />
=== Aaron Shaw (Northwestern University) ===<br />
<br />
[[File:Shaw-2017.jpg|thumb|250px|Airbrushed, filtered, and meitu'd purikura of Aaron from 2017]] <br />
<br />
Hello! I'm Aaron (he/they). I grew up around New York and went to school for a while in northern California. Along the way, I got involved in participatory movements and projects of various kinds. At first, these were more traditional movements advancing egalitarian social agendas. Over time, I got involved in peer production projects, online communities, and other sorts of open collaboration online.<br />
<br />
These days, I am an Associate Professor in the Department of Communication Studies at Northwestern where I am affiliated with the [http://mts.northwestern.edu Media, Technology & Society (MTS) Program] and the [http://tsb.northwestern.edu Technology & Social Behavior Program], courtesy appointed in the Sociology Department, a faculty associate of the Institute for Policy Research, the Buffett Institute for Global Affairs, the Center for Human-Computer Interaction + Design, and the SONIC lab. Elsewhere, I am a faculty associate of the [http://cyber.law.harvard.edu Berkman Klein Center for Internet and Society] at Harvard University. A good place to find more information is [http://aaronshaw.org my website]. If you'd like to get in touch, please [mailto:aaronshaw@northwestern.edu send me an email] (and don't be shy about re-sending if I don't reply).<br />
</div><br />
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<div style="clear:both;"><br />
<br />
== Staff ==<br />
<br />
=== Molly de Blanc (Northwestern University) ===<br />
<br />
I do communications, outreach, and research support for CDSC. This means that, among other things, I think about how our work can help the communities we study and other online communities. My CDSC research is about how community values are embodied in policies and rules.<br />
<br />
Before joining CDSC, I worked in open education and free and open source software. I recently completed a degree in Bioethics from New York University. My work was on health care technologies, autonomy, and the tools we use to protect autonomy, including informed consent and privacy. My thesis was on Right-to-Repair and implanted medical devices.<br />
<br />
I spend most of my time reading, but sometimes bake bread, I write and play music, hang out with my cat, ride a bike, and swim. I wrote [https://techautonomy.org The Declaration of Digital Autonomy] with Karen Sandler.<br />
<br />
I can be reached at molly [dot] deblanc [at] northwestern [dot] edu. Feel free to address emails to "Molly," but if you insist on using a salutation "Mx." is fine.<br />
<br />
== Postdocs == <br />
<br />
<div style="clear:both;"><br />
=== Nathan TeBlunthuis (Northwestern University) ===<br />
{{User:groceryheist/bio}}<br />
<br />
== Graduate Students ==<br />
<br />
<div style="clear:both;"><br />
<br />
=== Kevin Ackermann (Northwestern University) ===<br />
Hiiii! I'm Kevin. (∩`-´)⊃━☆゚.*・。゚<br />
<br />
I'm a first year PhD Student in the Media, Technology and Society program at Northwestern University. I'm interested in studying impacts of commercialization on digital community space, historicizing platform governance conversations, and thinking about the political (and emotional) ramifications of quantification. In the past, my attempts to study these topics have largely revolved around studying dead computer networks of the 80s and 90s. Said another way, I'm interested in how communities form and falter online, and what it's like to be a part of one. <br />
<br />
I've spent years of my pre-PhD life honing archival methodology skills, so I'm a sucker for qualitative storytelling, but I hope to try out myriad methods in my graduate studies. There are so many ways to know things!<br />
<br />
</div><br />
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<div style="clear:both;"><br />
=== Kaylea Champion (University of Washington) ===<br />
<br />
[[File:Kaylea-Champion-300x300.jpg|thumb|200px|Kaylea in purple and blue.]]<br />
<br />
I (she/her) am investigating how organizations collaborate to build information public goods -- groovy things like Linux and Wikipedia. What gets made and maintained -- and what gets neglected?<br />
<br />
After growing up in Oregon, I spent two decades in Chicago, primarily at the University of Chicago as an academic technology director and consultant. I have a BA in Near Eastern Languages and Civilizations and an MS in Computer Science, both from the University of Chicago. I also hold an MA in Critical & Creative Thinking from the University of Massachusetts, Boston.<br />
<br />
My husband, three kids, and I live in Shoreline, WA, which seems to be Seattle's version of Evanston. I'm particularly fond of visiting museums, tromping in the woods, cooking for crowds, smashing goblins, and scribbling fiction.<br />
<br />
</div><br />
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<div style="clear:both;"><br />
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=== Regina Cheng (University of Washington) ===<br />
<br />
[[File:Regina hime.JPG|thumb|200px|Regina with her new feline best friend, [https://www.instagram.com/hime_theprincesscat/ Hime].]]<br />
<br />
I'm a PhD candidate in the Human-Centered Design and Engineering department at University of Washington, co-advised by Mako Hill and Jennifer Turns. I describe my research goal as to understand and support collaborative informal learning in online communities of creators. I am interested in studying how different types of collaborative activities (e.g. feedback exchange, collaborative sense-making) lead to different learning outcomes, and designing for more effective collaboration to facilitate learning. Right now I am especially interested in the domain of data science learning among non-technical population.<br />
<br />
Outside research, I like cats, drawing (mostly fanart these days), reading, cooking, hiking, hapkidoing, and preaching about my mother tongue, [https://en.wikipedia.org/wiki/Hangzhou_dialect Hangzhou dialect] <br />
<br />
</div><br />
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<div style="clear:both;"><br />
=== Hsuen-Chi (Hazel) Chiu (Purdue University) ===<br />
[[File:Hazel games.JPG|thumb|200px|Hazel in May 2022]]<br />
<br />
Hello! I am a First year PhD student in the Brian Lamb School of Communication at Purdue University and I am on Media, Technology and Society track. I am advised by Dr. Jeremy Foote. I study computer-mediated communication, especially using quantitative and computational approaches. I am interested in seeing how people using different affordances on social media to manage their privacy, identity and self-disclosure across platforms. I am also interested in looking at how misinformation spreads on social media.<br />
<br />
Before coming to Purdue, I earned my MS degree in Media Science focusing on Marketing Communication Research at Boston University.<br />
<br />
Outside research, I like baseball games, foods, traveling and dogs.<br />
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=== Carl Colglazier (Northwestern University) ===<br />
<br />
{{User:Carl/bio}}<br />
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</div><br />
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<div style="clear:both;"><br />
=== Stefania Druga (University of Washington) ===<br />
<br />
[[File:Stef2019.jpg|thumb|200px|Stef in Summer of 2019, [//anoxic.me/huli Fancy].]]<br />
<br />
I'm a first-year Ph.D. student in the Information School at the University of Washington, co-advised by Jason Yip and Alexis Hiniker. I am the co-founder of Cognimates and HacKIDemia. My research focuses on how children interact with and make sense of the growing collection of “smart” inter-connected playthings in the world around them together with their parents. I am exploring how families, as they play with these new smart assistants and applications, develop new ways of thinking about intelligence, emotion, and social interaction. Based on these studies, I am designing new tools and activities to introduce families to machine learning and data science in a playful way. <br />
<br />
Outside research, I like climbing, dogs, reading, dancing and learning new languages. <br />
<br />
</div><br />
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=== Yibin Fan (University of Washington) ===<br />
Hi ;) I am a first year MA/PhD student in the Department of Communication at University of Washington. My graduate advisor is Professor Benjamin Mako Hill. My research interest is focued on digital group dynamics, and I am deeply curious about questions like how online communities connect and influence each other, or when and why group polarization forms. I am glad to include both quantitive and qualitative methods in my research, and also looking forward to learning more social scientific methods to see whether they make effects in different areas or topics.<br />
<br />
</div><br />
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=== Floor Fiers (Northwestern University) ===<br />
<br />
[[File:Small.FloorFiers.jpg|thumb|200px|Floor on their way back to the US in the midst of the pandemic]]<br />
<br />
:'''Pronouns:''' they/she<br />
<br />
<br />
Hi there! I am is a PhD Candidate in the [https://communication.northwestern.edu/programs/phd_media_technology_society Media, Technology and Society program ] at Northwestern Uni. Academically speaking, I am interested in the field of digital inequality, particularly as it relates to online labor markets and the gig economy. Outside academia, I love (cold water) swimming and rollerblading, and I find lots of energy in organizing two music & theater festivals in the Netherlands.<br />
<br />
Originally from the Netherlands, I first came to the US attend the [https://www.uwc.org/ United World College ] (Montezuma, NM), after which I pursued a BA in Sociology from [https://www.stlawu.edu/ St. Lawrence University ] (Canton, NY). During the pandemic, I worked remotely from the University of Zurich's [https://www.ikmz.uzh.ch/en/research/divisions/internet-use-and-society/team.html Internet & Society division]. For more background, see [https://www.floorfiers.com my website].<br />
</div><br />
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=== Ryan Funkhouser (Purdue University) ===<br />
<br />
Hey! My name is Ryan Funkhouser, I'm a first year PhD student in the Brian Lamb School of Communication at Purdue University, and I study conflict in communication across difference. In particular, I'm interested in using computational approaches to studying online communities and the ways in which they can foster discourse that reduces incivility and increases understanding across lines of ideological conflict.<br />
<br />
Before studying at Purdue, I earned an interdisciplinary humanities MA at Trinity Western University in British Columbia where I studied rhetoric and communication. I also began a second masters, this time specifically in communication, at the University of Wisconsin-Milwaukee. <br />
<br />
I originally hail from the beautiful city of Bellingham in the PNW, a place which nurtured within me a love for mountains and long-distance trail running. While I am living a relatively mountain-less existence in West Lafayette, Indiana while at Purdue, I continue to find joy in running and finding the beauty in the midwest. When not running, you will likely me and my wife watching a good show or going for walks.<br />
<br />
</div><br />
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=== Emilia Gan (University of Washington) ===<br />
<br />
[[File:EGan.jpg|200px|thumb|Emilia G.]]<br />
<br />
I'm a PhD candidate in the [https://www.cs.washington.edu/ Paul G. Allen School of Computer Science & Engineering] at the University of Washington (Seattle). My research has involved analyzing data from the [https://scratch.mit.edu/ Scratch programming platform] (Link: [https://mako.cc/academic/gan_hill_dasgupta-gender_feedback_sharing-CSCW18.pdf paper]) and from [https://codeday.org/ CodeDay]. I am interested in factors that promote longterm participation in coding by newcomers to programming.<br />
<br />
Before starting graduate school in CS, I earned an MS ([https://globalhealth.washington.edu/education-training/phd-pathobiology Pathobiology]) from UW. I initially started learning how to program with the thought of using these skills for analyzing large biological data sets, but I eventually realized everything I was doing was pointing me away from biology and towards computer science. <br />
<br />
Before starting graduate school at UW, I homeschooled with my kids for over a decade, and before that I earned an MD from the [https://www.umassmed.edu/ University of Massachusetts Medical School] and a BS in Materials Science and Engineering from [https://dmse.mit.edu/ MIT].<br />
<br />
[https://emilia.cloud/ Personal Website]<br />
<br />
</div><br />
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=== Wm Salt Hale (University of Washington) ===<br />
[[File:Salt_Xmas.jpg|thumb|200px|Salt shedding the holiday cheer (2016)]]<br />
<br />
Growing up in Seattle during the early 90s offered many technological opportunities, most of which I took advantage of. As an avid GNU/Linux user for over 20 years, I have been exposed to numerous technology orientated communities on various levels.<br />
<br />
During high school I entered the Running Start program, completing an Associate's degree in Computer Science from South Seattle College. After which I transfered to the University of Washington, pursuing the same major. It was not a fit, instead I developed a number of businesses, traveled, and spoke at various conferences, conventions, events, faires, and festivals.<br />
<br />
Upon returning to the University of Washington to complete my Batchelor's degree in Communication, I connected with [[Mako]] and was shown a world of academia previously unimagined. After another year of traveling, I have decided to return to the UW Department of Comm yet again and am just beginning to delve deeper into the intersection of Technology and Society in the MA/PhD program.<br />
<br />
I am extremely interested in: Free/Libre/Open Source Software (FLOSS) and Culture; Hackers, Makers, and Breakers; and Computer-Mediated Communication using real-time synchronous systems. Along with numerous hobbies including: urban hiking (walking), dancing (folk, east coast swing, lindy, blues), windsports (windsurfing, kiteboarding, sailing), bicycling, boffering, cooking, driving, event planning, gaming, programming, public speaking, reading, robotics, skiing, and travel.<br />
<br />
Up to date information and links to various profiles around the web can be found on ''my'' IndieWeb presence, [http://www.altsalt.net/ The Alt World of Salt].<br />
<br />
</div><br />
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=== Sohyeon Hwang (Northwestern University) ===<br />
<br />
[[File:Sohyeonhwang.jpg|thumb|200px|Sohyeon and her dog-child, Tubby.]]<br />
<br />
I'm Sohyeon (she/they), a third-year PhD student in the Media, Technology, and Society program at Northwestern University, advised by Aaron Shaw. My research interests broadly circle around online governance, mostly around ideas of heterogeneity, scale, and polycentric + decentralized models. I focus on the complexities arising in governance, such as how online groups diversely interpret, innovate beyond, subvert, and co-opt socio-technical affordances to manage themselves.<br />
<br />
I am pretty methods-agnostic, doing both computational/quantitative approaches as well as qualitative work here and there. You can find more information at my [https://www.sohyeonhwang.com site].<br />
<br />
Outside of work, I like to eat french fries (love poutine) and take (blurry but not by choice) film photos. <br />
<br />
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=== Dyuti Jha (Purdue university) ===<br />
[[File:DS(1).png|thumb|200px|Dyuti when she used to have time to go out.]]<br />
<br />
Hi there! I am a first year PhD student at the Lamb School at Purdue. Dr Jeremy Foote is my advisor. My interests sit at the intersection of sociology, political science, and communication My work has largely been qualitative in the past but I am interested to learn computational methods and use them to study political aggression and violence in online communities. I worked in the Indian nonprofit sector for five years before deciding to come back to academia. As I find my way around what other things interest me, you will see them here!<br />
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Outside of work, I love playing my ukulele and singing, watching and analysing trashy films from all over the world, and cooking.<br />
<br />
</div><br />
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=== Charles Kiene (University of Washington) ===<br />
<br />
[[File:Ch2.jpg|thumb|200px|Charlie.]]<br />
<br />
I am currently a PhD candidate in the Department of Communication at the University of Washington in Seattle, WA. I am advised by Professor Benjamin Mako Hill. As part of my doctoral research, I study organizational behavior of volunteer-based groups that manage communities in computer-mediated, online settings, such as Discord servers, subreddits, and MMORPG guilds. <br />
<br />
Topics include:<br />
<br />
* Massive influxes of newcomers<br />
* Technological change and adaption<br />
* Organizational culture and conflicts<br />
* Emergence and evolution of rules<br />
* Turnover and division of labor<br />
<br />
I use interviewing and ethnographic research methods for inductive qualitative studies of the groups that manage online communities. I also use computational social science methods (programming and maintaining automated web crawling software in SQL databases; machine learning; statistical modeling) for collecting and analyzing data as part of both descriptive and deductive research studies of online communities.<br />
<br />
<br />
Details at [[User:Healspersecond]]<br />
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=== Ellie Ross (University of Washington) ===<br />
<br />
[[File:Elliew-butterfly.JPG|thumb|200px|Ellie at Turtle Bay.]]<br />
<br />
I'm a first year MA/PhD student in the Communication Department at University of Washington, advised by Mako Hill. I am currently using the splitting of online communities to evaluate two age old hypotheses and derive what kinds of value are offered by core and periphery members of a network structure. <br />
<br />
Outside of the University, I play video games and watch old tv shows. I have an adventure cat named Bengie and a stay at home cat named Nala. <br />
</div><br />
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== Undergraduate Students ==<br />
=== Serene Ong (Northwestern University) ===<br />
<br />
I am a current junior at Northwestern studying Cognitive Science with a concentration in Artificial Intelligence as well as Psychology and minoring in Computer Science. I'm interested in understanding the motivations behind human decision making and why there tends to be consistent patterns of irrationality. I plan on entering the consulting field post graduation. Outside of school I like to crotchet, watch nature documentaries, and explore Chicago!<br />
<br />
</div><br />
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<br />
=== Research Assistants ===<br />
* Marianne Cano, Northwestern University<br />
* Noah Hellyer, University of Washington<br />
* Divya Sikka, Interlake High School<br />
* Grace Zhu, Northwestern University<br />
* Carolyn Zou, Northwestern University<br />
* Emily Zou, Northwestern University<br />
<br />
=== Alums ===<br />
* Marlene Alanis, Northwestern University<br />
* Gabrielle Alava, Northwestern University<br />
* Paz Baum, Northwestern University<br />
* Dylan Griffin, Northwestern University<br />
* Amy Guo, Northwestern University<br />
* Matthew Holleran-Meyer, Northwestern University<br />
* Daryn McElroy, Northwestern University<br />
* Eric Rosin, Northwestern University<br />
* Donny Tou, Northwestern University<br />
* Davida Yalley, Northwestern University<br />
* Hannah Yang, Northwestern University<br />
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== Affiliate Researchers ==<br />
<div style="clear:both;"><br />
=== Mad Price Ball (Open Humans Foundation) ===<br />
[[File:Mad-portrait-photo-201910.jpg|200px|thumb|Mad eagerly tearing apart [https://twitter.com/madprime/status/979833858039271425 another terrible blockchain idea].]]<br />
<br />
I am Executive Director of Open Humans Foundation and co-founder of [https://www.openhumans.org Open Humans]. My research involvement is more "meta" these days: I help others do it. With Open Humans, we try to enable a new approach for research in health and human subjects research, focusing on personal data. Our work is generally "open" and strives to enable peer production, enabling individuals to create and share tools for getting personal data, analyzing it, and potentially contributing it to aggregate projects (from patient groups to citizen scientists, as well as traditional academic studies). I'm also a Shuttleworth Foundation Fellow (alum) and a member of the BoD of MyData Global.<br />
<br />
Open Humans was inspired by my dual histories in genomics research and free/open culture. My PhD was in biotech and postdoc work involved running George Church's Personal Genome Project, which invited people to donate genome & health data to science by making it public – where I learned a lot about personal data and human subjects research. I'm also familiar with free/open culture folks for well over a decade, contributing here and there; one of my favorite past projects was helping create an offline copy of Wikipedia for OLPC distributed in Peru & Uruguay (my role was creating the article list, mostly based on traffic & connectivity data).<br />
<br />
I live in San Diego, but online you can find me on [http://twitter.com/madprime @madprime on Twitter], in the [http://slackin.openhumans.org Open Humans slack group], and sometimes IRC (madprime) – or reach me by email (mad) at openhumans.org.<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Tilman Bayer ===<br />
[[File:Tilman at Internet Archive 2018.jpg|thumb|170px|Tilman sitting in the [https://en.wikipedia.org/wiki/Internet_Archive Internet Archive's] pews, piously contemplating the world's knowledge]]<br />
I am a longtime Wikipedia contributor (as [[:w:User:HaeB|User:HaeB]]) and editor of the [https://meta.wikimedia.org/wiki/Research:Newsletter Wikimedia Research Newsletter], a monthly publication surveying and reviewing recent academic research about Wikipedia and other Wikimedia projects, which I co-founded in 2011 with my then-colleague Dario Taraborelli at the Wikimedia Foundation. I am also one of the two maintainers of the associated [https://twitter.com/wikiresearch @WikiResearch] Twitter feed. For the past several years, I have joined Mako, Aaron and others in presenting an annual [https://wikimania2018.wikimedia.org/wiki/Program/State_of_Wikimedia_Research_2017-2018 "State of Wikimedia Research"] overview at the Wikimania community conference, where I have also presented on other data and research topics such as the question [https://upload.wikimedia.org/wikipedia/commons/e/e1/Which_parts_of_a_%28Wikipedia%29_article_are_actually_being_read_%28Wikimania_2018%29.pdf which parts of a Wikipedia article people actually read]. <br />
<br />
My work as a data analyst on the Wikimedia Foundation's [https://www.mediawiki.org/w/index.php?title=Product_Analytics&oldid=3173327 Product Analytics team] included controlled experiments and exploratory data analysis to support the development of new software features for Wikipedia readers and contributors, and the analysis of core readership metrics like pageviews. With the Foundation's web team, I drove the implementation of a new metric designed to better understand reader engagement, based on an instrumentation of time spent on page (dwell time). This became the subject of a [https://meta.wikimedia.org/wiki/Research:Reading_time research project] with Nate TeBlunthuis and my then-colleague Olga Vasileva, with findings e.g. about differences in reading behavior between users in the Global South and the Global North.<br />
<br />
My academic background is in pure mathematics, with degrees from the University of Cambridge and the University of Bonn. I am based in San Francisco and can be reached via Gmail ("HaeBwiki") and as "HaeB" on IRC (Freenode).<br />
</div><br />
<br />
<br />
<div style="clear:both;"><br />
=== Sayamindu Dasgupta (University of Washington) ===<br />
<br />
[[File:Sayamindu.jpg|thumb|200px|Sayamindu, mildly perturbed.]]<br />
<br />
After getting a PhD from MIT, I was a postdoctoral fellow at the University of Washington's eScience Institute and was hosted by CDSC over 2017-2018. I then spent three and a half years as an assistant professor at the School of Information and Library Science, UNC Chapel Hill, and I am currently an assistant professor the University of Washington's department of Human Centered Design and Engineering where I study, design, and build pathways that engage young people in learning with data and digital technologies. Our lab is called the [https://depts.washington.edu/ledlab/ Learning, Epistemology, and Design Lab (LED Lab)].<br />
<br />
You can find more about my work on my [https://unmad.in homepage].<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Bastian Greshake Tzovaras (Center for Research & Interdisciplinarity, Université Paris Descartes) ===<br />
[[File:BastianGreshakeTzovaras.jpg|200px|thumb|Bastian, being so old-timey that his beard has grown.]]<br />
<br />
Despite having an academic background in biology/bioinformatics, I've been active in peer-produced citizen science since around 2011. I'm one of the co-founders of the crowdsourced, open data repository openSNP ([https://opensnp.org]), which collects personal genomics data sets from users of Direct-To-Consumer genetic testing companies to put them into the public domain. Since 2017 I'm also the Director of Research for Open Humans (https://www.openhumans.org), an ecosystem for participatory citizen science that aims to allow people to analyze and learn from their own personal data as well as given members the opportunity to share their data with (citizen science) research projects. Among other things we have piloted a JupyterHub-based approach to give people their own virtual machines that allow them to write, run and share data analysis notebooks without having to share any personal information (see [https://exploratory.openhumans.org]).<br />
<br />
Since 2019 I'm a research fellow at the Center for Research & Interdisciplinarity in Paris ([https://cri-paris.org/]), where I will study how the ideas of peer-production can be translated to facilitate co-created citizen science projects in which participants are fully involved in all stages of research, from start to finish. Lately a lot of focus there has been on how we can scale up the individualistic quantified self experiments people do to larger cohorts. I also teach students the basics of citizen science and self-tracking. <br />
<br />
Last but not least I'm involved in community building and mentoring in bioinformatics and for open projects in general: I'm a board member of the Open Bioinformatics Foundation ([https://www.open-bio.org/]), have mentored for Mozilla's Open Leadership Cohorts, Outreachy & Google Summer of Code.<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Andrés Monroy-Hernández (Snap Research) ===<br />
[[File:andresmh.jpg|thumb|180px|🚀]]<br />
<br />
I'm a researcher at [https://www.snap.com/ Snap Inc.] and an affiliate faculty at the University of Washington. My work focuses on the study and design of social computing systems. Some areas I've worked on are crowdsourcing, peer production, remixing, civic tech, urban computing, and online learning.<br />
<br />
Some projects I've worked on lately include [http://calendar.help Calendar.help], a hybrid intelligence scheduling assistant partly powered by crowds; Narcotweets, a research project studying how people use social media during war and political uprisings; and the [http://scratch.mit.edu Scratch Online Community], a website where millions of young people learn to program and remix games and animations. <br />
<br />
You can find me at [http://twitter.com/andresmh @andresmh] or at [http://andresmh.com/ www.andresmh.com].<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Jonathan T. Morgan (Crowdstrike) ===<br />
<br />
[[File:Jtm_profile_pic.jpg|thumb|200px|Jonathan in his preferred horizontal orientation.]]<br />
<br />
I'm a UX researcher at CrowdStrike and an affiliate faculty member in the UW department of Human Centered Design & Engineering. Most of my research involves understanding the sociotechnical mechanisms through which people who use complex collaborative software systems coordinate their work across time and space. You can find out more about me and my work [https://meta.wikimedia.org/wiki/User:Jmorgan_(WMF) here] and [http://jtmorgan.net/ here].<br />
<br />
I am a founding mentor for the [[Community_Data_Science_Workshops|Community Data Science Workshops]], and I also develop and teach UW courses on related topics, like [[Human_Centered_Data_Science|Human Centered Data Science]]. <br />
<br />
I am a voracious and omnivorous reader, and a passionately amateurish musician. When I'm away from the keyboard, you can usually find me exploring the beaches and forests of Puget Sound with my wife and my dog, [[w:Ozymandias|Ozymandias]].<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Morten Warncke-Wang (Wikimedia Foundation) ===<br />
[[File:Warncke-Wang, Morten - Dec 2017.jpg|200px|thumb|Morten prior to growing a scientifically sound beard.]]<br />
<br />
I've been participating in online and peer production communities for over 20 years, and recently (December 2016) got a PhD studying them. My research focus has been on content quality in peer production communities like Wikipedia and OpenStreetMap: what is high quality content, how is it created, can we build tools to judge it, and is it produced where there is demand for it? In addition to research publications, this work has also led to a Python library for predicting Wikipedia article quality ([https://github.com/wiki-ai/articlequality articlequality]) that is publicly available on Wikipedia through the [https://www.mediawiki.org/wiki/ORES ORES API]. I am also a Research Fellow with the [https://research.wikimedia.org Wikimedia Foundation's Research group].<br />
<br />
Another one of my interests is using recommender systems to help contributors find work to do. In Wikipedia this manifests in my maintenance of [https://en.wikipedia.org/wiki/User:SuggestBot SuggestBot]. The bot can recommend articles to work on based on a user's edit history, or they can supply articles or categories they want to base the suggestions on. SuggestBot is currently available in seven languages.<br />
<br />
I've participated as a mentor and instructor in some of the Community Data Science Workshops. Apart from these things, I also like reading (both books and magazines), watching movies, playing [https://en.wikipedia.org/wiki/Squash_(sport) squash], and attempting to make music.<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Nick Vincent (UC Davis) ===<br />
My research focuses on studying the relationships between human-generated data and computing technologies to mitigate negative impacts of these technologies. I am especially interested in research that (1) makes people aware of the value of their data and (2) helps people leverage the value of their data. My work relates to concepts such as "data dignity", "data as labor", "data leverage", and "data dividends".<br />
<br />
Here's my [https://www.nickmvincent.com website]!<br />
<br />
</div><br />
<br />
== Friends and Community Members ==<br />
<br />
<br />
<div style="clear:both;"><br />
=== Alice Ferrazzi ===<br />
[[File:107572.jpeg|thumb|180px|Alice Ferrazzi]]<br />
<br />
I'm a researcher and community member who collaborates and helps the CDSC in various ways. My research work focuses on the study of operating systems kernel where I work mostly in live patch systems. One of my projects is [https://wiki.gentoo.org/wiki/Elivepatch Elivepatch].<br />
<br />
I'm the Gentoo Kernel Project Leader, mainly focused in kernel release automatization. You can find me at [http://twitter.com/aliceinwire @aliceinwire] or at [http://aliceinwire.net/ www.aliceinwire.net]. My Gentoo profile is at [https://wiki.gentoo.org/wiki/User:Aliceinwire User:Aliceinwire]. I am on IRC (OFTC) as alicef_.<br />
<br />
<br />
<br />
<div style="clear:both;"><br />
=== Samuel Klein ===<br />
[[File:Orienteering tunnels.jpg|thumb|180px|right|Samuel Klein on the right (with a surprise Aaron shaw on the left).]]<br />
I'm a wikimedian, urban spelunker, and founding member of MIT's [http://kfg.mit.edu Knowledge Futures Group]. One of my projects is the Innovation Information Initiative, a data collab for patent and prior art datasets. <br />
<br />
Occasionally in IRC as _sj_. [[User:Sj|Sj]] ([[User talk:Sj|talk]]) 15:54, 17 August 2019 (EDT)<br />
<br />
<br />
<div style="clear:both;"><br />
=== Abel Serrano Juste ===<br />
<br />
[[File:Abeserra.jpeg|thumb|200px|Abel Serrano Juste]]<br />
<br />
Interested in how technology can serve communities of people for good. I see free software as an implicit requirement for this.<br />
<br />
I've been working for two years in the University Complutense of Madrid doing data analysis on collaborative online communities (CBPP), more specifically, on wikis. You can see my publications and more info about me in [https://akronix.es/ my homepage].<br />
<br />
I hold a Bachelor's Degree in Computer Science by the UCM and currently I'm enrolled in a Master's Degree of Data Science by the UOC.<br />
<br />
Also, I like bikes, nature, hiking, traveling, and sharing my life with beautiful people.<br />
<br />
<br />
<div style="clear:both;"><br />
=== Sejal Khatri ===<br />
<br />
[[File:Sejal_Khatri.jpg|thumb|200px|Sejal]]<br />
<br />
I recently graduated from the Information School at the University of Washington, Seattle. My specialization was in User Experience Research and Design in the Information Management program at iSchool. I did my undergrad in Computer Science at SPPU in Pune, India, and then interned for Wikimedia Foundation as a UX Engineer. My current research interests revolve around online communities, peer-production, and open source software. When I'm not working, I participate in design jams and hackathons where I get the opportunity to turn curiosities and concerns into design interventions. <br />
</div><br />
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<div style="clear:both;"><br />
<br />
=== Kat Walsh ===<br />
<br />
[[File:katwalsh_purple.jpg|thumb|200px|Kat Walsh, with freshly purpled hair]]<br />
<br />
I'm a lawyer working in copyright, speech, policy, and nonprofit leadership around various Free and Open projects and communities, currently working with individual clients including Creative Commons. I got into open communities through volunteering for Wikimedia, first as an editor, then in community dispute resolution, and then as a board member for several years. I've also been on the board of the Free Software Foundation. <br />
<br />
I enjoy collaborating with academic researchers on work in peer production communities and their copyright/"intellectual property", dispute resolution, governance, and legal policy issues. I am located just north of San Francisco, where I enjoy playing my bassoon, viola, and occasionally some other things in a delightfully weird collection of musical groups, and lifting heavy objects for no particular reason.<br />
<br />
</div><br />
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<div style="clear:both;"><br />
<br />
=== Hong Qu ===<br />
<br />
I'm a PhD candidate at the [https://www.networkscienceinstitute.org Network Science Institute] in Northeastern University. I try to analyze and understand how social networks facilite collective action, proprogate beliefs, and influence public opinion. For my Masters degree, I studied HCI and NLP with Professor Marti Hearst at UC Berkeley's School of Information, and developed a passion for user-centered design.<br />
<br />
Although I am working with a lot of graph data (and hairball visualizations), I really miss qualitative user research such as contextual inquiry and unstructured interviewers, and hope to conduct more mix methods studies as much as I can in the future.<br />
<br />
== Alumni ==<br />
<br />
<div style="clear:both;"><br />
=== Jim Maddock (Northwestern) ===<br />
[[File:maddock_cheese_sandwhich.jpg|thumb|200px|Jim eats a cheese sandwich while riding a cow in the Swiss Alps]]<br />
<br />
I'm a PhD Student in the Computer Science and Communications departments at Northwestern University. I currently work with Darren Gergle and Aaron Shaw, studying collaboration and coordination dynamics within social computing systems, such as Wikipedia and Zooniverse. Throughout my tenure as a graduate student I've also interned at MSR India, Google, and Mozilla.<br />
<br />
<br />
I first became interested in HCI during my undergraduate degree at the University of Washington. I earned a degree in Human Centered Design and Engineering, where I worked with Professor Kate Starbird to understand rumoring behavior in crisis situations. I also studied Medieval European history.<br />
<br />
When I'm not working on research, I'm probably riding my bike or planning a backpacking trip. You can find more about my research at my [http://jmaddock.net/ website].<br />
<br />
</div></div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=People&diff=266043People2023-02-17T16:48:02Z<p>Nickmvincent: /* Graduate Students */ moving Nick Vincent</p>
<hr />
<div>We're an interdisciplinary group at Carleton College, Northwestern University, Purdue University and the University of Washington. Faculty, postdocs, graduate students, affiliates, and alumni are listed below (in alphabetical order within each section) except when we've failed at alphabetizing.<br />
<br />
You can see pictures of all together over at our [[group photos]] page. Pictures of us individually are here.<br />
<br />
We are a friendly group and we welcome new affiliates! If you have been working with us for a while, perhaps it's time to add yourself to this page as an affiliate. Feel free to add yourself (use the Edit tab) in the appropriate subsection organized alphabetically by last name, and please include a sentence on HOW you are related to the group (and a fun picture of yourself!).<br />
<br />
Shorter summaries of [[research interests]] are also available.<br />
<br />
== Faculty ==<br />
<div style="clear:both;"><br />
<br />
=== Jeremy Foote (Purdue University) ===<br />
<br />
[[File:Jeremy.jpg|thumb|200px|Jeremy and his family on a very flat Midwest hike]]<br />
<br />
:'''Pronouns:''' he/him<br />
<br />
I grew up in Nevada, did my undergrad (in English!) at BYU in Utah, and then worked as a practitioner of online collaboration. I was the product manager for a small [https://www.lingotek.com/ collaborative translation company] in Utah. I decided that I cared a lot more about understanding collaboration than designing software, and I came back to school. I did a Master's degree at Purdue, studying with [https://www.cla.purdue.edu/communication/directory/?p=Seungyoon_Lee Seungyoon Lee], and then worked on a PhD at Northwestern, as a member of CDSC. I'm now back at Purdue in the [https://www.cla.purdue.edu/academic/communication/ Brian Lamb School of Communication], this time as a faculty member.<br />
<br />
Most of my current research is focused around understanding how people decide where to participate in online communities--why people start new communities, how community membership influences future behavior, and how communication structures relate to community outcomes. I'm particularly interested in how these decisions scale up into the social construction of understanding, knowledge, and opinion. More about my research is at my [http://www.jeremydfoote.com academic homepage].<br />
<br />
Much of my spare time is spent with my family (my wife and I have 5 kids!) or with my [http://www.mormon.org church community]. I love the Midwest but really miss hiking and skiing in the mountains and try to do both as much as possible.<br />
</div><br />
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<div style="clear:both;"><br />
<br />
=== Benjamin Mako Hill (University of Washington) ===<br />
<br />
[[File:Mako-Meitu-201701.jpg|thumb|200px|Unedited picture of Mako in Berlin (2016).]]<br />
<br />
:'''Pronouns:''' he/him<br />
<br />
After contributing peer production communities in various ways since I was a teenager, I began to realize (the hard way) that peer production rarely works and that getting it to work remained much more art than science. After being talked into the idea that academia was the right place to fix this by [https://evhippel.mit.edu/ Eric von Hippel], I've devoted the last decade of my life to trying to contribute to an emerging science of Internet-based collaborative production. Since starting as an academic, I have published tens of thousand of [https://www.wikidata.org/wiki/Q103184 articles]—nearly all of them are [https://www.wikidata.org/wiki/Lexeme:L2768 the].<br />
<br />
In the more boring accounting (which I've copied and pasted from elsewhere): I am an Associate Professor in the University of Washington Department of Communication and an Adjunct Associate Professor in the departments of Human-Centered Design and Engineering as well as in the Information School and the Paul G. Allen School of Computer Science and Engineering. At UW, I am also Affiliate Faculty in the Center for Statistics and the Social Sciences, the eScience Institute, and the "Design Use Build" (DUB) group that supports research on on human computer interaction. I am also a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University and an affiliate of the Institute of Quantitative Social Science at Harvard.<br />
<br />
Much more information is on [https://mako.cc/academic/ my academic homepage]. If you need to find me, I have put [https://mako.cc/contact/ more detailed contact information online] than I probably should.<br />
<br />
</div><br />
<div style="clear:both;"><br />
<br />
=== Sneha Narayan (Carleton College) ===<br />
<br />
[[File:Snehaphoto.jpg|thumb|200px|Sneha hanging out by Lake Michigan]]<br />
I'm an Assistant Professor of Computer Science at [https://www.carleton.edu/ Carleton College]. Before that, I did my PhD in the Technology and Social Behavior program at Northwestern University, advised by Aaron Shaw (whose bio you can find by scrolling up a couple of sections). I grew up in Bangalore, India, studied mathematics at Oberlin College, and received a masters degree in Sociology and Social Anthropology from Central European University, Budapest.<br />
<br />
I've spent many years living in housing co-ops, and volunteering on the boards of co-operative organizations. My involvement in the co-op movement led to my interest in learning more (and producing knowledge) about participatory, volunteer-run endeavors such as peer production projects and online collaboration communities. My research focuses on understanding how newcomers join and become embedded in volunteer-run organizations, and what kinds of technological interventions might affect their continued participation in these communities. For (slightly) more information about all this, you can check out my [http://www.snehanarayan.com/ homepage].<br />
</div><br />
<br />
<br />
=== Aaron Shaw (Northwestern University) ===<br />
<br />
[[File:Shaw-2017.jpg|thumb|250px|Airbrushed, filtered, and meitu'd purikura of Aaron from 2017]] <br />
<br />
Hello! I'm Aaron (he/they). I grew up around New York and went to school for a while in northern California. Along the way, I got involved in participatory movements and projects of various kinds. At first, these were more traditional movements advancing egalitarian social agendas. Over time, I got involved in peer production projects, online communities, and other sorts of open collaboration online.<br />
<br />
These days, I am an Associate Professor in the Department of Communication Studies at Northwestern where I am affiliated with the [http://mts.northwestern.edu Media, Technology & Society (MTS) Program] and the [http://tsb.northwestern.edu Technology & Social Behavior Program], courtesy appointed in the Sociology Department, a faculty associate of the Institute for Policy Research, the Buffett Institute for Global Affairs, the Center for Human-Computer Interaction + Design, and the SONIC lab. Elsewhere, I am a faculty associate of the [http://cyber.law.harvard.edu Berkman Klein Center for Internet and Society] at Harvard University. A good place to find more information is [http://aaronshaw.org my website]. If you'd like to get in touch, please [mailto:aaronshaw@northwestern.edu send me an email] (and don't be shy about re-sending if I don't reply).<br />
</div><br />
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<div style="clear:both;"><br />
<br />
== Staff ==<br />
<br />
=== Molly de Blanc (Northwestern University) ===<br />
<br />
I do communications, outreach, and research support for CDSC. This means that, among other things, I think about how our work can help the communities we study and other online communities. My CDSC research is about how community values are embodied in policies and rules.<br />
<br />
Before joining CDSC, I worked in open education and free and open source software. I recently completed a degree in Bioethics from New York University. My work was on health care technologies, autonomy, and the tools we use to protect autonomy, including informed consent and privacy. My thesis was on Right-to-Repair and implanted medical devices.<br />
<br />
I spend most of my time reading, but sometimes bake bread, I write and play music, hang out with my cat, ride a bike, and swim. I wrote [https://techautonomy.org The Declaration of Digital Autonomy] with Karen Sandler.<br />
<br />
I can be reached at molly [dot] deblanc [at] northwestern [dot] edu. Feel free to address emails to "Molly," but if you insist on using a salutation "Mx." is fine.<br />
<br />
== Postdocs == <br />
<br />
<div style="clear:both;"><br />
=== Nathan TeBlunthuis (Northwestern University) ===<br />
{{User:groceryheist/bio}}<br />
<br />
== Graduate Students ==<br />
<br />
<div style="clear:both;"><br />
<br />
=== Kevin Ackermann (Northwestern University) ===<br />
Hiiii! I'm Kevin. (∩`-´)⊃━☆゚.*・。゚<br />
<br />
I'm a first year PhD Student in the Media, Technology and Society program at Northwestern University. I'm interested in studying impacts of commercialization on digital community space, historicizing platform governance conversations, and thinking about the political (and emotional) ramifications of quantification. In the past, my attempts to study these topics have largely revolved around studying dead computer networks of the 80s and 90s. Said another way, I'm interested in how communities form and falter online, and what it's like to be a part of one. <br />
<br />
I've spent years of my pre-PhD life honing archival methodology skills, so I'm a sucker for qualitative storytelling, but I hope to try out myriad methods in my graduate studies. There are so many ways to know things!<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Kaylea Champion (University of Washington) ===<br />
<br />
[[File:Kaylea-Champion-300x300.jpg|thumb|200px|Kaylea in purple and blue.]]<br />
<br />
I (she/her) am investigating how organizations collaborate to build information public goods -- groovy things like Linux and Wikipedia. What gets made and maintained -- and what gets neglected?<br />
<br />
After growing up in Oregon, I spent two decades in Chicago, primarily at the University of Chicago as an academic technology director and consultant. I have a BA in Near Eastern Languages and Civilizations and an MS in Computer Science, both from the University of Chicago. I also hold an MA in Critical & Creative Thinking from the University of Massachusetts, Boston.<br />
<br />
My husband, three kids, and I live in Shoreline, WA, which seems to be Seattle's version of Evanston. I'm particularly fond of visiting museums, tromping in the woods, cooking for crowds, smashing goblins, and scribbling fiction.<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Regina Cheng (University of Washington) ===<br />
<br />
[[File:Regina hime.JPG|thumb|200px|Regina with her new feline best friend, [https://www.instagram.com/hime_theprincesscat/ Hime].]]<br />
<br />
I'm a PhD candidate in the Human-Centered Design and Engineering department at University of Washington, co-advised by Mako Hill and Jennifer Turns. I describe my research goal as to understand and support collaborative informal learning in online communities of creators. I am interested in studying how different types of collaborative activities (e.g. feedback exchange, collaborative sense-making) lead to different learning outcomes, and designing for more effective collaboration to facilitate learning. Right now I am especially interested in the domain of data science learning among non-technical population.<br />
<br />
Outside research, I like cats, drawing (mostly fanart these days), reading, cooking, hiking, hapkidoing, and preaching about my mother tongue, [https://en.wikipedia.org/wiki/Hangzhou_dialect Hangzhou dialect] <br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Hsuen-Chi (Hazel) Chiu (Purdue University) ===<br />
[[File:Hazel games.JPG|thumb|200px|Hazel in May 2022]]<br />
<br />
Hello! I am a First year PhD student in the Brian Lamb School of Communication at Purdue University and I am on Media, Technology and Society track. I am advised by Dr. Jeremy Foote. I study computer-mediated communication, especially using quantitative and computational approaches. I am interested in seeing how people using different affordances on social media to manage their privacy, identity and self-disclosure across platforms. I am also interested in looking at how misinformation spreads on social media.<br />
<br />
Before coming to Purdue, I earned my MS degree in Media Science focusing on Marketing Communication Research at Boston University.<br />
<br />
Outside research, I like baseball games, foods, traveling and dogs.<br />
<br />
<div style="clear:both;"><br />
<br />
=== Carl Colglazier (Northwestern University) ===<br />
<br />
{{User:Carl/bio}}<br />
<br />
</div><br />
<br />
<br />
<div style="clear:both;"><br />
=== Stefania Druga (University of Washington) ===<br />
<br />
[[File:Stef2019.jpg|thumb|200px|Stef in Summer of 2019, [//anoxic.me/huli Fancy].]]<br />
<br />
I'm a first-year Ph.D. student in the Information School at the University of Washington, co-advised by Jason Yip and Alexis Hiniker. I am the co-founder of Cognimates and HacKIDemia. My research focuses on how children interact with and make sense of the growing collection of “smart” inter-connected playthings in the world around them together with their parents. I am exploring how families, as they play with these new smart assistants and applications, develop new ways of thinking about intelligence, emotion, and social interaction. Based on these studies, I am designing new tools and activities to introduce families to machine learning and data science in a playful way. <br />
<br />
Outside research, I like climbing, dogs, reading, dancing and learning new languages. <br />
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=== Yibin Fan (University of Washington) ===<br />
Hi ;) I am a first year MA/PhD student in the Department of Communication at University of Washington. My graduate advisor is Professor Benjamin Mako Hill. My research interest is focued on digital group dynamics, and I am deeply curious about questions like how online communities connect and influence each other, or when and why group polarization forms. I am glad to include both quantitive and qualitative methods in my research, and also looking forward to learning more social scientific methods to see whether they make effects in different areas or topics.<br />
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=== Floor Fiers (Northwestern University) ===<br />
<br />
[[File:Small.FloorFiers.jpg|thumb|200px|Floor on their way back to the US in the midst of the pandemic]]<br />
<br />
:'''Pronouns:''' they/she<br />
<br />
<br />
Hi there! I am is a PhD Candidate in the [https://communication.northwestern.edu/programs/phd_media_technology_society Media, Technology and Society program ] at Northwestern Uni. Academically speaking, I am interested in the field of digital inequality, particularly as it relates to online labor markets and the gig economy. Outside academia, I love (cold water) swimming and rollerblading, and I find lots of energy in organizing two music & theater festivals in the Netherlands.<br />
<br />
Originally from the Netherlands, I first came to the US attend the [https://www.uwc.org/ United World College ] (Montezuma, NM), after which I pursued a BA in Sociology from [https://www.stlawu.edu/ St. Lawrence University ] (Canton, NY). During the pandemic, I worked remotely from the University of Zurich's [https://www.ikmz.uzh.ch/en/research/divisions/internet-use-and-society/team.html Internet & Society division]. For more background, see [https://www.floorfiers.com my website].<br />
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=== Ryan Funkhouser (Purdue University) ===<br />
<br />
Hey! My name is Ryan Funkhouser, I'm a first year PhD student in the Brian Lamb School of Communication at Purdue University, and I study conflict in communication across difference. In particular, I'm interested in using computational approaches to studying online communities and the ways in which they can foster discourse that reduces incivility and increases understanding across lines of ideological conflict.<br />
<br />
Before studying at Purdue, I earned an interdisciplinary humanities MA at Trinity Western University in British Columbia where I studied rhetoric and communication. I also began a second masters, this time specifically in communication, at the University of Wisconsin-Milwaukee. <br />
<br />
I originally hail from the beautiful city of Bellingham in the PNW, a place which nurtured within me a love for mountains and long-distance trail running. While I am living a relatively mountain-less existence in West Lafayette, Indiana while at Purdue, I continue to find joy in running and finding the beauty in the midwest. When not running, you will likely me and my wife watching a good show or going for walks.<br />
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=== Emilia Gan (University of Washington) ===<br />
<br />
[[File:EGan.jpg|200px|thumb|Emilia G.]]<br />
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I'm a PhD candidate in the [https://www.cs.washington.edu/ Paul G. Allen School of Computer Science & Engineering] at the University of Washington (Seattle). My research has involved analyzing data from the [https://scratch.mit.edu/ Scratch programming platform] (Link: [https://mako.cc/academic/gan_hill_dasgupta-gender_feedback_sharing-CSCW18.pdf paper]) and from [https://codeday.org/ CodeDay]. I am interested in factors that promote longterm participation in coding by newcomers to programming.<br />
<br />
Before starting graduate school in CS, I earned an MS ([https://globalhealth.washington.edu/education-training/phd-pathobiology Pathobiology]) from UW. I initially started learning how to program with the thought of using these skills for analyzing large biological data sets, but I eventually realized everything I was doing was pointing me away from biology and towards computer science. <br />
<br />
Before starting graduate school at UW, I homeschooled with my kids for over a decade, and before that I earned an MD from the [https://www.umassmed.edu/ University of Massachusetts Medical School] and a BS in Materials Science and Engineering from [https://dmse.mit.edu/ MIT].<br />
<br />
[https://emilia.cloud/ Personal Website]<br />
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=== Wm Salt Hale (University of Washington) ===<br />
[[File:Salt_Xmas.jpg|thumb|200px|Salt shedding the holiday cheer (2016)]]<br />
<br />
Growing up in Seattle during the early 90s offered many technological opportunities, most of which I took advantage of. As an avid GNU/Linux user for over 20 years, I have been exposed to numerous technology orientated communities on various levels.<br />
<br />
During high school I entered the Running Start program, completing an Associate's degree in Computer Science from South Seattle College. After which I transfered to the University of Washington, pursuing the same major. It was not a fit, instead I developed a number of businesses, traveled, and spoke at various conferences, conventions, events, faires, and festivals.<br />
<br />
Upon returning to the University of Washington to complete my Batchelor's degree in Communication, I connected with [[Mako]] and was shown a world of academia previously unimagined. After another year of traveling, I have decided to return to the UW Department of Comm yet again and am just beginning to delve deeper into the intersection of Technology and Society in the MA/PhD program.<br />
<br />
I am extremely interested in: Free/Libre/Open Source Software (FLOSS) and Culture; Hackers, Makers, and Breakers; and Computer-Mediated Communication using real-time synchronous systems. Along with numerous hobbies including: urban hiking (walking), dancing (folk, east coast swing, lindy, blues), windsports (windsurfing, kiteboarding, sailing), bicycling, boffering, cooking, driving, event planning, gaming, programming, public speaking, reading, robotics, skiing, and travel.<br />
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Up to date information and links to various profiles around the web can be found on ''my'' IndieWeb presence, [http://www.altsalt.net/ The Alt World of Salt].<br />
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=== Sohyeon Hwang (Northwestern University) ===<br />
<br />
[[File:Sohyeonhwang.jpg|thumb|200px|Sohyeon and her dog-child, Tubby.]]<br />
<br />
I'm Sohyeon (she/they), a third-year PhD student in the Media, Technology, and Society program at Northwestern University, advised by Aaron Shaw. My research interests broadly circle around online governance, mostly around ideas of heterogeneity, scale, and polycentric + decentralized models. I focus on the complexities arising in governance, such as how online groups diversely interpret, innovate beyond, subvert, and co-opt socio-technical affordances to manage themselves.<br />
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I am pretty methods-agnostic, doing both computational/quantitative approaches as well as qualitative work here and there. You can find more information at my [https://www.sohyeonhwang.com site].<br />
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Outside of work, I like to eat french fries (love poutine) and take (blurry but not by choice) film photos. <br />
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=== Dyuti Jha (Purdue university) ===<br />
[[File:DS(1).png|thumb|200px|Dyuti when she used to have time to go out.]]<br />
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Hi there! I am a first year PhD student at the Lamb School at Purdue. Dr Jeremy Foote is my advisor. My interests sit at the intersection of sociology, political science, and communication My work has largely been qualitative in the past but I am interested to learn computational methods and use them to study political aggression and violence in online communities. I worked in the Indian nonprofit sector for five years before deciding to come back to academia. As I find my way around what other things interest me, you will see them here!<br />
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Outside of work, I love playing my ukulele and singing, watching and analysing trashy films from all over the world, and cooking.<br />
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=== Charles Kiene (University of Washington) ===<br />
<br />
[[File:Ch2.jpg|thumb|200px|Charlie.]]<br />
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I am currently a PhD candidate in the Department of Communication at the University of Washington in Seattle, WA. I am advised by Professor Benjamin Mako Hill. As part of my doctoral research, I study organizational behavior of volunteer-based groups that manage communities in computer-mediated, online settings, such as Discord servers, subreddits, and MMORPG guilds. <br />
<br />
Topics include:<br />
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* Massive influxes of newcomers<br />
* Technological change and adaption<br />
* Organizational culture and conflicts<br />
* Emergence and evolution of rules<br />
* Turnover and division of labor<br />
<br />
I use interviewing and ethnographic research methods for inductive qualitative studies of the groups that manage online communities. I also use computational social science methods (programming and maintaining automated web crawling software in SQL databases; machine learning; statistical modeling) for collecting and analyzing data as part of both descriptive and deductive research studies of online communities.<br />
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<br />
Details at [[User:Healspersecond]]<br />
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=== Ellie Ross (University of Washington) ===<br />
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[[File:Elliew-butterfly.JPG|thumb|200px|Ellie at Turtle Bay.]]<br />
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I'm a first year MA/PhD student in the Communication Department at University of Washington, advised by Mako Hill. I am currently using the splitting of online communities to evaluate two age old hypotheses and derive what kinds of value are offered by core and periphery members of a network structure. <br />
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Outside of the University, I play video games and watch old tv shows. I have an adventure cat named Bengie and a stay at home cat named Nala. <br />
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== Undergraduate Students ==<br />
=== Serene Ong (Northwestern University) ===<br />
<br />
I am a current junior at Northwestern studying Cognitive Science with a concentration in Artificial Intelligence as well as Psychology and minoring in Computer Science. I'm interested in understanding the motivations behind human decision making and why there tends to be consistent patterns of irrationality. I plan on entering the consulting field post graduation. Outside of school I like to crotchet, watch nature documentaries, and explore Chicago!<br />
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<br />
=== Research Assistants ===<br />
* Marianne Cano, Northwestern University<br />
* Noah Hellyer, University of Washington<br />
* Divya Sikka, Interlake High School<br />
* Grace Zhu, Northwestern University<br />
* Carolyn Zou, Northwestern University<br />
* Emily Zou, Northwestern University<br />
<br />
=== Alums ===<br />
* Marlene Alanis, Northwestern University<br />
* Gabrielle Alava, Northwestern University<br />
* Paz Baum, Northwestern University<br />
* Dylan Griffin, Northwestern University<br />
* Amy Guo, Northwestern University<br />
* Matthew Holleran-Meyer, Northwestern University<br />
* Daryn McElroy, Northwestern University<br />
* Eric Rosin, Northwestern University<br />
* Donny Tou, Northwestern University<br />
* Davida Yalley, Northwestern University<br />
* Hannah Yang, Northwestern University<br />
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== Affiliate Researchers ==<br />
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=== Mad Price Ball (Open Humans Foundation) ===<br />
[[File:Mad-portrait-photo-201910.jpg|200px|thumb|Mad eagerly tearing apart [https://twitter.com/madprime/status/979833858039271425 another terrible blockchain idea].]]<br />
<br />
I am Executive Director of Open Humans Foundation and co-founder of [https://www.openhumans.org Open Humans]. My research involvement is more "meta" these days: I help others do it. With Open Humans, we try to enable a new approach for research in health and human subjects research, focusing on personal data. Our work is generally "open" and strives to enable peer production, enabling individuals to create and share tools for getting personal data, analyzing it, and potentially contributing it to aggregate projects (from patient groups to citizen scientists, as well as traditional academic studies). I'm also a Shuttleworth Foundation Fellow (alum) and a member of the BoD of MyData Global.<br />
<br />
Open Humans was inspired by my dual histories in genomics research and free/open culture. My PhD was in biotech and postdoc work involved running George Church's Personal Genome Project, which invited people to donate genome & health data to science by making it public – where I learned a lot about personal data and human subjects research. I'm also familiar with free/open culture folks for well over a decade, contributing here and there; one of my favorite past projects was helping create an offline copy of Wikipedia for OLPC distributed in Peru & Uruguay (my role was creating the article list, mostly based on traffic & connectivity data).<br />
<br />
I live in San Diego, but online you can find me on [http://twitter.com/madprime @madprime on Twitter], in the [http://slackin.openhumans.org Open Humans slack group], and sometimes IRC (madprime) – or reach me by email (mad) at openhumans.org.<br />
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=== Tilman Bayer ===<br />
[[File:Tilman at Internet Archive 2018.jpg|thumb|170px|Tilman sitting in the [https://en.wikipedia.org/wiki/Internet_Archive Internet Archive's] pews, piously contemplating the world's knowledge]]<br />
I am a longtime Wikipedia contributor (as [[:w:User:HaeB|User:HaeB]]) and editor of the [https://meta.wikimedia.org/wiki/Research:Newsletter Wikimedia Research Newsletter], a monthly publication surveying and reviewing recent academic research about Wikipedia and other Wikimedia projects, which I co-founded in 2011 with my then-colleague Dario Taraborelli at the Wikimedia Foundation. I am also one of the two maintainers of the associated [https://twitter.com/wikiresearch @WikiResearch] Twitter feed. For the past several years, I have joined Mako, Aaron and others in presenting an annual [https://wikimania2018.wikimedia.org/wiki/Program/State_of_Wikimedia_Research_2017-2018 "State of Wikimedia Research"] overview at the Wikimania community conference, where I have also presented on other data and research topics such as the question [https://upload.wikimedia.org/wikipedia/commons/e/e1/Which_parts_of_a_%28Wikipedia%29_article_are_actually_being_read_%28Wikimania_2018%29.pdf which parts of a Wikipedia article people actually read]. <br />
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My work as a data analyst on the Wikimedia Foundation's [https://www.mediawiki.org/w/index.php?title=Product_Analytics&oldid=3173327 Product Analytics team] included controlled experiments and exploratory data analysis to support the development of new software features for Wikipedia readers and contributors, and the analysis of core readership metrics like pageviews. With the Foundation's web team, I drove the implementation of a new metric designed to better understand reader engagement, based on an instrumentation of time spent on page (dwell time). This became the subject of a [https://meta.wikimedia.org/wiki/Research:Reading_time research project] with Nate TeBlunthuis and my then-colleague Olga Vasileva, with findings e.g. about differences in reading behavior between users in the Global South and the Global North.<br />
<br />
My academic background is in pure mathematics, with degrees from the University of Cambridge and the University of Bonn. I am based in San Francisco and can be reached via Gmail ("HaeBwiki") and as "HaeB" on IRC (Freenode).<br />
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=== Sayamindu Dasgupta (University of Washington) ===<br />
<br />
[[File:Sayamindu.jpg|thumb|200px|Sayamindu, mildly perturbed.]]<br />
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After getting a PhD from MIT, I was a postdoctoral fellow at the University of Washington's eScience Institute and was hosted by CDSC over 2017-2018. I then spent three and a half years as an assistant professor at the School of Information and Library Science, UNC Chapel Hill, and I am currently an assistant professor the University of Washington's department of Human Centered Design and Engineering where I study, design, and build pathways that engage young people in learning with data and digital technologies. Our lab is called the [https://depts.washington.edu/ledlab/ Learning, Epistemology, and Design Lab (LED Lab)].<br />
<br />
You can find more about my work on my [https://unmad.in homepage].<br />
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=== Bastian Greshake Tzovaras (Center for Research & Interdisciplinarity, Université Paris Descartes) ===<br />
[[File:BastianGreshakeTzovaras.jpg|200px|thumb|Bastian, being so old-timey that his beard has grown.]]<br />
<br />
Despite having an academic background in biology/bioinformatics, I've been active in peer-produced citizen science since around 2011. I'm one of the co-founders of the crowdsourced, open data repository openSNP ([https://opensnp.org]), which collects personal genomics data sets from users of Direct-To-Consumer genetic testing companies to put them into the public domain. Since 2017 I'm also the Director of Research for Open Humans (https://www.openhumans.org), an ecosystem for participatory citizen science that aims to allow people to analyze and learn from their own personal data as well as given members the opportunity to share their data with (citizen science) research projects. Among other things we have piloted a JupyterHub-based approach to give people their own virtual machines that allow them to write, run and share data analysis notebooks without having to share any personal information (see [https://exploratory.openhumans.org]).<br />
<br />
Since 2019 I'm a research fellow at the Center for Research & Interdisciplinarity in Paris ([https://cri-paris.org/]), where I will study how the ideas of peer-production can be translated to facilitate co-created citizen science projects in which participants are fully involved in all stages of research, from start to finish. Lately a lot of focus there has been on how we can scale up the individualistic quantified self experiments people do to larger cohorts. I also teach students the basics of citizen science and self-tracking. <br />
<br />
Last but not least I'm involved in community building and mentoring in bioinformatics and for open projects in general: I'm a board member of the Open Bioinformatics Foundation ([https://www.open-bio.org/]), have mentored for Mozilla's Open Leadership Cohorts, Outreachy & Google Summer of Code.<br />
</div><br />
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=== Andrés Monroy-Hernández (Snap Research) ===<br />
[[File:andresmh.jpg|thumb|180px|🚀]]<br />
<br />
I'm a researcher at [https://www.snap.com/ Snap Inc.] and an affiliate faculty at the University of Washington. My work focuses on the study and design of social computing systems. Some areas I've worked on are crowdsourcing, peer production, remixing, civic tech, urban computing, and online learning.<br />
<br />
Some projects I've worked on lately include [http://calendar.help Calendar.help], a hybrid intelligence scheduling assistant partly powered by crowds; Narcotweets, a research project studying how people use social media during war and political uprisings; and the [http://scratch.mit.edu Scratch Online Community], a website where millions of young people learn to program and remix games and animations. <br />
<br />
You can find me at [http://twitter.com/andresmh @andresmh] or at [http://andresmh.com/ www.andresmh.com].<br />
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=== Jonathan T. Morgan (Crowdstrike) ===<br />
<br />
[[File:Jtm_profile_pic.jpg|thumb|200px|Jonathan in his preferred horizontal orientation.]]<br />
<br />
I'm a UX researcher at CrowdStrike and an affiliate faculty member in the UW department of Human Centered Design & Engineering. Most of my research involves understanding the sociotechnical mechanisms through which people who use complex collaborative software systems coordinate their work across time and space. You can find out more about me and my work [https://meta.wikimedia.org/wiki/User:Jmorgan_(WMF) here] and [http://jtmorgan.net/ here].<br />
<br />
I am a founding mentor for the [[Community_Data_Science_Workshops|Community Data Science Workshops]], and I also develop and teach UW courses on related topics, like [[Human_Centered_Data_Science|Human Centered Data Science]]. <br />
<br />
I am a voracious and omnivorous reader, and a passionately amateurish musician. When I'm away from the keyboard, you can usually find me exploring the beaches and forests of Puget Sound with my wife and my dog, [[w:Ozymandias|Ozymandias]].<br />
</div><br />
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<br />
=== Morten Warncke-Wang (Wikimedia Foundation) ===<br />
[[File:Warncke-Wang, Morten - Dec 2017.jpg|200px|thumb|Morten prior to growing a scientifically sound beard.]]<br />
<br />
I've been participating in online and peer production communities for over 20 years, and recently (December 2016) got a PhD studying them. My research focus has been on content quality in peer production communities like Wikipedia and OpenStreetMap: what is high quality content, how is it created, can we build tools to judge it, and is it produced where there is demand for it? In addition to research publications, this work has also led to a Python library for predicting Wikipedia article quality ([https://github.com/wiki-ai/articlequality articlequality]) that is publicly available on Wikipedia through the [https://www.mediawiki.org/wiki/ORES ORES API]. I am also a Research Fellow with the [https://research.wikimedia.org Wikimedia Foundation's Research group].<br />
<br />
Another one of my interests is using recommender systems to help contributors find work to do. In Wikipedia this manifests in my maintenance of [https://en.wikipedia.org/wiki/User:SuggestBot SuggestBot]. The bot can recommend articles to work on based on a user's edit history, or they can supply articles or categories they want to base the suggestions on. SuggestBot is currently available in seven languages.<br />
<br />
I've participated as a mentor and instructor in some of the Community Data Science Workshops. Apart from these things, I also like reading (both books and magazines), watching movies, playing [https://en.wikipedia.org/wiki/Squash_(sport) squash], and attempting to make music.<br />
</div><br />
<br />
<br />
== Friends and Community Members ==<br />
<br />
<br />
<div style="clear:both;"><br />
=== Alice Ferrazzi ===<br />
[[File:107572.jpeg|thumb|180px|Alice Ferrazzi]]<br />
<br />
I'm a researcher and community member who collaborates and helps the CDSC in various ways. My research work focuses on the study of operating systems kernel where I work mostly in live patch systems. One of my projects is [https://wiki.gentoo.org/wiki/Elivepatch Elivepatch].<br />
<br />
I'm the Gentoo Kernel Project Leader, mainly focused in kernel release automatization. You can find me at [http://twitter.com/aliceinwire @aliceinwire] or at [http://aliceinwire.net/ www.aliceinwire.net]. My Gentoo profile is at [https://wiki.gentoo.org/wiki/User:Aliceinwire User:Aliceinwire]. I am on IRC (OFTC) as alicef_.<br />
<br />
<br />
<br />
<div style="clear:both;"><br />
=== Samuel Klein ===<br />
[[File:Orienteering tunnels.jpg|thumb|180px|right|Samuel Klein on the right (with a surprise Aaron shaw on the left).]]<br />
I'm a wikimedian, urban spelunker, and founding member of MIT's [http://kfg.mit.edu Knowledge Futures Group]. One of my projects is the Innovation Information Initiative, a data collab for patent and prior art datasets. <br />
<br />
Occasionally in IRC as _sj_. [[User:Sj|Sj]] ([[User talk:Sj|talk]]) 15:54, 17 August 2019 (EDT)<br />
<br />
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<div style="clear:both;"><br />
=== Abel Serrano Juste ===<br />
<br />
[[File:Abeserra.jpeg|thumb|200px|Abel Serrano Juste]]<br />
<br />
Interested in how technology can serve communities of people for good. I see free software as an implicit requirement for this.<br />
<br />
I've been working for two years in the University Complutense of Madrid doing data analysis on collaborative online communities (CBPP), more specifically, on wikis. You can see my publications and more info about me in [https://akronix.es/ my homepage].<br />
<br />
I hold a Bachelor's Degree in Computer Science by the UCM and currently I'm enrolled in a Master's Degree of Data Science by the UOC.<br />
<br />
Also, I like bikes, nature, hiking, traveling, and sharing my life with beautiful people.<br />
<br />
<br />
<div style="clear:both;"><br />
=== Sejal Khatri ===<br />
<br />
[[File:Sejal_Khatri.jpg|thumb|200px|Sejal]]<br />
<br />
I recently graduated from the Information School at the University of Washington, Seattle. My specialization was in User Experience Research and Design in the Information Management program at iSchool. I did my undergrad in Computer Science at SPPU in Pune, India, and then interned for Wikimedia Foundation as a UX Engineer. My current research interests revolve around online communities, peer-production, and open source software. When I'm not working, I participate in design jams and hackathons where I get the opportunity to turn curiosities and concerns into design interventions. <br />
</div><br />
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<div style="clear:both;"><br />
<br />
=== Kat Walsh ===<br />
<br />
[[File:katwalsh_purple.jpg|thumb|200px|Kat Walsh, with freshly purpled hair]]<br />
<br />
I'm a lawyer working in copyright, speech, policy, and nonprofit leadership around various Free and Open projects and communities, currently working with individual clients including Creative Commons. I got into open communities through volunteering for Wikimedia, first as an editor, then in community dispute resolution, and then as a board member for several years. I've also been on the board of the Free Software Foundation. <br />
<br />
I enjoy collaborating with academic researchers on work in peer production communities and their copyright/"intellectual property", dispute resolution, governance, and legal policy issues. I am located just north of San Francisco, where I enjoy playing my bassoon, viola, and occasionally some other things in a delightfully weird collection of musical groups, and lifting heavy objects for no particular reason.<br />
<br />
</div><br />
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<br />
=== Hong Qu ===<br />
<br />
I'm a PhD candidate at the [https://www.networkscienceinstitute.org Network Science Institute] in Northeastern University. I try to analyze and understand how social networks facilite collective action, proprogate beliefs, and influence public opinion. For my Masters degree, I studied HCI and NLP with Professor Marti Hearst at UC Berkeley's School of Information, and developed a passion for user-centered design.<br />
<br />
Although I am working with a lot of graph data (and hairball visualizations), I really miss qualitative user research such as contextual inquiry and unstructured interviewers, and hope to conduct more mix methods studies as much as I can in the future.<br />
<br />
== Alumni ==<br />
<br />
<div style="clear:both;"><br />
=== Jim Maddock (Northwestern) ===<br />
[[File:maddock_cheese_sandwhich.jpg|thumb|200px|Jim eats a cheese sandwich while riding a cow in the Swiss Alps]]<br />
<br />
I'm a PhD Student in the Computer Science and Communications departments at Northwestern University. I currently work with Darren Gergle and Aaron Shaw, studying collaboration and coordination dynamics within social computing systems, such as Wikipedia and Zooniverse. Throughout my tenure as a graduate student I've also interned at MSR India, Google, and Mozilla.<br />
<br />
<br />
I first became interested in HCI during my undergraduate degree at the University of Washington. I earned a degree in Human Centered Design and Engineering, where I worked with Professor Kate Starbird to understand rumoring behavior in crisis situations. I also studied Medieval European history.<br />
<br />
When I'm not working on research, I'm probably riding my bike or planning a backpacking trip. You can find more about my research at my [http://jmaddock.net/ website].<br />
<br />
</div></div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Workshop_and_Soft_Block&diff=265929CommunityData:Workshop and Soft Block2023-02-13T18:30:04Z<p>Nickmvincent: /* Workshops and Softblocks */</p>
<hr />
<div>== Workshops and Softblocks ==<br />
<br />
{{notice|Winter 2023 quarter workshops are on Mondays at 2:00pm ET / 1:00pm CT / 11:00am PT. Please circulate/share your work by the end of the day (in your local time) of the Friday preceding your slot.}}<br />
<br />
The '''CDSC workshop''' provides a weekly venue to present/share work about once per quarter. The '''softblock''' provides a time for coworking sessions, meetups with other labs and researchers, and ad-hoc meetings.<br />
<br />
Like in the critique and feedback session, you are welcome to workshop a wide range of things: planning documents, draft papers, manuscripts with reviews, grant applications, job market materials, practice talks, brainstorming potential projects, etc.<br />
<br />
'''Everyone is expected to attend the workshop sessions. Everyone is also expected participate by attending and presenting/sharing work each quarter. Sign up below!'''<br />
<br />
* January 9<br />
* January 23 - Carl: Two qualification exam research proposals! <br />
* January 30 - floor: diss prospectus<br />
* (alt time slot!) February 2 - Kevin Driscoll's ''Modem World'' + Aaron book review draft<br />
* February 6 - Visit with [https://felixreda.eu/en/ Felix Reda]<br />
* February 13 - Yibin: Incidental discussion and polarized political expression on Reddit<br />
* February 20- Xinya(Cindy) Gong, proposal review and get some feedbacks.<br />
* February 27 - empty (Nick V had to cancel, very sorry!)<br />
* March 6 - Ellie Ross: Core and Peripheral Structure in Online Communities<br />
* March 13 - floor: syllabus for class next quarter<br />
* March 20 - sohyeon: idk, something though :) <br />
* March 27 - Omnibus (tentative)<br />
<br />
== Past workshops ==<br />
<br />
=== Fall 2022 ===<br />
<br />
* September 16 - Retreat planning<br />
* September 23 - <br />
* September 30 - Carl: De-federation paper updates.<br />
* October 6 - Regina: job market material draft (second 30 mins)<br />
* October 13 - <br />
* October 20 - Prospective Student Panel <br />
* October 27 - Nate's measurement error paper<br />
* November 3 - Molly dB<br />
* November 10 - CDSC Membership (coming out of retreat unconference; floor, regina, salt, et al.) + Emilia practice talk<br />
* November 17 - Botstacking CHI paper + reviewer comments (Sohyeon, Charlie, Aaron) + CDSC Membership <br />
* November 24 - Pie baking (savory or sweet, all good)<br />
* December 1 - Coworking time to write blogs!! (floor added this)<br />
* December 8 - kaylea / divya<br />
* December 15 - The CDSC End of Year Mandatory Fun Online Party<br />
<br />
=== Summer 20222 ===<br />
<br />
==== Workshop ====<br />
<br />
* June 21 -<br />
* June 28 - <br />
* July 5 - Kaylea: probably CSCW paper something something<br />
* July 12<br />
* July 19 - Carl: whatever needs the most feedback at this point. :)<br />
* July 26 - Aaron: cross-sector collaborations in King County<br />
* August 2 - Molly: tbd<br />
* August 9 - <br />
* August 16 - <br />
* August 23 - Floor: diss plans<br />
* August 30 - Kaylea: book review of The Conversational Firm<br />
* Sept 6 - <br />
* Sept 13 - <br />
* Sept 19 -<br />
<br />
==== Soft Block ====<br />
<br />
* June 17 - CAT Lab meetup<br />
* June 20 -<br />
* June 27 - Review of 2021-2022 academic year (what went well, what could have been better)<br />
* July 4 - No meeting<br />
* July 11 - Blog sprint<br />
* July 18 - <br />
* July 25 - <br />
* August 1 - <br />
* August 8 - Isabelle Catherine Langrock<br />
* August 15 - <br />
* August 22 - <br />
* August 29 - <br />
* Sept 5 - <br />
* Sept 12 - <br />
* Sept 18 -<br />
<br />
=== Spring 2022 ===<br />
<br />
==== Workshop ====<br />
* <strike>Mar 29</strike><br />
* <strike>April 5 - Nate and Richard: Preliminary draft of specialization and mutualism project. </strike><br />
* <strike>April 12</strike><br />
* April 19 - Regina and Sejal: CHI Practice Talks (Nothing circulated in advance)<br />
* April 26 - Sohyeon: discord bot study, initial memos<br />
* May 3 (CHI week)<br />
* May 10 <br />
* May 17 - ICA practice talks (Presenters: Kaylea - will be late, proposal defense is the same day, ???)<br />
* May 24 - ICA practice talks (Presenters: ????)<br />
* May 31 (ICA week)<br />
* June 7 - <strike>Regina: Draft of Data Science Community paper</strike><br />
* June 14 - Katharina: Feedback for CSCW Doctoral Consortium submission draft<br />
<br />
==== Soft block ====<br />
* <strike>April 1</strike><br />
* <strike>April 8</strike><br />
* <strike>April 15</strike><br />
* April 22 - GroupLens meetup<br />
* April 29 - Dialogue invitation list sprint<br />
* May 6 - Carl: wikia data descriptor paper draft<br />
* May 13 - <strike>CAT Lab mixer</strike> Floor (library project idea)<br />
* May 20 - Community dialogue happening today!<br />
* May 27 - <br />
* June 3 - GroupLens meetup (take 2) [https://etherpad.communitydata.science/p/grouplens-mixer participant bios here]<br />
* June 10 - <br />
<br />
<br />
=== Winter 2022 ===<br />
<br />
We had a lot of interest in workshops this quarter, so we used the soft block time for them as well.<br />
<br />
==== Workshop ====<br />
<br />
* Jan 10 - Nate: Notes on a methodological paper on the effects of measurement error in machine learning models on research. <br />
* <strike>Jan 17 - No Meeting (MLK Jr Day)</strike><br />
* <strike>Jan 24</strike><br />
* Jan 31 - Jeremy: Draft of revisions for "A systems approach to studying online organizations"<br />
* Feb 7 - St3f: Defense proposal draft. <br />
* <strike>Feb 14 - Sohyeon: WIKIPI extended abstract submission planning document</strike><br />
* Feb 21 - Carl: Moderation and Content Policies on the Fediverse (journal article draft).<br />
* Feb 28 - Molly: modifiability (including concepts like right to repair) and consent in medical devices (thesis)<br />
* Mar 7 - Zarine: Ideological capture/disinformation in small language Wikis project<br />
* Mar 14 - Charlie: TBD (Dissertation Proposal 1 pagers)<br />
<br />
==== Soft block ====<br />
<br />
* Jan 28 - Regina: Feedback for CSCW revision<br />
* Feb 4 - Floor: Fellowship app<br />
* Feb 11 - CDSC Dialogues<br />
* <strike>Feb 18</strike> <br />
* Feb 25 - Yichen Shen (NU sociology student visiting)!<br />
* Mar 4 - NU SoC grad student showcase<br />
* Mar 11 - Regina (Workshop!): Dissertation Proposal draft<br />
<br />
<br />
=== Fall 2021 ===<br />
* Oct 1 - Floor: Online labor SLR<br />
* Oct 8 - Nate: No Community Can Do Everything (revisions for CSCW)<br />
* Oct 15 - Sohyeon: Information loss project (to be WWW2022 submission)<br />
* Oct 22 - St3f: Coraland Citizen Science App for Families (R&R for CSCW) <br />
* Oct 29 - Nick V: The Theory of Critical Mass and Data Leverage<br />
* Nov 5 - Charlie: CSCW "Why These Rules?" submission / feedback on reviews and paper<br />
* Nov 12 - Sneha: Proposal for an NSF (or other) proposal on community governance<br />
* Nov 19 - Carl<br />
* <strike>Nov 26</strike> - Thanksgiving week / no meeting<br />
* Dec 3 - Regina: Three dissertation ideas<br />
* Dec 10 - Kaylea: Reviews & Revision Planning for the Qualities of Quality systematic lit review paper</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Workshop_and_Soft_Block&diff=255811CommunityData:Workshop and Soft Block2023-01-17T23:14:36Z<p>Nickmvincent: Nick moved from Mar 6 to Feb 27</p>
<hr />
<div>== Workshops and Softblocks ==<br />
<br />
{{notice|Winter 2023 quarter workshops are on Mondays at 2:00pm ET / 1:00pm CT / 11:00am PT. Please circulate/share your work by the end of the day (in your local time) of the Friday preceding your slot.}}<br />
<br />
The '''CDSC workshop''' provides a weekly venue to present/share work about once per quarter. The '''softblock''' provides a time for coworking sessions, meetups with other labs and researchers, and ad-hoc meetings.<br />
<br />
Like in the critique and feedback session, you are welcome to workshop a wide range of things: planning documents, draft papers, manuscripts with reviews, grant applications, job market materials, practice talks, brainstorming potential projects, etc.<br />
<br />
'''Everyone is expected to attend the workshop sessions. Everyone is also expected participate by attending and presenting/sharing work each quarter. Sign up below!'''<br />
<br />
* January 9<br />
* January 23 - Carl: Two qualification exam research proposals! <br />
* January 30 - floor: diss prospectus<br />
* February 6 - Visit with [https://felixreda.eu/en/ Felix Reda]<br />
* February 13 - sohyeon: idk, something though :)<br />
* February 20- Xinya(Cindy) Gong, proposal review and get some feedbacks.<br />
* February 27 - Nick Vincent + Bogdana Rakova: Something like: Contesting Algorithms and Community-driven Auditing <br />
* March 6 - <br />
* March 13 - floor: syllabus for class next quarter<br />
* March 20<br />
* March 27 - Omnibus (tentative)<br />
<br />
== Past workshops ==<br />
<br />
=== Fall 2022 ===<br />
<br />
* September 16 - Retreat planning<br />
* September 23 - <br />
* September 30 - Carl: De-federation paper updates.<br />
* October 6 - Regina: job market material draft (second 30 mins)<br />
* October 13 - <br />
* October 20 - Prospective Student Panel <br />
* October 27 - Nate's measurement error paper<br />
* November 3 - Molly dB<br />
* November 10 - CDSC Membership (coming out of retreat unconference; floor, regina, salt, et al.) + Emilia practice talk<br />
* November 17 - Botstacking CHI paper + reviewer comments (Sohyeon, Charlie, Aaron) + CDSC Membership <br />
* November 24 - Pie baking (savory or sweet, all good)<br />
* December 1 - Coworking time to write blogs!! (floor added this)<br />
* December 8 - kaylea / divya<br />
* December 15 - The CDSC End of Year Mandatory Fun Online Party<br />
<br />
=== Summer 20222 ===<br />
<br />
==== Workshop ====<br />
<br />
* June 21 -<br />
* June 28 - <br />
* July 5 - Kaylea: probably CSCW paper something something<br />
* July 12<br />
* July 19 - Carl: whatever needs the most feedback at this point. :)<br />
* July 26 - Aaron: cross-sector collaborations in King County<br />
* August 2 - Molly: tbd<br />
* August 9 - <br />
* August 16 - <br />
* August 23 - Floor: diss plans<br />
* August 30 - Kaylea: book review of The Conversational Firm<br />
* Sept 6 - <br />
* Sept 13 - <br />
* Sept 19 -<br />
<br />
==== Soft Block ====<br />
<br />
* June 17 - CAT Lab meetup<br />
* June 20 -<br />
* June 27 - Review of 2021-2022 academic year (what went well, what could have been better)<br />
* July 4 - No meeting<br />
* July 11 - Blog sprint<br />
* July 18 - <br />
* July 25 - <br />
* August 1 - <br />
* August 8 - Isabelle Catherine Langrock<br />
* August 15 - <br />
* August 22 - <br />
* August 29 - <br />
* Sept 5 - <br />
* Sept 12 - <br />
* Sept 18 -<br />
<br />
=== Spring 2022 ===<br />
<br />
==== Workshop ====<br />
* <strike>Mar 29</strike><br />
* <strike>April 5 - Nate and Richard: Preliminary draft of specialization and mutualism project. </strike><br />
* <strike>April 12</strike><br />
* April 19 - Regina and Sejal: CHI Practice Talks (Nothing circulated in advance)<br />
* April 26 - Sohyeon: discord bot study, initial memos<br />
* May 3 (CHI week)<br />
* May 10 <br />
* May 17 - ICA practice talks (Presenters: Kaylea - will be late, proposal defense is the same day, ???)<br />
* May 24 - ICA practice talks (Presenters: ????)<br />
* May 31 (ICA week)<br />
* June 7 - <strike>Regina: Draft of Data Science Community paper</strike><br />
* June 14 - Katharina: Feedback for CSCW Doctoral Consortium submission draft<br />
<br />
==== Soft block ====<br />
* <strike>April 1</strike><br />
* <strike>April 8</strike><br />
* <strike>April 15</strike><br />
* April 22 - GroupLens meetup<br />
* April 29 - Dialogue invitation list sprint<br />
* May 6 - Carl: wikia data descriptor paper draft<br />
* May 13 - <strike>CAT Lab mixer</strike> Floor (library project idea)<br />
* May 20 - Community dialogue happening today!<br />
* May 27 - <br />
* June 3 - GroupLens meetup (take 2) [https://etherpad.communitydata.science/p/grouplens-mixer participant bios here]<br />
* June 10 - <br />
<br />
<br />
=== Winter 2022 ===<br />
<br />
We had a lot of interest in workshops this quarter, so we used the soft block time for them as well.<br />
<br />
==== Workshop ====<br />
<br />
* Jan 10 - Nate: Notes on a methodological paper on the effects of measurement error in machine learning models on research. <br />
* <strike>Jan 17 - No Meeting (MLK Jr Day)</strike><br />
* <strike>Jan 24</strike><br />
* Jan 31 - Jeremy: Draft of revisions for "A systems approach to studying online organizations"<br />
* Feb 7 - St3f: Defense proposal draft. <br />
* <strike>Feb 14 - Sohyeon: WIKIPI extended abstract submission planning document</strike><br />
* Feb 21 - Carl: Moderation and Content Policies on the Fediverse (journal article draft).<br />
* Feb 28 - Molly: modifiability (including concepts like right to repair) and consent in medical devices (thesis)<br />
* Mar 7 - Zarine: Ideological capture/disinformation in small language Wikis project<br />
* Mar 14 - Charlie: TBD (Dissertation Proposal 1 pagers)<br />
<br />
==== Soft block ====<br />
<br />
* Jan 28 - Regina: Feedback for CSCW revision<br />
* Feb 4 - Floor: Fellowship app<br />
* Feb 11 - CDSC Dialogues<br />
* <strike>Feb 18</strike> <br />
* Feb 25 - Yichen Shen (NU sociology student visiting)!<br />
* Mar 4 - NU SoC grad student showcase<br />
* Mar 11 - Regina (Workshop!): Dissertation Proposal draft<br />
<br />
<br />
=== Fall 2021 ===<br />
* Oct 1 - Floor: Online labor SLR<br />
* Oct 8 - Nate: No Community Can Do Everything (revisions for CSCW)<br />
* Oct 15 - Sohyeon: Information loss project (to be WWW2022 submission)<br />
* Oct 22 - St3f: Coraland Citizen Science App for Families (R&R for CSCW) <br />
* Oct 29 - Nick V: The Theory of Critical Mass and Data Leverage<br />
* Nov 5 - Charlie: CSCW "Why These Rules?" submission / feedback on reviews and paper<br />
* Nov 12 - Sneha: Proposal for an NSF (or other) proposal on community governance<br />
* Nov 19 - Carl<br />
* <strike>Nov 26</strike> - Thanksgiving week / no meeting<br />
* Dec 3 - Regina: Three dissertation ideas<br />
* Dec 10 - Kaylea: Reviews & Revision Planning for the Qualities of Quality systematic lit review paper</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Workshop_and_Soft_Block&diff=255667CommunityData:Workshop and Soft Block2023-01-09T21:24:15Z<p>Nickmvincent: /* Workshops and Softblocks */ Nick V grabs March 6 slot</p>
<hr />
<div>== Workshops and Softblocks ==<br />
<br />
{{notice|Winter 2023 quarter workshops are on Mondays at 2:00pm ET / 1:00pm CT / 11:00am PT. Please circulate/share your work by the end of the day (in your local time) of the Friday preceding your slot.}}<br />
<br />
The '''CDSC workshop''' provides a weekly venue to present/share work about once per quarter. The '''softblock''' provides a time for coworking sessions, meetups with other labs and researchers, and ad-hoc meetings.<br />
<br />
Like in the critique and feedback session, you are welcome to workshop a wide range of things: planning documents, draft papers, manuscripts with reviews, grant applications, job market materials, practice talks, brainstorming potential projects, etc.<br />
<br />
'''Everyone is expected to attend the workshop sessions. Everyone is also expected participate by attending and presenting/sharing work each quarter. Sign up below!'''<br />
<br />
* January 9<br />
* January 23 - Carl: Two qualification exam research proposals! <br />
* January 30 - Aaron: draft book review of Driscoll's ''Modem World'' <br />
* February 6 - Visit with [https://felixreda.eu/en/ Felix Reda]<br />
* February 13 - sohyeon: idk, something though :)<br />
* February 20- Xinya(Cindy) Gong, proposal review and get some feedbacks.<br />
* February 27<br />
* March 6 - Nick Vincent + Bogdana Rakova: Something like: Algorithmic Contestability and Community-driven Auditing <br />
* March 13<br />
* March 20<br />
* March 27 - Omnibus (tentative)<br />
<br />
== Past workshops ==<br />
<br />
=== Fall 2022 ===<br />
<br />
* September 16 - Retreat planning<br />
* September 23 - <br />
* September 30 - Carl: De-federation paper updates.<br />
* October 6 - Regina: job market material draft (second 30 mins)<br />
* October 13 - <br />
* October 20 - Prospective Student Panel <br />
* October 27 - Nate's measurement error paper<br />
* November 3 - Molly dB<br />
* November 10 - CDSC Membership (coming out of retreat unconference; floor, regina, salt, et al.) + Emilia practice talk<br />
* November 17 - Botstacking CHI paper + reviewer comments (Sohyeon, Charlie, Aaron) + CDSC Membership <br />
* November 24 - Pie baking (savory or sweet, all good)<br />
* December 1 - Coworking time to write blogs!! (floor added this)<br />
* December 8 - kaylea / divya<br />
* December 15 - The CDSC End of Year Mandatory Fun Online Party<br />
<br />
=== Summer 20222 ===<br />
<br />
==== Workshop ====<br />
<br />
* June 21 -<br />
* June 28 - <br />
* July 5 - Kaylea: probably CSCW paper something something<br />
* July 12<br />
* July 19 - Carl: whatever needs the most feedback at this point. :)<br />
* July 26 - Aaron: cross-sector collaborations in King County<br />
* August 2 - Molly: tbd<br />
* August 9 - <br />
* August 16 - <br />
* August 23 - Floor: diss plans<br />
* August 30 - Kaylea: book review of The Conversational Firm<br />
* Sept 6 - <br />
* Sept 13 - <br />
* Sept 19 -<br />
<br />
==== Soft Block ====<br />
<br />
* June 17 - CAT Lab meetup<br />
* June 20 -<br />
* June 27 - Review of 2021-2022 academic year (what went well, what could have been better)<br />
* July 4 - No meeting<br />
* July 11 - Blog sprint<br />
* July 18 - <br />
* July 25 - <br />
* August 1 - <br />
* August 8 - Isabelle Catherine Langrock<br />
* August 15 - <br />
* August 22 - <br />
* August 29 - <br />
* Sept 5 - <br />
* Sept 12 - <br />
* Sept 18 -<br />
<br />
=== Spring 2022 ===<br />
<br />
==== Workshop ====<br />
* <strike>Mar 29</strike><br />
* <strike>April 5 - Nate and Richard: Preliminary draft of specialization and mutualism project. </strike><br />
* <strike>April 12</strike><br />
* April 19 - Regina and Sejal: CHI Practice Talks (Nothing circulated in advance)<br />
* April 26 - Sohyeon: discord bot study, initial memos<br />
* May 3 (CHI week)<br />
* May 10 <br />
* May 17 - ICA practice talks (Presenters: Kaylea - will be late, proposal defense is the same day, ???)<br />
* May 24 - ICA practice talks (Presenters: ????)<br />
* May 31 (ICA week)<br />
* June 7 - <strike>Regina: Draft of Data Science Community paper</strike><br />
* June 14 - Katharina: Feedback for CSCW Doctoral Consortium submission draft<br />
<br />
==== Soft block ====<br />
* <strike>April 1</strike><br />
* <strike>April 8</strike><br />
* <strike>April 15</strike><br />
* April 22 - GroupLens meetup<br />
* April 29 - Dialogue invitation list sprint<br />
* May 6 - Carl: wikia data descriptor paper draft<br />
* May 13 - <strike>CAT Lab mixer</strike> Floor (library project idea)<br />
* May 20 - Community dialogue happening today!<br />
* May 27 - <br />
* June 3 - GroupLens meetup (take 2) [https://etherpad.communitydata.science/p/grouplens-mixer participant bios here]<br />
* June 10 - <br />
<br />
<br />
=== Winter 2022 ===<br />
<br />
We had a lot of interest in workshops this quarter, so we used the soft block time for them as well.<br />
<br />
==== Workshop ====<br />
<br />
* Jan 10 - Nate: Notes on a methodological paper on the effects of measurement error in machine learning models on research. <br />
* <strike>Jan 17 - No Meeting (MLK Jr Day)</strike><br />
* <strike>Jan 24</strike><br />
* Jan 31 - Jeremy: Draft of revisions for "A systems approach to studying online organizations"<br />
* Feb 7 - St3f: Defense proposal draft. <br />
* <strike>Feb 14 - Sohyeon: WIKIPI extended abstract submission planning document</strike><br />
* Feb 21 - Carl: Moderation and Content Policies on the Fediverse (journal article draft).<br />
* Feb 28 - Molly: modifiability (including concepts like right to repair) and consent in medical devices (thesis)<br />
* Mar 7 - Zarine: Ideological capture/disinformation in small language Wikis project<br />
* Mar 14 - Charlie: TBD (Dissertation Proposal 1 pagers)<br />
<br />
==== Soft block ====<br />
<br />
* Jan 28 - Regina: Feedback for CSCW revision<br />
* Feb 4 - Floor: Fellowship app<br />
* Feb 11 - CDSC Dialogues<br />
* <strike>Feb 18</strike> <br />
* Feb 25 - Yichen Shen (NU sociology student visiting)!<br />
* Mar 4 - NU SoC grad student showcase<br />
* Mar 11 - Regina (Workshop!): Dissertation Proposal draft<br />
<br />
<br />
=== Fall 2021 ===<br />
* Oct 1 - Floor: Online labor SLR<br />
* Oct 8 - Nate: No Community Can Do Everything (revisions for CSCW)<br />
* Oct 15 - Sohyeon: Information loss project (to be WWW2022 submission)<br />
* Oct 22 - St3f: Coraland Citizen Science App for Families (R&R for CSCW) <br />
* Oct 29 - Nick V: The Theory of Critical Mass and Data Leverage<br />
* Nov 5 - Charlie: CSCW "Why These Rules?" submission / feedback on reviews and paper<br />
* Nov 12 - Sneha: Proposal for an NSF (or other) proposal on community governance<br />
* Nov 19 - Carl<br />
* <strike>Nov 26</strike> - Thanksgiving week / no meeting<br />
* Dec 3 - Regina: Three dissertation ideas<br />
* Dec 10 - Kaylea: Reviews & Revision Planning for the Qualities of Quality systematic lit review paper</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=251129CommunityData:Group Tasks2022-10-15T20:48:01Z<p>Nickmvincent: /* Sept. 2022 proposed roles */</p>
<hr />
<div>== Sept. 2022 proposed roles ==<br />
<br />
<br />
* '''Meetings & (internal) communications''' (Term: ≥1 quarter; Expected commitment: ~1 hour per week)<br />
** Meeting announcements/reminders, agendas, note-taking<br />
** Scheduling next quarter's meetings<br />
** Solicit updates to "important info" spreadsheet/form?<br />
* '''Events (workshops, softblocks, social)''' (Term: ≥1 quarter; Expected commitment: 1+ hour per week)<br />
** Workshops + softblocks<br />
*** Planning sessions/schedule, eliciting signups<br />
*** Wrangling presenters, materials for circulation<br />
*** Wrangling guests, lab speed-dates, etc.<br />
*** Session note-taking<br />
** Social gatherings<br />
*** Birthdays<br />
*** Plan one social event per quarter?<br />
** Swag and gifts<br />
*** 1-2 new swag items per year<br />
*** Holiday gift selection + distribution<br />
* '''Curatorial / janitorial''' (Term: ≥1 quarter; Expected commitment: ~1 hour per week)<br />
** Blogging schedule, regular posts<br />
** Social media updates<br />
** Update publications+people pages of the wiki<br />
** Zotero + wiki gardening tasks (like [[CommunityData:Wiki|fighting spam and patrolling pages]])<br />
* '''The IT crowd''' (Term: ≥1 quarter; Expected commitment: 0-5 hours per week supporting Mako) [etherpad: https://etherpad.communitydata.science/p/cdsc-tech ] <br />
** Web hosting, servers<br />
** Web services (wiki, blog, code, gitolite)<br />
** Manage shared accounts/services<br />
** Hyak environment monitoring, updates<br />
* '''Meta''' (Term: ≥2 quarters; Expected commitment: 0-5 hours per week)<br />
** Monitor tasks, division of labor, give feedback<br />
** Assign empty/new tasks<br />
** Coordinate documentation of group processes/resources<br />
** Facilitate newcomer orientation<br />
** Coordinate role turnover and handoffs<br />
** Facilitate quarterly group process assessments<br />
** Support administration, records, and reports for grants/awards<br />
<br />
===Assignments:===<br />
<br />
* Meetings (full group meetings) + internal communications: Regina, Dyuti. Nick as less core member with no other chore group, will try to set a standard of being first in line to take notes.<br />
* Events: Charlie, Hazel, Kevin<br />
** Note that Mollydb has owned dialogues, prospective open house!<br />
* Curatorial + janitorial: Floor, Emilia, Yibin<br />
** Note that Mollydb has owned blogpost queue. Please connect!<br />
* IT crowd: (Mako), Kaylea, Carl<br />
* Meta / management: Sohyeon, Ellie, Ryan<br />
<br />
<br />
<!--<br />
== Operations == <br />
<br />
Ongoing actions with pretty regular periodicity.<br />
<br />
=== Regular (weekly+) ===<br />
==== Task descriptions + info ====<br />
* Take notes in the etherpad during meetings. <br />
** See [[CommunityData:Etherpad | Etherpad Resources and How To]]<br />
<br />
* Send announcements (including reminders) for meetings.<br />
** Also entails sending reminders. For most meetings, try to include agenda in the announcement email<br />
** See [[CommunityData:Email | Email-related Resources and How To]]<br />
<br />
* Scheduling workshop sessions (new)<br />
** See: [[CommunityData:Workshop | Workshop]]<br />
** Previously handled via "soft blocks"<br />
<br />
* Communications<br />
** See [[CommunityData:Blog_and_social_media | Blog and Social Media Resources and How To]]<br />
** [[CommunityData:Blog_post_schedule | Blog post schedule]]<br />
<br />
==== Task distribution ====<br />
{| class="wikitable" <br />
|-<br />
! Task<br />
! Past contributors/organizers<br />
! Current (FA2021)<br />
|-<br />
| Etherpad notetaking in meetings<br />
| Aaron, Mako, Siying, Sohyeon, ...<br />
| [proposed that this one is distributed across people throughout the quarter]<br />
|-<br />
| Meeting announcements (reminders, agenda)<br />
| Aaron, Mako<br />
| <br />
|-<br />
| Workshop schedule (prev. soft blocks)<br />
| ...<br />
| <br />
|-<br />
| Communications (blogs)<br />
| Mako<br />
| <br />
|}<br />
<br />
=== Quarterly or so ===<br />
==== Task descriptions + info ====<br />
* Send around the scheduling poll each quarter <br />
** See: [[CommunityData:HowToWhenToMeet | When To Meet How To]]<br />
<br />
* Organize retreats (virtual and non)<br />
** See: [[CommunityData:Resources]]<br />
** Virtual retreats (primarily C+F Sessions)<br />
** In person retreats (more organizing tasks)<br />
* Plan social events<br />
** See past examples: [[CommunityData:Resources#Past_Meetups | Past Meetups]]<br />
<br />
==== Task distribution ====<br />
{| class="wikitable" <br />
|-<br />
! Task<br />
! Past contributors/organizers<br />
! Current (FA2021)<br />
|-<br />
| Scheduling poll<br />
| Kaylea, Nate, Regina, Sohyeon, ...<br />
| <br />
|-<br />
| Retreats: Virtual<br />
| Charlie, Floor, Kaylea, Sohyeon, ...<br />
| <br />
|-<br />
| Retreats: In-person<br />
| Floor, Sohyeon, ...<br />
| <br />
|-<br />
| Social events<br />
| [so far in the past, just part of retreats?]<br /><br />
| <br />
|}<br />
<br />
== Maintenance ==<br />
<br />
More focused on infrastructure and tools. Less regular/predictable intervals. Sometimes mission-critical!<br />
<br />
* Wiki Gardening <br />
** read over 1-2 pages and identify you think they are up to date<br />
** update lists of people, bios<br />
** update publications<br />
** update teaching/courses<br />
** update your user page!<br />
** go through resources page and...<br />
*** identify things that are out of out of date<br />
*** revise the out-of-date stuff and or recruit someone to help<br />
*** identify groupings of topics, links that makes sense<br />
** make new documentation that we know we want/need:<br />
*** write page about doing patrolling, creating accounts, etc<br />
* Software Updating<br />
* Sysadmin/app-admin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
<br />
== Group Nurturing ==<br />
<br />
People-focused, mostly less-frequent (quarterly-or-so).<br />
<br />
* facilitate group-process reflection/feedback/improvement sessions at retreats<br />
* train people on systems & tools (hyak/overleaf/R/etc.) The [[CommunityData:Resources | Resources Page]] is a general umbrella location.<br />
* newcomer orientations, recruitment, hiring <br />
* organize feedback sessions for RAs<br />
* outreach to adjacent communities, individuals who should come hang out!<br />
<br />
{| class="wikitable" <br />
|-<br />
! Task<br />
! Past contributors/organizers<br />
! Current (FA2021)<br />
|-<br />
| facilitate group-process sessions at retreats<br />
| Floor, Mako, Molly, Stef, Sohyeon, ...<br /><br />
| <br />
|-<br />
| train people on systems & tools<br />
| Floor, Mako, Nate, ...<br /><br />
| <br />
|-<br />
| newcomer orientations, recruitment, hiring<br />
| Aaron, Floor, Mako, Sneha, Sohyeon, ...<br />
| <br />
|-<br />
| organize feedback sessions for RAs<br />
| Floor<br /><br />
| <br />
|-<br />
| outreach to adjacent communities, individuals<br />
| <br />
| <br />
|}<br />
--></div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=251007CommunityData:Meetup October 20222022-10-11T18:32:34Z<p>Nickmvincent: /* Attendee List */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
The [[/Pre-meetup todo list]] is now available!<br />
<br />
== COVID-19 note ==<br />
<br />
Please note that this event will adhere to all applicable [https://www.washington.edu/coronavirus/ COVID-19 policies and guidelines] of the State of Washington and the University of Washington, including the [https://www.ehs.washington.edu/covid-19-prevention-and-response/covid-19-case-response UW COVID-19 case response guidance]. Some highlights and particulars:<br />
* We ask that you perform (at least) a rapid antigen test prior to beginning your participation in group meetup activities. The hosts (UW folks) will help ensure there is an ample supply of rapid antigen tests available in the CDSC lab space as of Thursday to make this easy. If you determine that a PCR test is preferable for any reason, we don't have those in the lab, but they are available in the area.<br />
* The University recommends wearing a [https://www.washington.edu/coronavirus/2022/01/27/where-to-pick-up-free-high-quality-masks-starting-jan-31/ high-quality mask] inside UW facilities where they aren’t otherwise required. <br />
* Been exposed? Tested positive recently? Not sure how to handle it? Please follow this [https://www.ehs.washington.edu/system/files/resources/COVID-19-public-health-flowchart.pdf COVID-19 public health flowchart] from the UW Environmental Health and Safety team that offers pretty comprehensive coverage of many scenarios.<br />
* If you have symptoms associated with COVID-19, please [https://www.ehs.washington.edu/covid-19-prevention-and-response/covid-19-isolation-guidance follow the corresponding UW guidance].<br />
* If you need help identifying/paying for a space to isolate or quarantine in the context of the group meetup activities, faculty in the group are prepared to help out with this (at minimum, financially if needed).<br />
<br />
== Agenda ==<br />
=== Thursday, October 13 ===<br />
<br />
{| class="wikitable"<br />
!Time<br />
!Event<br />
!Location<br />
!Notes<br />
|-<br />
| 5:30-7:30pm <br />
| Dinner <br />
| [https://www.aguaverdecafe.com/ AguaVerde] ''(To be confirmed!)'' <br />
| Low-key social/food in the U District (1303 NE Boat St, Seattle, WA 98105)<br />
|}<br />
<br />
=== Friday, October 14 ===<br />
<br />
''Note on rooms:'' In addition to the CDSC lab and meetings rooms, we have CMU 126 for the full day and CMU 322 (Newslab) after 2pm (although the room should be available all day.<br />
<br />
{| class="wikitable"<br />
|- <br />
!Time<br />
!Event<br />
!Location<br />
!Notes<br />
|-<br />
| 8:30-9:30am<br />
| [[:wikipedia:Continental breakfast|continental spread]]<br />
| [[Community Data Science Lab (UW)|CMU 306 / CDSC-UW Lab]]<br />
| We start promptly at 9:30 downstairs so arrive early if you want food/coffee!<br />
|-<br />
| 9:30-10:20am<br />
| Lightning Talks Round #1<br />
| CMU 126<br />
| Randomly assigned!<br />
|-<br />
| 10:20-10:30am<br />
| Break<br />
| <br />
|<br />
|- <br />
| 10:30-11:20am<br />
| 道 [[(dao/tao) of CDSC: a facilitated panel]]<br />
| CMU 126<br />
|<br />
|-<br />
| 11:20am-11:30am<br />
| Break<br />
|<br />
|<br />
|-<br />
| 11:30am-12:20pm<br />
| Lightning Talks Round #2<br />
| CMU 126<br />
| Randomly assigned!<br />
|-<br />
| 12:20pm-1:30pm<br />
| Lunch from ([https://www.xiannoodles.com/ Xi'an noodles]) <br />
| Outside/CMU 126<br />
| Big catering order will be vegetarian. Additional restrictions? Please [https://etherpad.communitydata.science/p/xiannoodles202210 write out your order here]<br />
|- <br />
| 1:30-2:00pm<br />
| Lightning Talks Round #3<br />
| CMU 126<br />
| Randomly assigned!<br />
|- <br />
| 2:00-3:00pm<br />
| [[Care and Feeding: whole-group conversation]]<br />
| CMU 126<br />
|<br />
|- <br />
| 3:00-3:30pm<br />
| PLACEHOLDER whole-group convo, 1-1s, and setup for reception<br />
| CMU 126<br />
|<br />
|-<br />
| 3:30-5:00pm<br />
| Open House / Reception (w/ Posters)<br />
| [[Community Data Science Lab (UW)|CMU 306 / CDSC-UW Lab]]<br />
|<br />
|-<br />
| 5:00-6:00pm<br />
| Group travel to dinner<br />
| Link Light Rail<br />
|<br />
|-<br />
| 5:00-6:00pm<br />
| Dinner<br />
| Different rail?<br />
|<br />
|}<br />
<br />
==== Reception ====<br />
<br />
Folks presenting posters include:<br />
<br />
* Kaylea (Taboo Topics)<br />
* Mako/Kaylea (Tor Wikipedia Edits)<br />
* Sohyeon (Bot stacking, work done w/ Charlie, Serene, Aaron)<br />
* ''...add yourself here?''<br />
<br />
=== Saturday, October 15 ===<br />
<br />
''Note on rooms:'' In addition to the CDSC lab and meetings rooms, we have CMU 126 for the full day and CMU 322 (Newslab), CMU 302 (Media Lab) and CMU 325 (Conference Room) from 8am-3pm. <br />
<br />
{| class="wikitable"<br />
|- <br />
!Time<br />
!Event<br />
!Location<br />
!Notes<br />
|-<br />
| 8:30-9:30am<br />
| [[:wikipedia:Continental breakfast|continental spread]]<br />
| [[Community Data Science Lab (UW)|CMU 306 / CDSC-UW Lab]]<br />
| We start promptly at 9:30 downstairs so arrive early if you want food/coffee!<br />
|-<br />
| 9:30-10:30am<br />
| '''Unconference Slot 1'''<br />
| TBD<br />
| See below!<br />
|-<br />
| 10:30-11:30am<br />
| '''Unconference Slot 2'''<br />
| TBD<br />
| See below!<br />
|-<br />
| 11:30-12:30pm<br />
| [[The_Great_Half_Bake-Off | The Great Half Bake Off]]<br />
| TBD<br />
| <br />
|-<br />
| 12:30-1:30pm<br />
| (optional) Lunch from [https://saigondeliuw.com/ Saigon Deli]<br />
| CMU 126 / Outside<br />
| Note we're having "dinner" not long after this. Please [https://etherpad.communitydata.science/p/saigondeli202210 write out your order here]<br />
|-<br />
| 1:30-2:30pm<br />
| '''Unconference Slot 3'''<br />
| TBD<br />
| See below!<br />
|-<br />
| 2:30-6pm<br />
| Low key social/food afternoon time<br />
| [https://extraordinary.leastsquar.es/ Extraordinary Least Squares]<br />
| pizza, beer, hanging out, etc<br />
|}<br />
<br />
<br />
Unconference potential topics:<br />
* '''[Confirmed]''' Reading Group for Coase's penguin: all newcomers should attend this session!<br />
* informed consent doc for public research brainstorming/writing<br />
* workgroups on lab duties f2f sessions<br />
* Covid-19 Digital Observatory (kc)<br />
<br />
=== Sunday, October 16 ===<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions (incl breakfast & lunch)<br />
! Friday Dinner<br />
! Saturday sessions (incl breakfast & lunch)<br />
! Saturday afternoon/evening (incl early dinner); Family welcome! (if so, how many people?)<br />
|-<br />
| Nate<br />
| Maybe<br />
| Most likely<br />
| Yes<br />
| Unlikely<br />
|<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Stef<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Divya<br />
| No<br />
| Yes - lunch<br />
| No<br />
| No<br />
|<br />
|-<br />
| Kaylea<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Most likely<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes (hopefully +3!)<br />
|-<br />
| Hsuen-Chi (Hazel)<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Regina<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Floor<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Salt<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Likely<br />
|-<br />
| Nick V<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Most likely<br />
|-<br />
| Ryan F<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Molly<br />
| no<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Yibin<br />
| Yes<br />
| Yes!<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Ellie<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Carl<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| [[Mako]]<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes (my guess is that 3-4ish housemates will be there)<br />
|-<br />
| Charlie<br />
| Yes<br />
| First half of day, second half will be in afternoon meetings<br />
| Maybe<br />
| Yes<br />
| Maybe<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|-<br />
| Floor<br />
| vegetarian (most of the time at least)<br />
|-<br />
| Charlie<br />
| vegetarian <br />
|-<br />
| Ryan<br />
| gluten-free <br />
|-<br />
| Molly<br />
| vegetarian (no cilantro)<br />
|-<br />
| Mako<br />
| vegetarian<br />
|-<br />
| Sohyeon<br />
| vegetarian (most of the time)<br />
|}<br />
<br />
== Accommodations ==<br />
<br />
=== Guests seeking accommodation ===<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Now<br />
| Staying with friends<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Hsuen-Chi (Hazel)<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Carl<br />
|Yes<br />
|Any<br />
|-<br />
|Nick V<br />
|Would like to join for Thurs-Sat -- but only if marginal impact of my participation is low.<br />
|Any<br />
|-<br />
|Ryan<br />
|Yes<br />
|Local Host or AirBnB`<br />
|}<br />
<br />
=== Potential hosts ===<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
! Match<br />
|-<br />
| Mako<br />
| Futon in the basement<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
| Molly<br />
|}<br />
<br />
== Organizing ==<br />
<br />
See the page on [[/Session planning]] for more details.<br />
<br />
The teams include:<br />
<br />
* [[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading accommodations.<br />
* Mako, Kaylea, and Yibin are organizing the schedule<br />
* Charlie, Regina, and Aaron are working on meal planning<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*St3f <br />
** 🛬Date - Wednesday, October 12 [arrival 9am]<br />
** 🛫Date - Monday, October 17 [departure 5pm]<br />
** Status: '''[PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Wednesday, October 12th [9:09a-11:46am; UA748]<br />
** 🛫Date - Sunday, October 16th [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Wednesday, October 12th [9:09a-11:46am; UA748]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - Thursday, October 13th [7:00-9:45a; Alaska 248]<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
** Accommodations requested!<br />
<br />
*Nick Vincent <br />
** I'm flying in early Tuesday* morning. I have lodging plans, but will be a bit far from UW, so I'm happy to join in on group accommodations if my marginal impact is low (i.e. if I can join in without requiring an additional room to be booked, and mainly just make things easier/cheaper and not harder/more expensive). Otherwise, I'll figure things out on my own!<br />
** 🛫Date - Sunday, October 16<br />
** Status: '''PURCHASED'''<br />
<br />
*Regina<br />
** 🛬Date - Thursday, October 13th [11:16am-02:26pm; DL1481]<br />
** 🛫Date - Tuesday, October 18th [03:16pm-06:00pm; DL1334]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation.<br />
<br />
*Molly<br />
** 🛬Date - Monday, October 10 [time and flight information]<br />
** 🛫Date - Sunday, October 16 [time and flight information]<br />
** Status: '''PURCHASED'''<br />
<br />
* Jeremy Dyuti, Hazel, Ryan<br />
** 🛬Date - Thursday, October 13th [3:42pm&ndash;6:21pm; UA2278]<br />
** 🛫Date - Sunday, October 16th [11:07am&ndash;5:07pm; USA2245]<br />
** Status: '''PURCHASED'''<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250875CommunityData:Meetup October 20222022-10-06T19:28:47Z<p>Nickmvincent: /* Guests seeking accommodation */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Low-key social/food'' (likely starting around 5:30pm near/in the U District)<br />
<br />
Probable venue: [https://www.aguaverdecafe.com/ AguaVerde] (1303 NE Boat St, Seattle, WA 98105).<br />
<br />
'''Friday, October 14'''<br />
302 if possible otherwise 126<br />
<br />
* 9:00 Breakfast ("continental" spread from Trader Joes?)<br />
* 9:30 Lightning talks <br />
* 10:00 Lightning talks cont'd <br />
* 10:30 道 [[(dao/tao) of CDSC: a facilitated panel]] <br />
* 11:00 道 (dao/tao) of CDSC <br />
* 11:30 Lightning talks <br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?) ([https://www.xiannoodles.com/ Xi'an noodles])<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Care and Feeding: whole-group conversation<br />
* 15:00 PLACEHOLDER whole-group convo (or 1-1s)<br />
* 15:30 Reception (we'll invite many friends from UW and elsewhere, 3:30-5, consuming delightful refreshments and snacks) -- hard start time/stop time<br />
* 16:00 Reception cont'd<br />
* 16:30 Reception cont'd<br />
Train to Dinner, hope to eat @18:00<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
302 and 322<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
* informed consent doc for public research brainstorming/writing<br />
* workgroups on lab duties f2f sessions<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakFeast ("continental" items)<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch ([https://saigondeliuw.com/ Saigon Deli])<br />
* 13:30 unconference, co-working<br />
<br />
''' Low key social/food afternoon time (@ [https://extraordinary.leastsquar.es/ ELS])'''<br />
* 14:30-18:00 Pizza from ???<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
=== Agenda Todo ===<br />
<br />
mako will request rooms:<br />
* request 322 and 302 all day on saturday<br />
* ask Andrea about 360 in 302 on Friday the 14th (Mako)<br />
** request 302 all day on friday if available and 126 in afternoon 3-6pm for reception if available)<br />
** request 126 all day on friday if not available<br />
<br />
* yibin will organize the lightning talks and slides<br />
* kaylea will plan/organize the tao of CDSC<br />
* mako will coordinate the reception<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions (incl breakfast & lunch)<br />
! Friday Dinner<br />
! Saturday sessions (incl breakfast & lunch)<br />
! Saturday afternoon/evening (incl early dinner)<br />
|-<br />
| Nate<br />
| Maybe<br />
| Most likely<br />
| Yes<br />
| Unlikely<br />
|<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Stef<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Divya<br />
| No<br />
| Yes<br />
| No<br />
| No?<br />
|<br />
|-<br />
| Kaylea<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Most likely<br />
<br />
|<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Hsuen-Chi (Hazel)<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Regina<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Floor<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Salt<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Nick V<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Ryan F<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Molly<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Yibin<br />
| Yes<br />
| Yes!<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Ellie<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Carl<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| [[Mako]]<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|<br />
|-<br />
| Charlie<br />
| Yes<br />
| First half of day, second half will be in afternoon meetings<br />
| Maybe<br />
| Yes<br />
| Maybe<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|-<br />
| Floor<br />
| vegetarian (most of the time at least)<br />
|-<br />
| Charlie<br />
| vegetarian <br />
|-<br />
| Ryan<br />
| gluten-free <br />
|-<br />
| Molly<br />
| vegetarian (no cilantro)<br />
|-<br />
| Mako<br />
| vegetarian<br />
|-<br />
| Sohyeon<br />
| vegetarian (most of the time)<br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - Thursday, October 13th [7:00-9:45a; Alaska 248]<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
** Accommodations requested!<br />
<br />
*Nick Vincent <br />
** I'm flying in early Tuesday* morning. I have lodging plans, but will be a bit far from UW, so I'm happy to join in on group accommodations if my marginal impact is low (i.e. if I can join in without requiring an additional room to be booked, and mainly just make things easier/cheaper and not harder/more expensive). Otherwise, I'll figure things out on my own!<br />
** 🛫Date - Sunday, October 16<br />
** Status: '''PURCHASED'''<br />
<br />
*Regina<br />
** 🛬Date - Thursday, October 13th [11:16am-02:26pm; DL1481]<br />
** 🛫Date - Tuesday, October 18th [03:16pm-06:00pm; DL1334]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation.<br />
<br />
*Molly<br />
** 🛬Date - Monday, October 10 [time and flight information]<br />
** 🛫Date - Sunday, October 16 [time and flight information]<br />
** Status: '''PURCHASED'''<br />
<br />
* Jeremy<br />
** 🛬Date - Thursday, October 13th [3:42pm&ndash;6:21pm; UA2278]<br />
** 🛫Date - Sunday, October 16th [11:07am&ndash;5:07pm; USA2245]<br />
** Status: '''PURCHASED'''<br />
<br />
*Dyuti<br />
** 🛬Date - Thursday, October 13th [3:42pm&ndash;6:21pm; UA2278]<br />
** 🛫Date - Sunday, October 16th [11:07am&ndash;5:07pm; USA2245]<br />
** Status: '''PURCHASED'''<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Hsuen-Chi (Hazel)<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Carl<br />
|Yes<br />
|Any<br />
|-<br />
|Nick V<br />
|Would like to join for Thurs-Sat -- but only if marginal impact of my participation is low.<br />
|Any<br />
|-<br />
|Ryan<br />
|Yes<br />
|Local Host or AirBnB`<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
! Match<br />
|-<br />
| Mako<br />
| Futon in the basement<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
| Molly<br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
=== Activity planning ===<br />
<br />
Mako, Kaylea, and Yibin are organizing the schedule<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ; NV)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF; NV)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ; NV)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250735CommunityData:Meetup October 20222022-09-29T19:36:10Z<p>Nickmvincent: /* Travel Plans */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Low-key social/food'' (likely starting near/in the U District, maybe AguaVerde)<br />
<br />
'''Friday, October 14'''<br />
<br />
* 9:00 Breakfast<br />
* 9:30 Lightning talks<br />
* 10:00 Lightning talks cont'd<br />
* 10:30 道 (dao/tao) of CDSC: a facilitated panel<br />
* 11:00 道 (dao/tao) of CDSC<br />
* 11:30 Lightning talks<br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?)<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Reception (we'll invite many friends from UW and elsewhere) -- hard start time/stop time<br />
* 15:00 Reception cont'd<br />
* 15:30 Reception cont'd<br />
* 16:00 Care and Feeding: whole-group conversation<br />
* 16:30 PLACEHOLDER whole-group convo (or 1-1s)<br />
Train to Dinner, hope to eat @18:00<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakFeast<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch<br />
* 13:30 unconference, co-working<br />
<br />
''' Low key social/food time (likely @ ELS pending confirmation)'''<br />
* Food team will order pizza?<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions<br />
! Friday Dinner<br />
! Saturday sessions<br />
|-<br />
| Nate<br />
| Maybe<br />
| Most likely<br />
| Yes<br />
| Unlikely<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Divya<br />
| ??<br />
| ??<br />
| ??<br />
| ??<br />
|-<br />
| Kaylea<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Hsuen-Chi (Hazel)<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Regina<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Floor<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Salt<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Nick V<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Ryan F<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Molly<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Yibin<br />
| Yes<br />
| Yes!<br />
| Yes<br />
| Yes<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|-<br />
| Floor<br />
| vegetarian (most of the time at least)<br />
|-<br />
| Charlie<br />
| vegetarian <br />
|-<br />
| Ryan<br />
| gluten-free <br />
|-<br />
| Molly<br />
| vegetarian (no cilantro)<br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - (not booked yet)<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
<br />
*Nick Vincent <br />
** I'm flying in early Tuesday* morning. I have lodging plans, but will be a bit far from UW, so I'm happy to join in on group accommodations if my marginal impact is low (i.e. if I can join in without requiring an additional room to be booked, and mainly just make things easier/cheaper and not harder/more expensive). Otherwise, I'll figure things out on my own!<br />
** 🛫Date - Sunday, October 16<br />
** Status: '''PURCHASED'''<br />
<br />
*Regina<br />
** 🛬Date - Thursday, October 13th [11:16am-02:26pm; DL1481]<br />
** 🛫Date - Tuesday, October 18th [03:16pm-06:00pm; DL1334]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation.<br />
<br />
*Molly<br />
** 🛬Date - Monday, October 10 [time and flight information]<br />
** 🛫Date - Sunday, October 16 [time and flight information]<br />
** Status: '''PURCHASED'''<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Lab duties ===<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Hsuen-Chi (Hazel)<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Carl<br />
|Yes<br />
|Any<br />
|-<br />
|Nick V<br />
|Yes<br />
|Any<br />
|-<br />
|Ryan<br />
|Yes<br />
|Local Host or AirBnB<br />
| -<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
|-<br />
| Mako<br />
| At least one guest room (will confirm more!)<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
==== Notes ====<br />
* Breakfast items + snacks obtained<br />
* Catering orders in (Friday & Saturday lunches, Saturday dinner)<br />
* RHCP Friday dinner reservation made<br />
<br />
=== Activity planning ===<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ; NV)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF; NV)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ; NV)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250571CommunityData:Meetup October 20222022-09-26T23:42:12Z<p>Nickmvincent: /* Travel Plans */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Evening Social Event''<br />
<br />
'''Friday, October 14'''<br />
<br />
* 9:00 Breakfast<br />
* 9:30 Lightning talks<br />
* 10:00 Lightning talks cont'd<br />
* 10:30 道 (dao/tao) of CDSC: a facilitated panel<br />
* 11:00 道 (dao/tao) of CDSC<br />
* 11:30 Lightning talks<br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?)<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Reception (we'll invite many friends from UW and elsewhere) -- hard start time/stop time<br />
* 15:00 Reception cont'd<br />
* 15:30 Reception cont'd<br />
* 16:00 Care and Feeding: whole-group conversation<br />
* 16:30 PLACEHOLDER whole-group convo (or 1-1s)<br />
Train to Dinner, hope to eat @18:00<br />
<br />
<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakFeast<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch<br />
* 13:30 unconference, co-working<br />
<br />
Social Activities On Your Own<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions<br />
! Friday Dinner<br />
! Saturday sessions<br />
|-<br />
| Nate<br />
| Maybe<br />
| Most likely<br />
| Yes<br />
| Unlikely<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Divya<br />
| ??<br />
| ??<br />
| ??<br />
| ??<br />
|-<br />
| Kaylea<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Hsuen-Chi (Hazel)<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Regina<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Floor<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Salt<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Nick V<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|-<br />
| Floor<br />
| vegetarian (most of the time at least)<br />
|-<br />
| Charlie<br />
| vegetarian <br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - (not booked yet)<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
<br />
*Nick Vincent <br />
** I'm planning to get in Monday morning. I'm also planning to figure out my own accommodations for Mon-Wed and join in group accommodations Thurs-Sat (if possible), although if anyone else wants to split lodging for the earlier days I'm open to it as well!<br />
** 🛫Date - Sunday, October 16<br />
** Status: '''PURCHASED'''<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Lab duties ===<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Hsuen-Chi (Hazel)<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Carl<br />
|Yes<br />
|Any<br />
|-<br />
|Nick V<br />
|Yes<br />
|Any<br />
| -<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
|-<br />
| Mako<br />
| At least one guest room (will confirm more!)<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
==== Notes ====<br />
* Breakfast items + snacks obtained<br />
* Catering orders in (Friday & Saturday lunches, Saturday dinner)<br />
* RHCP Friday dinner reservation made<br />
<br />
=== Activity planning ===<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ; NV)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF; NV)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ; NV)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250570CommunityData:Meetup October 20222022-09-26T23:41:12Z<p>Nickmvincent: /* Travel Plans */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Evening Social Event''<br />
<br />
'''Friday, October 14'''<br />
<br />
* 9:00 Breakfast<br />
* 9:30 Lightning talks<br />
* 10:00 Lightning talks cont'd<br />
* 10:30 道 (dao/tao) of CDSC: a facilitated panel<br />
* 11:00 道 (dao/tao) of CDSC<br />
* 11:30 Lightning talks<br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?)<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Reception (we'll invite many friends from UW and elsewhere) -- hard start time/stop time<br />
* 15:00 Reception cont'd<br />
* 15:30 Reception cont'd<br />
* 16:00 Care and Feeding: whole-group conversation<br />
* 16:30 PLACEHOLDER whole-group convo (or 1-1s)<br />
Train to Dinner, hope to eat @18:00<br />
<br />
<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakFeast<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch<br />
* 13:30 unconference, co-working<br />
<br />
Social Activities On Your Own<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions<br />
! Friday Dinner<br />
! Saturday sessions<br />
|-<br />
| Nate<br />
| Maybe<br />
| Most likely<br />
| Yes<br />
| Unlikely<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Divya<br />
| ??<br />
| ??<br />
| ??<br />
| ??<br />
|-<br />
| Kaylea<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Hsuen-Chi (Hazel)<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Regina<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Floor<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Salt<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Nick V<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|-<br />
| Floor<br />
| vegetarian (most of the time at least)<br />
|-<br />
| Charlie<br />
| vegetarian <br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - (not booked yet)<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
<br />
*Nick Vincent <br />
** I'm planning to get in Monday morning. I'm also planning to figure out my own accommodations for Mon-Wed, unless there happens to be availability.<br />
** 🛫Date - Sunday, October 16<br />
** Status: Purchases<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Lab duties ===<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Hsuen-Chi (Hazel)<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Carl<br />
|Yes<br />
|Any<br />
|-<br />
|Nick V<br />
|Yes<br />
|Any<br />
| -<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
|-<br />
| Mako<br />
| At least one guest room (will confirm more!)<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
==== Notes ====<br />
* Breakfast items + snacks obtained<br />
* Catering orders in (Friday & Saturday lunches, Saturday dinner)<br />
* RHCP Friday dinner reservation made<br />
<br />
=== Activity planning ===<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ; NV)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF; NV)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ; NV)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250546CommunityData:Meetup October 20222022-09-26T22:03:32Z<p>Nickmvincent: /* Attendee List */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Evening Social Event''<br />
<br />
'''Friday, October 14'''<br />
<br />
* 9:00 Breakfast<br />
* 9:30 Lightning talks<br />
* 10:00 Lightning talks cont'd<br />
* 10:30 道 (dao/tao) of CDSC: a facilitated panel<br />
* 11:00 道 (dao/tao) of CDSC<br />
* 11:30 Lightning talks<br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?)<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Reception (we'll invite many friends from UW and elsewhere) -- hard start time/stop time<br />
* 15:00 Reception cont'd<br />
* 15:30 Reception cont'd<br />
* 16:00 Care and Feeding: whole-group conversation<br />
* 16:30 PLACEHOLDER whole-group convo (or 1-1s)<br />
Train to Dinner, hope to eat @18:00<br />
<br />
<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakFeast<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch<br />
* 13:30 unconference, co-working<br />
<br />
Social Activities On Your Own<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions<br />
! Friday Dinner<br />
! Saturday sessions<br />
|-<br />
| Nate<br />
| Maybe<br />
| Most likely<br />
| Yes<br />
| Unlikely<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Divya<br />
| ??<br />
| ??<br />
| ??<br />
| ??<br />
|-<br />
| Kaylea<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Hsuen-Chi (Hazel)<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Regina<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Floor<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Salt<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Nick V<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|-<br />
| Floor<br />
| vegetarian (most of the time at least)<br />
|-<br />
| Charlie<br />
| vegetarian <br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - (not booked yet)<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
<br />
*Nick Vincent <br />
** No inbound flight yet 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - Sunday, October 16<br />
** Status: Outbound finalized, inbound pending<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Lab duties ===<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Hsuen-Chi (Hazel)<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Carl<br />
|Yes<br />
|Any<br />
|-<br />
|Nick V<br />
|Yes<br />
|Any<br />
| -<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
|-<br />
| Mako<br />
| At least one guest room (will confirm more!)<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
==== Notes ====<br />
* Breakfast items + snacks obtained<br />
* Catering orders in (Friday & Saturday lunches, Saturday dinner)<br />
* RHCP Friday dinner reservation made<br />
<br />
=== Activity planning ===<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ; NV)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF; NV)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ; NV)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250528CommunityData:Meetup October 20222022-09-25T21:34:33Z<p>Nickmvincent: /* Guests seeking accommodation */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Evening Social Event''<br />
<br />
'''Friday, October 14'''<br />
<br />
* 9:00 Breakfast<br />
* 9:30 Lightning talks<br />
* 10:00 Lightning talks cont'd<br />
* 10:30 道 (dao/tao) of CDSC: a facilitated panel<br />
* 11:00 道 (dao/tao) of CDSC<br />
* 11:30 Lightning talks<br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?)<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Reception (we'll invite many friends from UW and elsewhere) -- hard start time/stop time<br />
* 15:00 Reception cont'd<br />
* 15:30 Reception cont'd<br />
* 16:00 Care and Feeding: whole-group conversation<br />
* 16:30 PLACEHOLDER whole-group convo (or 1-1s)<br />
Train to Dinner, hope to eat @18:00<br />
<br />
<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakFeast<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch<br />
* 13:30 unconference, co-working<br />
<br />
Social Activities On Your Own<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions<br />
! Friday Dinner<br />
! Saturday sessions<br />
|-<br />
| Nate<br />
| Maybe<br />
| Most likely<br />
| Yes<br />
| Unlikely<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Divya<br />
| ??<br />
| ??<br />
| ??<br />
| ??<br />
|-<br />
| Kaylea<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Hsuen-Chi (Hazel)<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Regina<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Floor<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Salt<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|-<br />
| Floor<br />
| vegetarian (most of the time at least)<br />
|-<br />
| Charlie<br />
| vegetarian <br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - (not booked yet)<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
<br />
*Nick Vincent <br />
** No inbound flight yet 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - Sunday, October 16<br />
** Status: Outbound finalized, inbound pending<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Lab duties ===<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Hsuen-Chi (Hazel)<br />
|Yes<br />
|Local Host or AirBnB<br />
|-<br />
|Carl<br />
|Yes<br />
|Any<br />
|-<br />
|Nick V<br />
|Yes<br />
|Any<br />
| -<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
|-<br />
| Mako<br />
| At least one guest room (will confirm more!)<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
==== Notes ====<br />
* Breakfast items + snacks obtained<br />
* Catering orders in (Friday & Saturday lunches, Saturday dinner)<br />
* RHCP Friday dinner reservation made<br />
<br />
=== Activity planning ===<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ; NV)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF; NV)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ; NV)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250489CommunityData:Meetup October 20222022-09-21T22:42:22Z<p>Nickmvincent: /* Session ideas */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Evening Social Event''<br />
<br />
'''Friday, October 14'''<br />
<br />
* 9:00 Breakfast<br />
* 9:30 Lightning talks<br />
* 10:00 Lightning talks cont'd<br />
* 10:30 道 (dao/tao) of CDSC: a facilitated panel<br />
* 11:00 道 (dao/tao) of CDSC<br />
* 11:30 Lightning talks<br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?)<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Poster session (we'll invite many friends from UW and elsewhere) -- hard start time/stop time<br />
* 15:00 Poster session cont'd<br />
* 15:30 Poster session cont'd<br />
* 16:00 PLACEHOLDER whole-group convo (or 1-1s)<br />
* 16:30 PLACEHOLDER whole-group convo (or 1-1s)<br />
Train to Dinner, hope to eat @18:00<br />
<br />
<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakfeast<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch<br />
* 13:30 unconference, co-working<br />
<br />
Social Activities On Your Own<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions<br />
! Friday Dinner<br />
! Saturday sessions<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - (not booked yet)<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
<br />
*Nick Vincent <br />
** No inbound flight yet 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - Sunday, October 16<br />
** Status: Outbound finalized, inbound pending<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Lab duties ===<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
|-<br />
| Mako<br />
| At least one guest room (will confirm more!)<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
==== Notes ====<br />
* Breakfast items + snacks obtained<br />
* Catering orders in (Friday & Saturday lunches, Saturday dinner)<br />
* RHCP Friday dinner reservation made<br />
<br />
=== Activity planning ===<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ; NV)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ; NV)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF; NV)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ; NV)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Meetup_October_2022&diff=250488CommunityData:Meetup October 20222022-09-21T22:41:32Z<p>Nickmvincent: /* Travel Plans */</p>
<hr />
<div>We're meeting in Seattle on October 14 - 15! If you're calling in, we'll be at http://meet.jit.si/cdsc as per our custom.<br />
<br />
We'll be taking group notes at [https://etherpad.wikimedia.org/p/cdsc-202210 in a shared Etherpad for the event]. You can see [https://pad.riseup.net/p/CDSC-Spr19 the pad we created last time] for an idea of what this might look like.<br />
<br />
== Agenda==<br />
<br />
'''Thursday, October 13'''<br />
<br />
''Evening Social Event''<br />
<br />
'''Friday, October 14'''<br />
<br />
* 9:00 Breakfast<br />
* 9:30 Lightning talks<br />
* 10:00 Lightning talks cont'd<br />
* 10:30 道 (dao/tao) of CDSC: a facilitated panel<br />
* 11:00 道 (dao/tao) of CDSC<br />
* 11:30 Lightning talks<br />
* 12:00 Lightning talks<br />
* 12:30 Lunch (w/ a speaker?)<br />
* 13:00 Lunch con't -- schedule check: is this working or do we need to adjust our remaining time?<br />
* 13:30 Lightning talks<br />
* 14:00 Lightning talks cont'd<br />
* 14:30 Poster session (we'll invite many friends from UW and elsewhere) -- hard start time/stop time<br />
* 15:00 Poster session cont'd<br />
* 15:30 Poster session cont'd<br />
* 16:00 PLACEHOLDER whole-group convo (or 1-1s)<br />
* 16:30 PLACEHOLDER whole-group convo (or 1-1s)<br />
Train to Dinner, hope to eat @18:00<br />
<br />
<br />
<br />
'''Saturday, October 15'''<br />
''(Unconference/Parallel Sessions Day, may have spill over from Friday''<br />
<br />
Unconference potential topics:<br />
* reading group: ostrom (governing the commons chapter 1-3)<br />
* reading group: benkler (chapter 1)<br />
<br />
''Schedule''<br />
<br />
* 9:00 breakfeast<br />
* 9:30 unconference span<br />
* 10:30 half bake off<br />
* 12:30 Lunch<br />
* 13:30 unconference, co-working<br />
<br />
Social Activities On Your Own<br />
<br />
'''Sunday, October 16'''<br />
<br />
On Your Own<br />
<br />
== Attendee List ==<br />
<br />
{| class="wikitable"<br />
|-<br />
! Attendee <br />
! Thursday Dinner<br />
! Friday Sessions<br />
! Friday Dinner<br />
! Saturday sessions<br />
|-<br />
| Jeremy<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Emilia<br />
| No<br />
| Yes -- should be able to attend all, unless I need to TA from 2:30-3:30<br />
| No<br />
| Yes<br />
|-<br />
| Dyuti<br />
| Yes, if it's late enough<br />
| Yes<br />
| Yes<br />
| Yes<br />
|-<br />
| Aaron<br />
| Yes<br />
| Yes<br />
| Yes<br />
| Yes<br />
|}<br />
<br />
== Dietary Restrictions ==<br />
<br />
'''[No need to add yourself here if you are unrestricted.]'''<br />
{| class="wikitable"<br />
|-<br />
! Name<br />
! Dietary notes<br />
|-<br />
| Emilia<br />
| vegetarian -- have class during M/W/F lunch & unlikely to attend dinners -- prefer not to eat indoors<br />
|-<br />
| Aaron<br />
| vegetarian; allergies (nuts, sesame)<br />
|}<br />
<br />
== Travel Plans ==<br />
If you are traveling in from outside, add your name and arrival details here (days, times and flights if you have them, status (purchased/in-progress), any notes).<br />
<br />
Copy /paste format, fill in with your details:<br />
<br />
*[NAME] <br />
** 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** Status: '''[PURCHASED / NOT PURCHASED]'''<br />
<br />
*Floor<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October [DAY] [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Will arrange my own accommodation on the first three nights and the last night; so would like to crash somewhere Thu - Sat.<br />
<br />
*Sohyeon<br />
** 🛬Date - Monday, October 10th [7:25a-10:01am; UA580]<br />
** 🛫Date - Sunday, October 16 [5:59p-11:55p; DL1116]<br />
** Status: '''PURCHASED'''<br />
** Have arranged own accommodation for the first three nights; so looking to crash somewhere Thu, Fri, Sat nights.<br />
<br />
*Carl<br />
** 🛬Date - (not booked yet)<br />
** 🛫Date - Sunday, October 16 [10:10p-6:20a; DL2621]<br />
** Status: '''PURCHASED'''<br />
<br />
*Nick Vincent <br />
** No inbound flight yet 🛬Date - [WEEKDAY], October [DAY] [time and flight information]<br />
** 🛫Date - Sunday, October 16<br />
** Status: Outbound finalized, inbound pending<br />
<br />
----<br />
<br />
== Seattle People To-do==<br />
<br />
=== Lab duties ===<br />
<br />
=== Logistics Planning ===<br />
* Get attendance list + travel details of people flying in<br />
* Get dietary restrictions<br />
* Decide retreat schedule<br />
* Figure out what the social event will be<br />
* Determine restaurants/ordering food<br />
* Reach out to any potential speakers if we want to have research presentations<br />
* Schedule C&F sessions<br />
<br />
=== Accommodations ===<br />
[[User:Groceryheist | Nate]], [[User:Efgan |Emilia]], and Ellie are leading this. They will organize AirBnBs for people who want to stay in an AirBnB with other group members and link people able to offer accommodations to people coming in from out of town willing to stay at someone's home. People can get their own places to stay by themselves if they want as well. <br />
<br />
==== Guests seeking accommodation ====<br />
'''If you need accommodations,''' please fill out the form below.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Do you need accommodations? <br />
! What kind are you looking for (Local Host, AirBnb, Hotel)?<br />
|-<br />
| Sohyeon<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Stef<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Floor<br />
| Yes<br />
| Local host or AirBnb<br />
|-<br />
| Jeremy<br />
| Yes<br />
| Whatevs<br />
|-<br />
|Dyuti<br />
|Yes<br />
|Local Host or AirBnB<br />
|}<br />
<br />
==== Potential hosts ====<br />
<br />
'''If you are willing to host someone from out of town,''' please add yourself to this form:<br />
<br />
{| class="wikitable"<br />
|-<br />
! Name <br />
! Description of sleeping arrangement (how many couches, guest rooms)<br />
! Location (neighborhood, distance to campus)<br />
|-<br />
| Mako<br />
| At least one guest room (will confirm more!)<br />
| Capitol Hill (15m ride to UW / 25m home; bikes likely available); 30 minutes or so by light rail + walking <br />
|}<br />
<br />
=== Meal planning ===<br />
Charlie, Regina, and Aaron are working on this. <br />
<br />
* Obtain list of dietary restrictions<br />
* Plan breakfast/lunch/dinner/snacks<br />
* Make reservations / catering orders for any restaurants<br />
<br />
==== Notes ====<br />
* Breakfast items + snacks obtained<br />
* Catering orders in (Friday & Saturday lunches, Saturday dinner)<br />
* RHCP Friday dinner reservation made<br />
<br />
=== Activity planning ===<br />
<br />
* Outline work-related events<br />
* Decide on non-work events<br />
* Decide on where we will hold the event(s)<br />
<br />
==== Session ideas ====<br />
Please add your initials to vote for sessions / activities.<br />
<br />
Goals: help newcomers feel welcome; reinforcing and establishing a safe, shared exchange of ideas, that it's okay to pitch out-there ideas; not every idea needs to turn into a project; stuff that is hard to do remotely (making people feel like they're part of the club)<br />
<br />
* Housekeeping stuff / keeping people up to speed on things like Hyak (NT; BMH; MDB; JF)<br />
* Time for open-ended conversation on a set of topics or prompts<br />
* Ways to facilitate interaction and intellectual exchange<br />
* Half-baked off (BMH; MDB; JF; RC,DJ)<br />
* Lightning talks on everyone's work (so we all know what everyone else is doing) (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ)<br />
* A reading group of fun / canonical texts (BMH; MDB; YF, RC)<br />
* Social activities (NT; FF; SD; BMH; KC; MDB; JF; YF; RC,DJ)<br />
* Things that make a good newcomer experience (NT; FF; BMH; YF)<br />
* Expressing and communicating work and ideas (e.g. Matsuzaki outlines) (KC; RC,DJ)<br />
* The lifecycle of research, how we develop research questions, etc (MDB)<br />
* Things we wish we knew when we start(ed) grad school (BMH; JF; YF,DJ)<br />
* The CDSC way of being a scholar; how to be in a lab/the CDSC; what makes CDSC special/unique; (NT; FF; SD; BMH; KC; MDB; YF) <br />
* C&F / workshops (small number? plan for after newcomer workshop) (BMH; JF,DJ)<br />
* invite a guest(s) (SD; BMH; MDB) <br />
* coworking sessions/unconference (FF; BMH; KC)<br />
<br />
==== Ideas of fun things to do ====<br />
<br />
== Previous Meetups ==<br />
<br />
We meet roughly twice a year and you can see what we've done in the past at:<br />
<br />
* [[CommunityData:Meetup December 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
[[Category:CDSC meetups]]</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=238860User:Aaronshaw/AdvisingOH2022-06-30T18:08:04Z<p>Nickmvincent: /* July 14 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Summer 2022) quarter signups ==<br />
<br />
=== June 30 ===<br />
* 1400-1430: Nick V (can easily trade if anyone needs, very flexible)<br />
* 1430-1500: sohyeon<br />
<br />
=== July 7 ===<br />
* 1400-1430: «available»<br />
* 1430-1500: «available»<br />
=== July 14 ===<br />
* 1400-1430: sohyeon<br />
* 1430-1500: nick v<br />
<br />
=== July 21 ===<br />
* 1400-1430: «available»<br />
* 1430-1500: «available»<br />
=== July 28 ===<br />
* 1400-1430: sohyeon<br />
* 1430-1500: «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=237578User:Aaronshaw/AdvisingOH2022-06-25T03:50:29Z<p>Nickmvincent: /* June 30 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Summer 2022) quarter signups ==<br />
<br />
=== June 30 ===<br />
* 1400-1430: Nick V (can easily trade if anyone needs, very flexible)<br />
* 1430-1500: «available»<br />
<br />
=== July 7 ===<br />
* 1400-1430: «available»<br />
* 1430-1500: «available»<br />
=== July 14 ===<br />
* 1400-1430: sohyeon<br />
* 1430-1500: «available»<br />
=== July 21 ===<br />
* 1400-1430: «available»<br />
* 1430-1500: «available»<br />
=== July 28 ===<br />
* 1400-1430: sohyeon<br />
* 1430-1500: «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Resources&diff=231289CommunityData:Resources2022-04-29T22:31:50Z<p>Nickmvincent: /* Computation, Severs, and HPC */ typo</p>
<hr />
<div>This page collects resources for Community Data Science Collective members.<br />
<br />
== General Resources ==<br />
<br />
* [[CommunityData:Introduction to CDSC Resources]]<br />
<br />
== Non-Technical Resources ==<br />
<br />
* [[Schedule]] — Deadlines, events, and similar<br />
* [[CommunityData:Workshop]] — Weekly workshop sessions for sharing work and getting feedback<br />
* [[CommunityData:Jargon]] — Jargon and Common Shorthand<br />
* [[CommunityData:Planning document]] — Details on producing Matsuzaki-style planning documents<br />
* [[CommunityData:Research participant compensation]] — Notes on procedures related to human subjects compensation (e.g. for interview studies)<br />
* [[CommunityData:Logos]] — Like our visual branding, not like λόγος. Although we should always make sure we're good in that department too. very clear pointers. Save yourself the trouble and learn to follow these today!<br />
* [[Community Data Science Lab (UW)]] — Directions to the lab space at UW. This is something you can share with visitors.<br />
* [[Community Data Science Lab (NU) Pandemic research plan]] — Pandemic research plan for CDSC NU created as part of Northwestern's response to the COVID-19 pandemic.<br />
* [[CommunityData:General examinations motivating questions]] — A set of questions borrowed and adapted from [https://www.hcde.washington.edu/turns Jennifer Turns] that are a useful ways to start preparing for general examinations.<br />
<br />
== Communication Infrastructure ==<br />
<br />
* [[CommunityData:Email]] — Information on email lists, email aliases and their management.<br />
* [[CommunityData:IRC]] — How to get set up on our chat system, [[:wikipedia:IRC|IRC]]<br />
* [[CommunityData:Jitsi]] — Some etiquette/usability tips for Jitsi, our preferred video conference tool. <br />
* [[CommunityData:Blog and social media]] — Writing/editing blogposts, tweets, and social media<br />
<br />
* [[CommunityData:Blog post schedule]] — What's up next?<br />
<br />
== Research Infrastructure ==<br />
<br />
* [[CommunityData:Code]] — List of software projects maintained by the collective.<br />
* [[CommunityData:Exporting from Python to R]]<br />
* [[CommunityData:Git]] — Getting set up on the git server<br />
* [[CommunityData:Otter.ai]] — Audio-to-text transcription software<br />
* [[CommunityData:Taguette]] — Qualitative coding analysis software<br />
* [[CommunityData:Tmux]] — Using tmux (terminal multiplexer) to keep a persistent session on a server. <br />
* [[CommunityData:Zotero]] — How to use our shared Zotero directory.<br />
* [[CommunityData:Etherpad]] — We use [[:wikipedia:Etherpad|Etherpad]] for collaborative real-time note-taking and such. This page has some information about that as well details about how to make sure your pad is backedup.<br />
* [[CommunityData:MySQL]] How to use MySQL databases on Kibo.<br />
<br />
== Papers, Presentations, and Templates ==<br />
<br />
Stuff related to getting setup and/or troubleshooting things related to LaTeX and papers:<br />
<br />
* [[CommunityData:TeX]] — Installing our LaTeX templates<br />
* [[CommunityData:Beamer]] — Installing/using [[Mako]]'s Beamer templates<br />
* [[CommunityData:Knitr]] — Using Knitr with Tex to build graphs, tables, insert and format numbers in tex documents. <br />
* [[CommunityData:Embedding fonts in PDFs]] — <code>ggplot2</code> creates PDFs with fonts that are not embedded which, in turn, causes the ACM to bounce our papers back. This page describes how to fix it.<br />
* [[CommunityData:Build papers]] — Both the TeX and Beamer templates above come along with a Makefile that makes some assumptions about your workflow. Learn about that here.<br />
* [[CommunityData:LaTeX to Word]] — Some journals require submissions in Word format. Here are some options for doing that.<br />
* [[CommunityData:LaTex Diff]] — For an R+R, it's often helpful to create a PDF that shows the changes made. Here's one way to do that.<br />
<br />
A few of us use HTML-based presentation. Information on that is here:<br />
<br />
* [[CommunityData:reveal.js]] — Using RMarkdown to create reveal.js HTML presentations<br />
<br />
== Computation, Servers, and HPC ==<br />
<br />
* [[CommunityData:Compute Overview and Resource Matching]] -- What we have and what it's good for<br />
* [[CommunityData:Hyak]] — Using the Hyak supercomputer system at UW for research (several pages are linked from the top of that page)<br />
* [[CommunityData:Hyak_tutorial]] - Tutorial for new people to learn how to use Hyak.<br />
* [[CommunityData:Kibo]] — Getting started with the Kibo system at NU for research.<br />
* [[CommunityData:MySQL]] — Creating MySQL databases on Kibo<br />
* [[CommunityData:Northwestern VPN]] — Connecting to the Northwestern VPN<br />
* [[CommunityData:Backups (nada)]] — Details on what is, and what isn't, backed up from nada.<br />
<br />
== Research and Data ==<br />
<br />
* [[CommunityData:ORES]] - Using ORES with wikipedia data<br />
* [[CommunityData:Wikia data]] — Documents information about how to get and validate wikia dumps.<br />
<br />
Project Pages:<br />
<br />
* [[CommunityData:Message Walls]] -- Documents information about how to get and validate wikia dumps.<br />
<br />
== Future Meetings and Conferences ==<br />
<br />
* [[CommunityData:UW Weekly Meeting]]<br />
* [[CommunityData:Critique and Feedback Session]]<br />
<br />
== Past Meetups ==<br />
<br />
Group meetups:<br />
<br />
* [https://wiki.communitydata.science/Fall_2021_Retreat Online Retreat October 2021]<br />
* <strike>[[CommunityData:Meetup April 2020]]</strike> (cancelled due to [[COVID]])<br />
* [[CommunityData:Meetup September 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup April 2018: Organizational notes]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
Other meetups:<br />
<br />
* [[CommunityData:CSCW 2019]]<br />
* [[Sociotechnocanonicon|Sociotechnocanonicon Great Books Discussion Series]]<br />
<br />
== University of Washington Resources ==<br />
<br />
* [[CommunityData:Related seminars at UW]]<br />
* [[CommunityData:IRB training for Scratch Research at UW]]<br />
* [[CommunityData:UW NetID]]<br />
<br />
== Northwestern Resources ==<br />
<br />
* [[CommunityData:NU grant reimbursement]]<br />
* [https://wiki.communitydata.science/User:Aaronshaw/AdvisingOH Aaron's OH sign up]<br />
<br />
== Diversions == <br />
* [[CommunityData:Light events]] — The light in the lab at UW is funny. We have three fluorescent lights. On flipping the light switch, only two turn on. The third turns on ''eventually''. We are studying this arcane phenomenon.<br />
<br />
* [[CommunityData:GameIDs]] — A directory containing the game IDs for CDSC members to connect with each other across various gaming platforms.<br />
<br />
== Group Tasks == <br />
* [[CommunityData:Group Tasks]] - A collection of different tasks that benefit the group.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Not_So_Obvious_FAQ&diff=223800Not So Obvious FAQ2021-11-08T23:42:03Z<p>Nickmvincent: Created page with "The "Not So Obvious" Graduate School FAQ created by graduate student members of The Community Data Science Collective How much of grad school involves... actually taking clas..."</p>
<hr />
<div>The "Not So Obvious" Graduate School FAQ<br />
created by graduate student members of The Community Data Science Collective<br />
<br />
How much of grad school involves... actually taking classes?<br />
Around 2-4 years. The extent to which those classes are also determined by program or more flexibly chosen by the student is variable across programs. E.g. some programs have more thorough "core" curricula.<br />
<br />
<br />
What do you do the rest of the time?<br />
Mostly, research and teaching! Also, mentoring, service work (for instance review papers, organize social events, building community in program/across programs).<br />
<br />
<br />
How much do I pay / get paid?<br />
Doctoral programs will almost always *pay you* a stipend on top of covering your tuition (this is true of all the programs CDSC is involved with). Be very skeptical of any offers of admission you receive that do not include tuition + stipend. Currently, the stipend for NU's MTS and TSB is around 32k / year (no grad student union, though there are ongoing efforts to unionize), UW is about 22k/year (but you will be union and have *awesome* healthcare benefits plus formal job protections and solidarity benefits like recourse in the case of workplace harassment), and Purdue's stipend is 23-25k/year in a low cost of living city. You may also have opportunities for additional paid teaching and work roles. Finally, students who earn certain kinds of grants and fellowships or take industry internships can substantially increase their yearly compensations. <br />
Some specifics for increasing yearly compensations: For NU, if you get an external fellowship (aka a fellowship NOT coming from NU), they generally also give you a top-up of 6k a year ($500 per month).<br />
<br />
<br />
What does a successful year of grad school look like?<br />
Depends on your advisor, but... if you want to do research after grad school, submitting high-quality papers for publication, completing coursework, planning a long term research agenda. Publications are a primary (but not the only!) unit of "accomplishment" in academia, so much of grad school centers on research, writing, and publishing. If your primary goal is to teach, you'll definitely have that opportunity, both as a teaching assistant and as a supervised instructor of your own course (you might even get to design the course yourself).<br />
An important aspect of grad school that maybe is not super talked about is also becoming acquainted with the community of researchers/colleagues you want to be involved with moving forward and making those kinds of connections! This includes fellow grad students, many who do and will go on to do super cool things. This can happen through one's own volition or by taking advantage of events hosted by your program/institution, conference + social events, etc.<br />
<br />
How important are advisors/to what extent do they really control the grad school experience?<br />
The working dynamic between you and your advisor is always dependent on, unsurprisingly (1) you and (2) your advisor. Getting a sense of a potential advisor's working style by talking to previous/curent students can be helpful. And once you get an advisor, having conversations on Day One on what you expect from one another is also very important! Setting shared expectations can save a lot of trouble in the long run, and having a working relationship with an advisor in this way can really improve the grad school experience. I think it's important to work with someone you personally vibe with.<br />
It is also good and generally encouraged to work with faculty who are NOT your advisor; this expands your collaborative experiences and also, fosters potential to find a diverse set of mentors (but probably don't assume that a more senior collaborator on a project is automatically going to be willing to put in the labor of mentoring as well). I personally think that having multiple mentors is great, because it diversifies the feedback/advice you get and can put less pressure on a single individual to guide your academic career.<br />
<br />
<br />
What happens when my paper is accepted?<br />
For conference papers (more important in computing fields), you also get to / have to present the work at a conference.<br />
<br />
<br />
What impacts what hours I work, what my day to day looks like, etc.?<br />
Grad school is generally more flexible than other fields of employment. However, things like work hours and day-to-day structure may depend on your *advisor* and *lab*. Some groups try to keep fairly typical schedules (e.g. everyone is available between 9a and 5p). Other groups may embrace odd work hours (e.g. you and your advisor may collaborate on a research project late at night)---this might also be super regular, e.g. crunching for a deadline and setting work sessions for that purpose. If you're at UW, your union contract is for 20 hrs/week (whether you are teaching or doing research; if your work goes outside that boundary then there is recourse) and a full-time course load is 10 credits, translating to a target of 30 hrs/week of time in class/homework.<br />
In some ways, being a grad student is a lot like being an entrepreneur: you will need to set many of your goals yourself and structure your own time. You might have quarterly goals, but how to reach them may be unclear -- in fact, figuring that out is often part of the goal itself.<br />
We highly advise you to talk to potential advisors about this topic early and often! Setting boundaries is always a good thing given that graduate school can be so flexible/undefined in this sense.<br />
<br />
Is grad school really as horrible as it sounds on Twitter/in comics/on TV? <br />
Nope. It's hard but it's super fun. Also work. A lot of work. If you're suffering in grad school then please reach out for resources to make a change.<br />
<br />
How would I know if grad school isn't really for me?<br />
Grad school is not 'the next thing after undergrad'. Being an expert learner like you were in college might not make you a very good creator of new knowledge. In fact, many people pursue a PhD many years after undergrad, due to the experiences outside of academia (and these non-academic experiences are in many senses a real advantage/boon). A PhD is not necessary for most careers -- in fact, it might make certain career options closed to you if you disclose having earned it. Why do you want to go to grad school?<br />
Are you curious about a range of topics, concepts, and settings -- or very curious about a single topic/concept/setting? Are you able to make long-term commitments and keep them? Do you feel deep joy from working hard on complex problems you care about? Are you willing to read a lot and think really hard for a long period of time? Can you do that hard work and then live with being wrong in public, repeatedly? Can you take tough critique? Are you ok with a profession that makes work-life balance a bit illusory or at least messy and hard? <br />
How are you with ambiguity? Can you set your own schedule and get things done, maybe without a lot of hand-holding? Some advisors are more hands on than others, but part of being a graduate student is training to be an independent researcher/scientist/knowledge-creator: advocating for your interests, deciding what to do when, and finding problems and solutions in a community but perhaps with very limited direction is part of the experience.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=PhD_Q%26A_-_2021&diff=223799PhD Q&A - 20212021-11-08T23:39:05Z<p>Nickmvincent: </p>
<hr />
<div><br />
<br />
'''Meeting room link: [https://meet.gnome.org/mol-npz-rnf-hio https://meet.gnome.org/mol-npz-rnf-hio]'''<br />
<br />
'''[https://docs.google.com/presentation/d/19Ba7Zt_enM4aN3k2-L9pl53uyXpMAWdlwoua21zTBgc/edit?usp=sharing Slides] (with a bunch of useful links)'''<br />
<br />
[[Not So Obvious FAQ]]<br />
<br />
* Introductions to CDSC<br />
* Q&A with Faculty<br />
* Breakout rooms with current and former students<br />
<br />
=CDSC Attendees=<br />
<br />
Read more about us on [[People]].<br />
<br />
==Faculty==<br />
* Sayamindu Dasgupta - UNC Chapel Hill / University of Washington (probable!)<br />
* Jeremy Foote - Purdue University<br />
* Benjamin Mako Hill - University of Washington<br />
* Sneha Narayan - Carleton College<br />
* Aaron Shaw - Northwestern University<br />
<br />
==Students==<br />
* Kaylea Champion - University of Washington. Interests: collaboration, infrastructure, anonymity, connecting research and practice<br />
* Regina Cheng - University of Washington (HCDE). Interests: interest-driven learning in online communities, creativity support, data science learning & collaboration <br />
* Carl Colglazier - Northwestern (Technology and Social Behavior). Interests: computational social science, decentralized (sometimes community-led) online governance, small tech.<br />
* Floor Fiers - Northwestern (Media, Technology, and Society): Interests: digital inequality/discrimination, skills, online labor market<br />
* Sohyeon Hwang - Northwestern (Media, Technology, and Society). Interests: online governance, heterogeneity, scale<br />
* Nathan TeBlunthuis - Northwestern (Postdoc). interests: computational social science, online communties, peer production, social movements, inter-organizational relationships.<br />
* Nick Vincent - Northwestern. Interests: human-centered machine learning, connections between various online communities and other systems<br />
<br />
=Room Links=<br />
<br />
* Main Room - https://meet.gnome.org/mol-npz-rnf-hio<br />
* Breakout Room 1 (Nick and Regina) - https://meet.gnome.org/mol-d1q-lrr-r5l<br />
* Breakout Room 2 (Carl and Nathan) - https://meet.gnome.org/mol-40p-xj1-rhi<br />
* Breakout Room 3 (Floor, Kaylea, and Sohyeon)- https://meet.gnome.org/mol-wuf-iqb-s3r<br />
* Breakout Room 4 (Jeremy about Purdue)- https://meet.gnome.org/mol-pox-9ds-g5v</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=PhD_Q%26A_-_2021&diff=223644PhD Q&A - 20212021-11-05T03:54:16Z<p>Nickmvincent: </p>
<hr />
<div>=CDSC Attendees=<br />
<br />
Read more about us on [[People]].<br />
<br />
==Faculty==<br />
* Jeremy Foote - Purdue<br />
* Benjamin Mako Hill - University of Washington<br />
* Aaron Shaw - Northwestern<br />
<br />
==Students==<br />
* Kaylea Champion - University of Washington<br />
* Regina Cheng - University of Washington (HCDE)<br />
* Carl Colglazier - Northwestern<br />
* Floor Fiers - Northwestern<br />
* Sohyeon Hwang - Northwestern<br />
* Nathan TeBlunthuis - University of Washington<br />
* Nick Vincent - Northwestern. Interests: human-centered machine learning, connections between various online communities and other systems<br />
<br />
=Room Links=<br />
<br />
* Main Room - https://meet.gnome.org/mol-npz-rnf-hio<br />
* Breakout Room 1 - https://meet.gnome.org/mol-d1q-lrr-r5l<br />
* Breakout Room 2 - https://meet.gnome.org/mol-40p-xj1-rhi<br />
* Breakout Room 3 - https://meet.gnome.org/mol-wuf-iqb-s3r<br />
* Breakout Room 4 - https://meet.gnome.org/mol-pox-9ds-g5v<br />
* Breakout Room 5 - https://meet.gnome.org/mol-k4t-tcl-6x1<br />
* Purdue - Breakout Room 6 - https://meet.gnome.org/mol-ggt-ebj-nj7</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=223176CommunityData:Group Tasks2021-10-09T03:33:54Z<p>Nickmvincent: /* Quarterly or so */</p>
<hr />
<div>== Operations == <br />
<br />
Ongoing actions with pretty regular periodicity.<br />
<br />
=== Regular (weekly+) ===<br />
* Take notes in the etherpad during meetings. <br />
** See [[CommunityData:Etherpad | Etherpad Resources and How To]]<br />
<br />
* Send announcements (including reminders) for meetings.<br />
** Also entails sending reminders<br />
** For most meetings, try to include agenda in the announcement email<br />
** See [[CommunityData:Email | Email-related Resources and How To]]<br />
<br />
* Scheduling workshop sessions (new)<br />
** See: [[CommunityData:Workshop | Workshop]]<br />
** Previously handled via "soft blocks"<br />
<br />
* Communications<br />
** See [[CommunityData:Blog_and_social_media | Blog and Social Media Resources and How To]]<br />
** Blog post and social media info: [[CommunityData:Blog_and_social_media | Blog]]<br />
** [[CommunityData:Blog_post_schedule | Blog post schedule]]<br />
<br />
=== Quarterly or so ===<br />
* Send around the scheduling poll each quarter <br />
** See: [[CommunityData:HowToWhenToMeet | When To Meet How To]]<br />
* Organize retreats (virtual and non)<br />
** See: [[CommunityData:Resources]]<br />
** Virtual retreats<br />
** In person retreats (more organizing tasks)<br />
* Plan social events<br />
** See past examples: [[CommunityData:Resources#Past_Meetups | Past Meetups]]<br />
<br />
== Maintenance ==<br />
<br />
More focused on infrastructure and tools. Less regular/predictable intervals. Sometimes mission-critical!<br />
<br />
* Wiki Gardening <br />
** read over 1-2 pages and identify you think they are up to date<br />
** update lists of people, bios<br />
** update publications<br />
** update teaching/courses<br />
** update your user page!<br />
** go through resources page and...<br />
*** identify things that are out of out of date<br />
*** revise the out-of-date stuff and or recruit someone to help<br />
*** identify groupings of topics, links that makes sense<br />
** make new documentation that we know we want/need:<br />
*** write page about doing patrolling, creating accounts, etc<br />
* Software Updating<br />
* Sysadmin/app-admin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
<br />
== Group Nurturing ==<br />
<br />
People-focused, mostly less-frequent (quarterly-or-so).<br />
<br />
* facilitate group-process reflection/feedback/improvement sessions at retreats<br />
* train people on systems & tools (hyak/overleaf/R/etc.) The [[CommunityData:Resources | Resources Page]] is a general umbrella location.<br />
* newcomer orientations, recruitment, hiring <br />
* organize feedback sessions for RAs<br />
* outreach to adjacent communities, individuals who should come hang out!</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=223175CommunityData:Group Tasks2021-10-09T03:33:11Z<p>Nickmvincent: /* Regular (weekly+) */</p>
<hr />
<div>== Operations == <br />
<br />
Ongoing actions with pretty regular periodicity.<br />
<br />
=== Regular (weekly+) ===<br />
* Take notes in the etherpad during meetings. <br />
** See [[CommunityData:Etherpad | Etherpad Resources and How To]]<br />
<br />
* Send announcements (including reminders) for meetings.<br />
** Also entails sending reminders<br />
** For most meetings, try to include agenda in the announcement email<br />
** See [[CommunityData:Email | Email-related Resources and How To]]<br />
<br />
* Scheduling workshop sessions (new)<br />
** See: [[CommunityData:Workshop | Workshop]]<br />
** Previously handled via "soft blocks"<br />
<br />
* Communications<br />
** See [[CommunityData:Blog_and_social_media | Blog and Social Media Resources and How To]]<br />
** Blog post and social media info: [[CommunityData:Blog_and_social_media | Blog]]<br />
** [[CommunityData:Blog_post_schedule | Blog post schedule]]<br />
<br />
=== Quarterly or so ===<br />
* Send around the scheduling poll each quarter <br />
** See: [[CommunityData:HowToWhenToMeet | When To Meet How To]]<br />
* Organize retreats (virtual and non)<br />
** See: [[CommunityData:Resources]]<br />
** Virtual retreats<br />
** In person retreats (more organizing tasks)<br />
* Plan social events<br />
** see list at: [[CommunityData:Resources]]<br />
<br />
== Maintenance ==<br />
<br />
More focused on infrastructure and tools. Less regular/predictable intervals. Sometimes mission-critical!<br />
<br />
* Wiki Gardening <br />
** read over 1-2 pages and identify you think they are up to date<br />
** update lists of people, bios<br />
** update publications<br />
** update teaching/courses<br />
** update your user page!<br />
** go through resources page and...<br />
*** identify things that are out of out of date<br />
*** revise the out-of-date stuff and or recruit someone to help<br />
*** identify groupings of topics, links that makes sense<br />
** make new documentation that we know we want/need:<br />
*** write page about doing patrolling, creating accounts, etc<br />
* Software Updating<br />
* Sysadmin/app-admin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
<br />
== Group Nurturing ==<br />
<br />
People-focused, mostly less-frequent (quarterly-or-so).<br />
<br />
* facilitate group-process reflection/feedback/improvement sessions at retreats<br />
* train people on systems & tools (hyak/overleaf/R/etc.) The [[CommunityData:Resources | Resources Page]] is a general umbrella location.<br />
* newcomer orientations, recruitment, hiring <br />
* organize feedback sessions for RAs<br />
* outreach to adjacent communities, individuals who should come hang out!</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=223173CommunityData:Group Tasks2021-10-09T03:30:10Z<p>Nickmvincent: /* Quarterly or so */</p>
<hr />
<div>== Operations == <br />
<br />
Ongoing actions with pretty regular periodicity.<br />
<br />
=== Regular (weekly+) ===<br />
* Take notes in the etherpad during meetings. <br />
** See: [[CommunityData:Etherpad | Etherpad Resources and How To]]<br />
<br />
* Send announcements (including reminders) for meetings.<br />
** Also entails sending reminders<br />
** For most meetings, try to include agenda in the announcement email<br />
** See [[CommunityData:Email | Email-related Resources and How To]] for more.<br />
<br />
* Scheduling workshop sessions (new)<br />
** See: [[CommunityData:Workshop | Workshop]]<br />
** Previously handled via "soft blocks"<br />
<br />
* Communications<br />
** See [[CommunityData:Blog_and_social_media | Blog and Social Media Resources and How To]]<br />
** Blog posts<br />
** Social Media (twitter)<br />
<br />
=== Quarterly or so ===<br />
* Send around the scheduling poll each quarter <br />
** See: [[CommunityData:HowToWhenToMeet | When To Meet How To]]<br />
* Organize retreats (virtual and non)<br />
** See: [[CommunityData:Resources]]<br />
** Virtual retreats<br />
** In person retreats (more organizing tasks)<br />
* Plan social events<br />
** see list at: [[CommunityData:Resources]]<br />
<br />
== Maintenance ==<br />
<br />
More focused on infrastructure and tools. Less regular/predictable intervals. Sometimes mission-critical!<br />
<br />
* Wiki Gardening <br />
** read over 1-2 pages and identify you think they are up to date<br />
** update lists of people, bios<br />
** update publications<br />
** update teaching/courses<br />
** update your user page!<br />
** go through resources page and...<br />
*** identify things that are out of out of date<br />
*** revise the out-of-date stuff and or recruit someone to help<br />
*** identify groupings of topics, links that makes sense<br />
** make new documentation that we know we want/need:<br />
*** write page about doing patrolling, creating accounts, etc<br />
* Software Updating<br />
* Sysadmin/app-admin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
<br />
== Group Nurturing ==<br />
<br />
People-focused, mostly less-frequent (quarterly-or-so).<br />
<br />
* facilitate group-process reflection/feedback/improvement sessions at retreats<br />
* train people on systems & tools (hyak/overleaf/R/etc.) The [[CommunityData:Resources | Resources Page]] is a general umbrella location.<br />
* newcomer orientations, recruitment, hiring <br />
* organize feedback sessions for RAs<br />
* outreach to adjacent communities, individuals who should come hang out!</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=223172CommunityData:Group Tasks2021-10-09T03:29:41Z<p>Nickmvincent: /* Regular (weekly+) */</p>
<hr />
<div>== Operations == <br />
<br />
Ongoing actions with pretty regular periodicity.<br />
<br />
=== Regular (weekly+) ===<br />
* Take notes in the etherpad during meetings. <br />
** See: [[CommunityData:Etherpad | Etherpad Resources and How To]]<br />
<br />
* Send announcements (including reminders) for meetings.<br />
** Also entails sending reminders<br />
** For most meetings, try to include agenda in the announcement email<br />
** See [[CommunityData:Email | Email-related Resources and How To]] for more.<br />
<br />
* Scheduling workshop sessions (new)<br />
** See: [[CommunityData:Workshop | Workshop]]<br />
** Previously handled via "soft blocks"<br />
<br />
* Communications<br />
** See [[CommunityData:Blog_and_social_media | Blog and Social Media Resources and How To]]<br />
** Blog posts<br />
** Social Media (twitter)<br />
<br />
=== Quarterly or so ===<br />
Send around the scheduling poll each quarter <br />
* See: [[CommunityData:HowToWhenToMeet | When To Meet How To page]]<br />
<br />
<br />
Organize retreats (virtual and non)<br />
* See: [[CommunityData:Resources]]<br />
* Virtual retreats<br />
* In person retreats (more organizing tasks)<br />
<br />
<br />
Plan social events<br />
* see list at: [[CommunityData:Resources]]<br />
<br />
== Maintenance ==<br />
<br />
More focused on infrastructure and tools. Less regular/predictable intervals. Sometimes mission-critical!<br />
<br />
* Wiki Gardening <br />
** read over 1-2 pages and identify you think they are up to date<br />
** update lists of people, bios<br />
** update publications<br />
** update teaching/courses<br />
** update your user page!<br />
** go through resources page and...<br />
*** identify things that are out of out of date<br />
*** revise the out-of-date stuff and or recruit someone to help<br />
*** identify groupings of topics, links that makes sense<br />
** make new documentation that we know we want/need:<br />
*** write page about doing patrolling, creating accounts, etc<br />
* Software Updating<br />
* Sysadmin/app-admin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
<br />
== Group Nurturing ==<br />
<br />
People-focused, mostly less-frequent (quarterly-or-so).<br />
<br />
* facilitate group-process reflection/feedback/improvement sessions at retreats<br />
* train people on systems & tools (hyak/overleaf/R/etc.) The [[CommunityData:Resources | Resources Page]] is a general umbrella location.<br />
* newcomer orientations, recruitment, hiring <br />
* organize feedback sessions for RAs<br />
* outreach to adjacent communities, individuals who should come hang out!</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=223171CommunityData:Group Tasks2021-10-09T03:28:07Z<p>Nickmvincent: /* Quarterly or so */</p>
<hr />
<div>== Operations == <br />
<br />
Ongoing actions with pretty regular periodicity.<br />
<br />
=== Regular (weekly+) ===<br />
Take notes in the etherpad during meetings. <br />
* See: [[CommunityData:Etherpad | Etherpad Resources and How To page]]<br />
<br />
Send announcements (including reminders) for meetings.<br />
* Also entails sending reminders<br />
* For most meetings, try to include agenda in the announcement email<br />
* See [[CommunityData:Email | Email-related Resources and How To page]] for more.<br />
<br />
Scheduling workshop sessions (new)<br />
* See: [[CommunityData:Workshop | Workshop page]]<br />
* Previously handled via "soft blocks"<br />
<br />
Communications<br />
* See [[CommunityData:Blog_and_social_media | Blog and Social Media Resources and How To page]]<br />
* Blog posts<br />
* Social Media (twitter)<br />
<br />
=== Quarterly or so ===<br />
Send around the scheduling poll each quarter <br />
* See: [[CommunityData:HowToWhenToMeet | When To Meet How To page]]<br />
<br />
<br />
Organize retreats (virtual and non)<br />
* See: [[CommunityData:Resources]]<br />
* Virtual retreats<br />
* In person retreats (more organizing tasks)<br />
<br />
<br />
Plan social events<br />
* see list at: [[CommunityData:Resources]]<br />
<br />
== Maintenance ==<br />
<br />
More focused on infrastructure and tools. Less regular/predictable intervals. Sometimes mission-critical!<br />
<br />
* Wiki Gardening <br />
** read over 1-2 pages and identify you think they are up to date<br />
** update lists of people, bios<br />
** update publications<br />
** update teaching/courses<br />
** update your user page!<br />
** go through resources page and...<br />
*** identify things that are out of out of date<br />
*** revise the out-of-date stuff and or recruit someone to help<br />
*** identify groupings of topics, links that makes sense<br />
** make new documentation that we know we want/need:<br />
*** write page about doing patrolling, creating accounts, etc<br />
* Software Updating<br />
* Sysadmin/app-admin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
<br />
== Group Nurturing ==<br />
<br />
People-focused, mostly less-frequent (quarterly-or-so).<br />
<br />
* facilitate group-process reflection/feedback/improvement sessions at retreats<br />
* train people on systems & tools (hyak/overleaf/R/etc.) The [[CommunityData:Resources | Resources Page]] is a general umbrella location.<br />
* newcomer orientations, recruitment, hiring <br />
* organize feedback sessions for RAs<br />
* outreach to adjacent communities, individuals who should come hang out!</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=223170CommunityData:Group Tasks2021-10-09T03:26:57Z<p>Nickmvincent: /* Regular (weekly+) */</p>
<hr />
<div>== Operations == <br />
<br />
Ongoing actions with pretty regular periodicity.<br />
<br />
=== Regular (weekly+) ===<br />
Take notes in the etherpad during meetings. <br />
* See: [[CommunityData:Etherpad | Etherpad Resources and How To page]]<br />
<br />
Send announcements (including reminders) for meetings.<br />
* Also entails sending reminders<br />
* For most meetings, try to include agenda in the announcement email<br />
* See [[CommunityData:Email | Email-related Resources and How To page]] for more.<br />
<br />
Scheduling workshop sessions (new)<br />
* See: [[CommunityData:Workshop | Workshop page]]<br />
* Previously handled via "soft blocks"<br />
<br />
Communications<br />
* See [[CommunityData:Blog_and_social_media | Blog and Social Media Resources and How To page]]<br />
* Blog posts<br />
* Social Media (twitter)<br />
<br />
=== Quarterly or so ===<br />
* send around the scheduling poll each quarter [[CommunityData:HowToWhenToMeet | When To Meet How To]]<br />
* organize retreats (virtual and non) -- see list at: [[CommunityData:Resources]]<br />
* plan social events -- see list at: [[CommunityData:Resources]]<br />
<br />
== Maintenance ==<br />
<br />
More focused on infrastructure and tools. Less regular/predictable intervals. Sometimes mission-critical!<br />
<br />
* Wiki Gardening <br />
** read over 1-2 pages and identify you think they are up to date<br />
** update lists of people, bios<br />
** update publications<br />
** update teaching/courses<br />
** update your user page!<br />
** go through resources page and...<br />
*** identify things that are out of out of date<br />
*** revise the out-of-date stuff and or recruit someone to help<br />
*** identify groupings of topics, links that makes sense<br />
** make new documentation that we know we want/need:<br />
*** write page about doing patrolling, creating accounts, etc<br />
* Software Updating<br />
* Sysadmin/app-admin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
<br />
== Group Nurturing ==<br />
<br />
People-focused, mostly less-frequent (quarterly-or-so).<br />
<br />
* facilitate group-process reflection/feedback/improvement sessions at retreats<br />
* train people on systems & tools (hyak/overleaf/R/etc.) The [[CommunityData:Resources | Resources Page]] is a general umbrella location.<br />
* newcomer orientations, recruitment, hiring <br />
* organize feedback sessions for RAs<br />
* outreach to adjacent communities, individuals who should come hang out!</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=People&diff=223145People2021-10-08T16:53:21Z<p>Nickmvincent: /* Nick Vincent (Northwestern) */</p>
<hr />
<div>We're an interdisciplinary group at Northwestern University and the University of Washington. Faculty, postdocs, graduate students, affiliates, and alumni are listed below (in alphabetical order within each section) except when we've failed at alphabetizing.<br />
<br />
You can see pictures of all together over at our [[group photos]] page. Pictures of us individually are here.<br />
<br />
We are a friendly group and we welcome new affiliates! If you have been working with us for a while, perhaps it's time to add yourself to this page as an affiliate. Feel free to add yourself (use the Edit tab), and please include a sentence on HOW you are related to the group (and a fun picture of yourself!).<br />
<br />
== Faculty ==<br />
<div style="clear:both;"><br />
=== Sayamindu Dasgupta (University of North Carolina at Chapel Hill) ===<br />
<br />
[[File:Sayamindu.jpg|thumb|200px|Sayamindu, mildly perturbed.]]<br />
<br />
I grew up in the city of Kolkata, India. At some point in school I wanted to study Physics, but then soon after, I came across computers, which messed up my plans considerably. Roughly 9 years after I had my first encounter with a computer, I read Seymour Papert’s ''Mindstorms: Children, Computers and Powerful Ideas'', and a year after that, I found myself at the MIT Media Lab, as a graduate student in a [https://www.media.mit.edu/groups/lifelong-kindergarten/overview/ research group] that, among other things, continue the work Seymour and his colleagues had started many years ago.<br />
<br />
After getting a PhD from MIT, I was a postdoctoral fellow at the University of Washington's eScience Institute and was hosted by the Department of Communication. Currently, I am an assistant professor at the School of Information and Library Science, UNC Chapel Hill, where I study, design, and build pathways that engage young people in learning with data. I also do a considerable amount of learning with data myself, where I use (mostly) quantitative methods to study how children and youth learn in large-scale informal online communities.<br />
<br />
You can find more about my work on my [https://unmad.in homepage].<br />
<br />
</div><br />
<br />
<br />
<div style="clear:both;"><br />
<br />
=== Jeremy Foote (Purdue University) ===<br />
<br />
[[File:Jeremy.jpg|thumb|200px|Jeremy and his family on a very flat Midwest hike]]<br />
<br />
I grew up in Nevada, did my undergrad (in English!) at BYU in Utah, and then worked as a practitioner of online collaboration. I was the product manager for a small [https://www.lingotek.com/ collaborative translation company] in Utah. I decided that I cared a lot more about understanding collaboration than designing software, and I came back to school. I did a Master's degree at Purdue, studying with [https://www.cla.purdue.edu/communication/directory/?p=Seungyoon_Lee Seungyoon Lee], and then worked on a PhD at Northwestern, as a member of CDSC. I'm now back at Purdue in the [https://www.cla.purdue.edu/academic/communication/ Brian Lamb School of Communication], this time as a faculty member.<br />
<br />
Most of my current research is focused around understanding how people decide where to participate in online communities--why people start new communities, how community membership influences future behavior, and how communication structures relate to community outcomes. I'm particularly interested in how these decisions scale up into the social construction of understanding, knowledge, and opinion. More about my research is at my [http://www.jeremydfoote.com academic homepage].<br />
<br />
Much of my spare time is spent with my family (my wife and I have 5 kids!) or with my [http://www.mormon.org church community]. I love the Midwest but really miss hiking and skiing in the mountains and try to do both as much as possible.<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Benjamin Mako Hill (University of Washington) ===<br />
<br />
[[File:Mako-Meitu-201701.jpg|thumb|200px|Unedited picture of Mako in Berlin (2016).]]<br />
<br />
After contributing peer production communities in various ways since I was a teenager, I began to realize (the hard way) that peer production rarely works and that getting it to work remained much more art than science. After being talked into the idea that academia was the right place to fix this by [https://evhippel.mit.edu/ Eric von Hippel], I've devoted the last decade of my life to trying to contribute to an emerging science of Internet-based collaborative production. Since starting as an academic, I have published tens of thousand of [https://www.wikidata.org/wiki/Q103184 articles]—nearly all of them are [https://www.wikidata.org/wiki/Lexeme:L2768 the].<br />
<br />
In the more boring accounting (which I've copied and pasted from elsewhere): I am an Assistant Professor in the University of Washington Department of Communication and an Adjunct Assistant Professor in the Department of Human-Centered Design & Engineering and Computer Science & Engineering. At UW, I am also Affiliate Faculty in the Center for Statistics and the Social Sciences, the eScience Institute, and the "Design Use Build" (DUB) group that supports research on on human computer interaction. I am also a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University and an affiliate of the Institute of Quantitative Social Science at Harvard.<br />
<br />
Much more information is on [https://mako.cc/academic/ my academic homepage]. If you need to find me, I have put [https://mako.cc/contact/ more detailed contact information online] than I probably should.<br />
<br />
</div><br />
<br />
<br />
<div style="clear:both;"><br />
=== Aaron Shaw (Northwestern University) ===<br />
<br />
[[File:Shaw-2017.jpg|thumb|250px|Airbrushed, filtered, and meitu'd purikura of Aaron from 2017]] <br />
<br />
Hello! I'm Aaron. I grew up around New York and went to school for a while in northern California. Along the way, I got involved in participatory movements and projects of various kinds. At first, these were more traditional movements advancing egalitarian social agendas. Over time, I got involved in peer production projects, online communities, and other sorts of open collaboration online.<br />
<br />
These days, I am an Associate Professor in the Department of Communication Studies at Northwestern where I direct the [http://mts.northwestern.edu Media, Technology & Society (MTS) Program]. At Northwestern, I am also part of the [http://tsb.northwestern.edu Technology & Social Behavior Program], courtesy appointed in the Sociology Department, a faculty associate of the Institute for Policy Research, and the SONIC lab. Elsewhere, I am a faculty associate of the [http://cyber.law.harvard.edu Berkman Klein Center for Internet and Society] at Harvard University. A good place to find more information is [http://aaronshaw.org my website]. If you'd like to get in touch, please [mailto:aaronshaw@northwestern.edu send me an email] (and don't be shy about re-sending if I don't reply).<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Sneha Narayan (Carleton College) ===<br />
<br />
[[File:Snehaphoto.jpg|thumb|200px|Sneha hanging out by Lake Michigan]]<br />
I'm an Assistant Professor of Computer Science at [https://www.carleton.edu/ Carleton College]. Before that, I did my PhD in the Technology and Social Behavior program at Northwestern University, advised by Aaron Shaw (whose bio you can find by scrolling up a couple of sections). I grew up in Bangalore, India, studied mathematics at Oberlin College, and received a masters degree in Sociology and Social Anthropology from Central European University, Budapest.<br />
<br />
I've spent many years living in housing co-ops, and volunteering on the boards of co-operative organizations. My involvement in the co-op movement led to my interest in learning more (and producing knowledge) about participatory, volunteer-run endeavors such as peer production projects and online collaboration communities. My research focuses on understanding how newcomers join and become embedded in volunteer-run organizations, and what kinds of technological interventions might affect their continued participation in these communities. For (slightly) more information about all this, you can check out my [http://www.snehanarayan.com/ homepage].<br />
</div><br />
<br />
== Graduate Students ==<br />
<br />
<div style="clear:both;"><br />
=== Kaylea Champion (University of Washington) ===<br />
<br />
I am investigating how society creates (or fails to create) humane online environments -- those which enable connection, exploration, and collaboration. <br />
<br />
After growing up in Oregon, I spent two decades in Chicago, primarily at the University of Chicago as an academic technology director and consultant. I have a BA in Near Eastern Languages and Civilizations and an MS in Computer Science, both from the University of Chicago. I also hold an MA in Critical & Creative Thinking from the University of Massachusetts, Boston.<br />
<br />
My husband, three kids, and I live in Shoreline, WA, which seems to be Seattle's version of Evanston. I'm particularly fond of visiting museums, tromping in the woods, cooking for crowds, smashing goblins, and scribbling fiction.<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Regina Cheng (University of Washington) ===<br />
<br />
[[File:Regina hime.JPG|thumb|200px|Regina with her new feline best friend, [https://www.instagram.com/hime_theprincesscat/ Hime].]]<br />
<br />
I'm a PhD candidate in the Human-Centered Design and Engineering department at University of Washington, co-advised by Mako Hill and Jennifer Turns. I describe my research goal as to understand and support collaborative informal learning in online communities of creators. I am interested in studying how different types of collaborative activities (e.g. feedback exchange, collaborative sense-making) lead to different learning outcomes, and designing for more effective collaboration to facilitate learning. Right now I am especially interested in the domain of data science learning among non-technical population.<br />
<br />
Outside research, I like cats, drawing (mostly fanart these days), reading, cooking, hiking, hapkidoing, and preaching about my mother tongue, [https://en.wikipedia.org/wiki/Hangzhou_dialect Hangzhou dialect] <br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Carl Colglazier (Northwestern University) ===<br />
<br />
I'm a first-year Ph.D. stduent in [https://tsb.northwestern.edu/ Technology and Social Behavior] at Northwestern. I'm interested in networks, information, sociotechnical systems, and how they are intertwined. My website is [https://carlcolglazier.com/ here].<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Stefania Druga (University of Washington) ===<br />
<br />
[[File:Stef2019.jpg|thumb|200px|Stef in Summer of 2019, [//anoxic.me/huli Fancy].]]<br />
<br />
I'm a first-year Ph.D. student in the Information School at the University of Washington, co-advised by Jason Yip and Alexis Hiniker. I am the co-founder of Cognimates and HacKIDemia. My research focuses on how children interact with and make sense of the growing collection of “smart” inter-connected playthings in the world around them together with their parents. I am exploring how families, as they play with these new smart assistants and applications, develop new ways of thinking about intelligence, emotion, and social interaction. Based on these studies, I am designing new tools and activities to introduce families to machine learning and data science in a playful way. <br />
<br />
Outside research, I like climbing, dogs, reading, dancing and learning new languages. <br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Floor Fiers (Northwestern University) ===<br />
<br />
[[File:Small.FloorFiers.jpg|thumb|200px|Floor on her way back to the US in the midst of the pandemic]]<br />
<br />
Hi there! I am is a PhD student in the [https://communication.northwestern.edu/programs/phd_media_technology_society Media, Technology and Society program ] at Northwestern Uni. Academically speaking, I am interested in the field of digital inequality, particularly as it relates to online labor markets and the gig economy. Outside academia, I love (cold water) swimming and rollerblading, and I spend way too much time on organizing two music & theater festivals in the Netherlands.<br />
<br />
Originally from the Netherlands, I first came to the US attend the [https://www.uwc.org/ United World College ] (Montezuma, NM), after which I pursued a BA in Sociology from [https://www.stlawu.edu/ St. Lawrence University ] (Canton, NY). During the pandemic, I worked remotely from the University of Zurich's [https://www.ikmz.uzh.ch/en/research/divisions/internet-use-and-society/team.html Internet & Society division]. For more background, see [https://www.floorfiers.com my website].<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Emilia Gan (University of Washington) ===<br />
<br />
[[File:EGan.jpg|200px|thumb|Emilia G.]]<br />
<br />
I'm a fifth-year PhD student in the [https://www.cs.washington.edu/ Paul G. Allen School of Computer Science & Engineering] at the University of Washington (Seattle). Currently, I am working on analyzing data from the [https://scratch.mit.edu/ Scratch programming platform]. Previous [https://mako.cc/academic/gan_hill_dasgupta-gender_feedback_sharing-CSCW18.pdf research] on Scratch involved looking at gender differences in the way kids use the Scratch Online Community. <br />
<br />
Before starting graduate school in CS, I earned an MS ([https://globalhealth.washington.edu/education-training/phd-pathobiology Pathobiology]) from UW. I initially started learning how to program with the thought of using these skills for analyzing large biological data sets, but I eventually realized everything I was doing was pointing me away from biology and towards computer science. <br />
<br />
Before starting graduate school at UW, I homeschooled with my kids for over a decade, and before that I earned an MD from the [https://www.umassmed.edu/ University of Massachusetts Medical School] and a BS in Materials Science and Engineering from [https://dmse.mit.edu/ MIT].<br />
<br />
[https://emilia.cloud/ Personal Website]<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Wm Salt Hale (University of Washington) ===<br />
[[File:Salt_Xmas.jpg|thumb|200px|Salt shedding the holiday cheer (2016)]]<br />
<br />
Growing up in Seattle during the early 90s offered many technological opportunities, most of which I took advantage of. As an avid GNU/Linux user for over 20 years, I have been exposed to numerous technology orientated communities on various levels.<br />
<br />
During high school I entered the Running Start program, completing an Associate's degree in Computer Science from South Seattle College. After which I transfered to the University of Washington, pursuing the same major. It was not a fit, instead I developed a number of businesses, traveled, and spoke at various conferences, conventions, events, faires, and festivals.<br />
<br />
Upon returning to the University of Washington to complete my Batchelor's degree in Communication, I connected with [[Mako]] and was shown a world of academia previously unimagined. After another year of traveling, I have decided to return to the UW Department of Comm yet again and am just beginning to delve deeper into the intersection of Technology and Society in the MA/PhD program.<br />
<br />
I am extremely interested in: Free/Libre/Open Source Software (FLOSS) and Culture; Hackers, Makers, and Breakers; and Computer-Mediated Communication using real-time synchronous systems. Along with numerous hobbies including: urban hiking (walking), dancing (folk, east coast swing, lindy, blues), windsports (windsurfing, kiteboarding, sailing), bicycling, boffering, cooking, driving, event planning, gaming, programming, public speaking, reading, robotics, skiing, and travel.<br />
<br />
Up to date information and links to various profiles around the web can be found on ''my'' IndieWeb presence, [http://www.altsalt.net/ The Alt World of Salt].<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Sohyeon Hwang (Northwestern University) ===<br />
<br />
[[File:Sohyeonhwang.jpg|thumb|200px|Sohyeon and her dog-child, Tubby.]]<br />
<br />
I'm a third-year PhD student in the Media, Technology, and Society program at Northwestern University, advised by Aaron Shaw. My research interests broadly circle around online governance, mostly around ideas of heterogeneity, scale, and polycentric + decentralized models. I focus on the complexities arising in governance, such as how online groups diversely interpret, innovate beyond, subvert, and co-opt socio-technical affordances to manage themselves.<br />
<br />
I am pretty methods-agnostic, doing both computational/quantitative approaches as well as qualitative work here and there. You can find more information at my [https://www.sohyeonhwang.com site].<br />
<br />
Outside of work, I like to eat french fries and take (blurry) film photos. <br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Charles Kiene (University of Washington) ===<br />
<br />
[[File:Ch2.jpg|thumb|200px|Charlie.]]<br />
<br />
I am a MA/PhD student in the Communication Department at the University of Washington. <br />
<br />
From Reddit subcommunities to Discord servers to ''World of Warcraft'' guilds, my interests lie in the strategies of governance and organizing in online communities, especially when they experience massive change, like user influxes, platform shifts, new technologies, mergers, or an exodus of community members. I'm also interested in the technologies online communities use to organize, such as bots for automating redundant aspects of moderation work, communication applications and the role of voice calls, and other forms of end-user programming that allow online groups to govern and defend their digital boundaries. My future work seeks to evaluate how online communities manage and make sense of internal conflicts and disputes over self-governance, as well as conflicts with other communities and how social boundaries are maintained or change from interactions with mutual or competitive outside groups.<br />
<br />
I use a variety of research strategies including both qualitative and quantitative methods, but most of my past work has involved in-depth interviewing and ethnography.<br />
<br />
Details at [[User:Healspersecond]]<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Jim Maddock (Northwestern) ===<br />
[[File:maddock_cheese_sandwhich.jpg|thumb|200px|Jim eats a cheese sandwich while riding a cow in the Swiss Alps]]<br />
<br />
I'm a PhD Student in the Computer Science and Communications departments at Northwestern University. I currently work with Darren Gergle and Aaron Shaw, studying collaboration and coordination dynamics within social computing systems, such as Wikipedia and Zooniverse. Throughout my tenure as a graduate student I've also interned at MSR India, Google, and Mozilla.<br />
<br />
<br />
I first became interested in HCI during my undergraduate degree at the University of Washington. I earned a degree in Human Centered Design and Engineering, where I worked with Professor Kate Starbird to understand rumoring behavior in crisis situations. I also studied Medieval European history.<br />
<br />
When I'm not working on research, I'm probably riding my bike or planning a backpacking trip. You can find more about my research at my [http://jmaddock.net/ website].<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Nathan TeBlunthuis (University of Washington) ===<br />
{{User:groceryheist/bio}}<br />
<br />
<br />
=== Nick Vincent (Northwestern University) ===<br />
My research focuses on studying the relationships between human-generated data and computing technologies to mitigate negative impacts of these technologies. I am especially interested in research that (1) makes people aware of the value of their data and (2) helps people leverage the value of their data. My work relates to concepts such as "data dignity", "data as labor", "data leverage", and "data dividends".<br />
<br />
Here's my [https://www.nickmvincent.com website]!<br />
<br />
== Undergraduate Students ==<br />
<br />
== Affiliate Researchers ==<br />
<div style="clear:both;"><br />
=== Mad Price Ball (Open Humans Foundation) ===<br />
[[File:Mad-portrait-photo-201910.jpg|200px|thumb|Mad eagerly tearing apart [https://twitter.com/madprime/status/979833858039271425 another terrible blockchain idea].]]<br />
<br />
I am Executive Director of Open Humans Foundation and co-founder of [https://www.openhumans.org Open Humans]. My research involvement is more "meta" these days: I help others do it. With Open Humans, we try to enable a new approach for research in health and human subjects research, focusing on personal data. Our work is generally "open" and strives to enable peer production, enabling individuals to create and share tools for getting personal data, analyzing it, and potentially contributing it to aggregate projects (from patient groups to citizen scientists, as well as traditional academic studies). I'm also a Shuttleworth Foundation Fellow (alum) and a member of the BoD of MyData Global.<br />
<br />
Open Humans was inspired by my dual histories in genomics research and free/open culture. My PhD was in biotech and postdoc work involved running George Church's Personal Genome Project, which invited people to donate genome & health data to science by making it public – where I learned a lot about personal data and human subjects research. I'm also familiar with free/open culture folks for well over a decade, contributing here and there; one of my favorite past projects was helping create an offline copy of Wikipedia for OLPC distributed in Peru & Uruguay (my role was creating the article list, mostly based on traffic & connectivity data).<br />
<br />
I live in San Diego, but online you can find me on [http://twitter.com/madprime @madprime on Twitter], in the [http://slackin.openhumans.org Open Humans slack group], and sometimes IRC (madprime) – or reach me by email (mad) at openhumans.org.<br />
</div><br />
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<div style="clear:both;"><br />
=== Tilman Bayer ===<br />
[[File:Tilman at Internet Archive 2018.jpg|thumb|170px|Tilman sitting in the [https://en.wikipedia.org/wiki/Internet_Archive Internet Archive's] pews, piously contemplating the world's knowledge]]<br />
I am a longtime Wikipedia contributor (as [[:w:User:HaeB|User:HaeB]]) and editor of the [https://meta.wikimedia.org/wiki/Research:Newsletter Wikimedia Research Newsletter], a monthly publication surveying and reviewing recent academic research about Wikipedia and other Wikimedia projects, which I co-founded in 2011 with my then-colleague Dario Taraborelli at the Wikimedia Foundation. I am also one of the two maintainers of the associated [https://twitter.com/wikiresearch @WikiResearch] Twitter feed. For the past several years, I have joined Mako, Aaron and others in presenting an annual [https://wikimania2018.wikimedia.org/wiki/Program/State_of_Wikimedia_Research_2017-2018 "State of Wikimedia Research"] overview at the Wikimania community conference, where I have also presented on other data and research topics such as the question [https://upload.wikimedia.org/wikipedia/commons/e/e1/Which_parts_of_a_%28Wikipedia%29_article_are_actually_being_read_%28Wikimania_2018%29.pdf which parts of a Wikipedia article people actually read]. <br />
<br />
My work as a data analyst on the Wikimedia Foundation's [https://www.mediawiki.org/w/index.php?title=Product_Analytics&oldid=3173327 Product Analytics team] included controlled experiments and exploratory data analysis to support the development of new software features for Wikipedia readers and contributors, and the analysis of core readership metrics like pageviews. With the Foundation's web team, I drove the implementation of a new metric designed to better understand reader engagement, based on an instrumentation of time spent on page (dwell time). This became the subject of a [https://meta.wikimedia.org/wiki/Research:Reading_time research project] with Nate TeBlunthuis and my then-colleague Olga Vasileva, with findings e.g. about differences in reading behavior between users in the Global South and the Global North.<br />
<br />
My academic background is in pure mathematics, with degrees from the University of Cambridge and the University of Bonn. I am based in San Francisco and can be reached via Gmail ("HaeBwiki") and as "HaeB" on IRC (Freenode).<br />
</div><br />
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=== Bastian Greshake Tzovaras (Center for Research & Interdisciplinarity, Université Paris Descartes) ===<br />
[[File:BastianGreshakeTzovaras.jpg|200px|thumb|Bastian, being so old-timey that his beard has grown.]]<br />
<br />
Despite having an academic background in biology/bioinformatics, I've been active in peer-produced citizen science since around 2011. I'm one of the co-founders of the crowdsourced, open data repository openSNP ([https://opensnp.org]), which collects personal genomics data sets from users of Direct-To-Consumer genetic testing companies to put them into the public domain. Since 2017 I'm also the Director of Research for Open Humans (https://www.openhumans.org), an ecosystem for participatory citizen science that aims to allow people to analyze and learn from their own personal data as well as given members the opportunity to share their data with (citizen science) research projects. Among other things we have piloted a JupyterHub-based approach to give people their own virtual machines that allow them to write, run and share data analysis notebooks without having to share any personal information (see [https://exploratory.openhumans.org]).<br />
<br />
Since 2019 I'm a research fellow at the Center for Research & Interdisciplinarity in Paris ([https://cri-paris.org/]), where I will study how the ideas of peer-production can be translated to facilitate co-created citizen science projects in which participants are fully involved in all stages of research, from start to finish. Lately a lot of focus there has been on how we can scale up the individualistic quantified self experiments people do to larger cohorts. I also teach students the basics of citizen science and self-tracking. <br />
<br />
Last but not least I'm involved in community building and mentoring in bioinformatics and for open projects in general: I'm a board member of the Open Bioinformatics Foundation ([https://www.open-bio.org/]), have mentored for Mozilla's Open Leadership Cohorts, Outreachy & Google Summer of Code.<br />
</div><br />
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=== Andrés Monroy-Hernández (Snap Research) ===<br />
[[File:andresmh.jpg|thumb|180px|🚀]]<br />
<br />
I'm a researcher at [https://www.snap.com/ Snap Inc.] and an affiliate faculty at the University of Washington. My work focuses on the study and design of social computing systems. Some areas I've worked on are crowdsourcing, peer production, remixing, civic tech, urban computing, and online learning.<br />
<br />
Some projects I've worked on lately include [http://calendar.help Calendar.help], a hybrid intelligence scheduling assistant partly powered by crowds; Narcotweets, a research project studying how people use social media during war and political uprisings; and the [http://scratch.mit.edu Scratch Online Community], a website where millions of young people learn to program and remix games and animations. <br />
<br />
You can find me at [http://twitter.com/andresmh @andresmh] or at [http://andresmh.com/ www.andresmh.com].<br />
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=== Jonathan T. Morgan (Crowdstrike) ===<br />
<br />
[[File:Jtm_profile_pic.jpg|thumb|200px|Jonathan in his preferred horizontal orientation.]]<br />
<br />
I'm a UX researcher at CrowdStrike and an affiliate faculty member in the UW department of Human Centered Design & Engineering. Most of my research involves understanding the sociotechnical mechanisms through which people who use complex collaborative software systems coordinate their work across time and space. You can find out more about me and my work [https://meta.wikimedia.org/wiki/User:Jmorgan_(WMF) here] and [http://jtmorgan.net/ here].<br />
<br />
I am a founding mentor for the [[Community_Data_Science_Workshops|Community Data Science Workshops]], and I also develop and teach UW courses on related topics, like [[Human_Centered_Data_Science|Human Centered Data Science]]. <br />
<br />
I am a voracious and omnivorous reader, and a passionately amateurish musician. When I'm away from the keyboard, you can usually find me exploring the beaches and forests of Puget Sound with my wife and my dog, [[w:Ozymandias|Ozymandias]].<br />
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<br />
=== Morten Warncke-Wang (Wikimedia Foundation) ===<br />
[[File:Warncke-Wang, Morten - Dec 2017.jpg|200px|thumb|Morten prior to growing a scientifically sound beard.]]<br />
<br />
I've been participating in online and peer production communities for over 20 years, and recently (December 2016) got a PhD studying them. My research focus has been on content quality in peer production communities like Wikipedia and OpenStreetMap: what is high quality content, how is it created, can we build tools to judge it, and is it produced where there is demand for it? In addition to research publications, this work has also led to a Python library for predicting Wikipedia article quality ([https://github.com/wiki-ai/articlequality articlequality]) that is publicly available on Wikipedia through the [https://www.mediawiki.org/wiki/ORES ORES API]. I am also a Research Fellow with the [https://research.wikimedia.org Wikimedia Foundation's Research group].<br />
<br />
Another one of my interests is using recommender systems to help contributors find work to do. In Wikipedia this manifests in my maintenance of [https://en.wikipedia.org/wiki/User:SuggestBot SuggestBot]. The bot can recommend articles to work on based on a user's edit history, or they can supply articles or categories they want to base the suggestions on. SuggestBot is currently available in seven languages.<br />
<br />
I've participated as a mentor and instructor in some of the Community Data Science Workshops. Apart from these things, I also like reading (both books and magazines), watching movies, playing [https://en.wikipedia.org/wiki/Squash_(sport) squash], and attempting to make music.<br />
</div><br />
<br />
== Friends and Community Members ==<br />
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=== Alice Ferrazzi ===<br />
[[File:107572.jpeg|thumb|180px|Alice Ferrazzi]]<br />
<br />
I'm a researcher and community member who collaborates and helps the CDSC in various ways. My research work focuses on the study of operating systems kernel where I work mostly in live patch systems. One of my projects is [https://wiki.gentoo.org/wiki/Elivepatch Elivepatch].<br />
<br />
I'm the Gentoo Kernel Project Leader, mainly focused in kernel release automatization. You can find me at [http://twitter.com/aliceinwire @aliceinwire] or at [http://aliceinwire.net/ www.aliceinwire.net]. My Gentoo profile is at [https://wiki.gentoo.org/wiki/User:Aliceinwire User:Aliceinwire]. I am on IRC (OFTC) as alicef_.<br />
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=== Samuel Klein ===<br />
[[File:Orienteering tunnels.jpg|thumb|180px|right|Samuel Klein on the right (with a surprise Aaron shaw on the left).]]<br />
I'm a wikimedian, urban spelunker, and founding member of MIT's [http://kfg.mit.edu Knowledge Futures Group]. One of my projects is the Innovation Information Initiative, a data collab for patent and prior art datasets. <br />
<br />
Occasionally in IRC as _sj_. [[User:Sj|Sj]] ([[User talk:Sj|talk]]) 15:54, 17 August 2019 (EDT)<br />
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<div style="clear:both;"><br />
=== Abel Serrano Juste ===<br />
<br />
[[File:Abeserra.jpeg|thumb|200px|Abel Serrano Juste]]<br />
<br />
Interested in how technology can serve communities of people for good. I see free software as an implicit requirement for this.<br />
<br />
I've been working for two years in the University Complutense of Madrid doing data analysis on collaborative online communities (CBPP), more specifically, on wikis. You can see my publications and more info about me in [https://akronix.es/ my homepage].<br />
<br />
I hold a Bachelor's Degree in Computer Science by the UCM and currently I'm enrolled in a Master's Degree of Data Science by the UOC.<br />
<br />
Also, I like bikes, nature, hiking, traveling, and sharing my life with beautiful people.<br />
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<div style="clear:both;"><br />
=== Sejal Khatri ===<br />
<br />
[[File:Sejal_Khatri.jpg|thumb|200px|Sejal]]<br />
<br />
I recently graduated from the Information School at the University of Washington, Seattle. My specialization was in User Experience Research and Design in the Information Management program at iSchool. I did my undergrad in Computer Science at SPPU in Pune, India, and then interned for Wikimedia Foundation as a UX Engineer. My current research interests revolve around online communities, peer-production, and open source software. When I'm not working, I participate in design jams and hackathons where I get the opportunity to turn curiosities and concerns into design interventions. <br />
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<br />
=== Kat Walsh ===<br />
<br />
[[File:katwalsh_purple.jpg|thumb|200px|Kat Walsh, with freshly purpled hair]]<br />
<br />
I'm a lawyer working in copyright, speech, policy, and nonprofit leadership around various Free and Open projects and communities, currently working with individual clients including Creative Commons. I got into open communities through volunteering for Wikimedia, first as an editor, then in community dispute resolution, and then as a board member for several years. I've also been on the board of the Free Software Foundation. <br />
<br />
I enjoy collaborating with academic researchers on work in peer production communities and their copyright/"intellectual property", dispute resolution, governance, and legal policy issues. I am located just north of San Francisco, where I enjoy playing my bassoon, viola, and occasionally some other things in a delightfully weird collection of musical groups, and lifting heavy objects for no particular reason.<br />
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<br />
== Alumni ==<br />
<br />
'''NOBODY HAS EVER LEFT'''</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=People&diff=223144People2021-10-08T16:52:16Z<p>Nickmvincent: /* Graduate Students */ Adding Nick Vincent (myself)</p>
<hr />
<div>We're an interdisciplinary group at Northwestern University and the University of Washington. Faculty, postdocs, graduate students, affiliates, and alumni are listed below (in alphabetical order within each section) except when we've failed at alphabetizing.<br />
<br />
You can see pictures of all together over at our [[group photos]] page. Pictures of us individually are here.<br />
<br />
We are a friendly group and we welcome new affiliates! If you have been working with us for a while, perhaps it's time to add yourself to this page as an affiliate. Feel free to add yourself (use the Edit tab), and please include a sentence on HOW you are related to the group (and a fun picture of yourself!).<br />
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== Faculty ==<br />
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=== Sayamindu Dasgupta (University of North Carolina at Chapel Hill) ===<br />
<br />
[[File:Sayamindu.jpg|thumb|200px|Sayamindu, mildly perturbed.]]<br />
<br />
I grew up in the city of Kolkata, India. At some point in school I wanted to study Physics, but then soon after, I came across computers, which messed up my plans considerably. Roughly 9 years after I had my first encounter with a computer, I read Seymour Papert’s ''Mindstorms: Children, Computers and Powerful Ideas'', and a year after that, I found myself at the MIT Media Lab, as a graduate student in a [https://www.media.mit.edu/groups/lifelong-kindergarten/overview/ research group] that, among other things, continue the work Seymour and his colleagues had started many years ago.<br />
<br />
After getting a PhD from MIT, I was a postdoctoral fellow at the University of Washington's eScience Institute and was hosted by the Department of Communication. Currently, I am an assistant professor at the School of Information and Library Science, UNC Chapel Hill, where I study, design, and build pathways that engage young people in learning with data. I also do a considerable amount of learning with data myself, where I use (mostly) quantitative methods to study how children and youth learn in large-scale informal online communities.<br />
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You can find more about my work on my [https://unmad.in homepage].<br />
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<br />
=== Jeremy Foote (Purdue University) ===<br />
<br />
[[File:Jeremy.jpg|thumb|200px|Jeremy and his family on a very flat Midwest hike]]<br />
<br />
I grew up in Nevada, did my undergrad (in English!) at BYU in Utah, and then worked as a practitioner of online collaboration. I was the product manager for a small [https://www.lingotek.com/ collaborative translation company] in Utah. I decided that I cared a lot more about understanding collaboration than designing software, and I came back to school. I did a Master's degree at Purdue, studying with [https://www.cla.purdue.edu/communication/directory/?p=Seungyoon_Lee Seungyoon Lee], and then worked on a PhD at Northwestern, as a member of CDSC. I'm now back at Purdue in the [https://www.cla.purdue.edu/academic/communication/ Brian Lamb School of Communication], this time as a faculty member.<br />
<br />
Most of my current research is focused around understanding how people decide where to participate in online communities--why people start new communities, how community membership influences future behavior, and how communication structures relate to community outcomes. I'm particularly interested in how these decisions scale up into the social construction of understanding, knowledge, and opinion. More about my research is at my [http://www.jeremydfoote.com academic homepage].<br />
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Much of my spare time is spent with my family (my wife and I have 5 kids!) or with my [http://www.mormon.org church community]. I love the Midwest but really miss hiking and skiing in the mountains and try to do both as much as possible.<br />
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=== Benjamin Mako Hill (University of Washington) ===<br />
<br />
[[File:Mako-Meitu-201701.jpg|thumb|200px|Unedited picture of Mako in Berlin (2016).]]<br />
<br />
After contributing peer production communities in various ways since I was a teenager, I began to realize (the hard way) that peer production rarely works and that getting it to work remained much more art than science. After being talked into the idea that academia was the right place to fix this by [https://evhippel.mit.edu/ Eric von Hippel], I've devoted the last decade of my life to trying to contribute to an emerging science of Internet-based collaborative production. Since starting as an academic, I have published tens of thousand of [https://www.wikidata.org/wiki/Q103184 articles]—nearly all of them are [https://www.wikidata.org/wiki/Lexeme:L2768 the].<br />
<br />
In the more boring accounting (which I've copied and pasted from elsewhere): I am an Assistant Professor in the University of Washington Department of Communication and an Adjunct Assistant Professor in the Department of Human-Centered Design & Engineering and Computer Science & Engineering. At UW, I am also Affiliate Faculty in the Center for Statistics and the Social Sciences, the eScience Institute, and the "Design Use Build" (DUB) group that supports research on on human computer interaction. I am also a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University and an affiliate of the Institute of Quantitative Social Science at Harvard.<br />
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Much more information is on [https://mako.cc/academic/ my academic homepage]. If you need to find me, I have put [https://mako.cc/contact/ more detailed contact information online] than I probably should.<br />
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=== Aaron Shaw (Northwestern University) ===<br />
<br />
[[File:Shaw-2017.jpg|thumb|250px|Airbrushed, filtered, and meitu'd purikura of Aaron from 2017]] <br />
<br />
Hello! I'm Aaron. I grew up around New York and went to school for a while in northern California. Along the way, I got involved in participatory movements and projects of various kinds. At first, these were more traditional movements advancing egalitarian social agendas. Over time, I got involved in peer production projects, online communities, and other sorts of open collaboration online.<br />
<br />
These days, I am an Associate Professor in the Department of Communication Studies at Northwestern where I direct the [http://mts.northwestern.edu Media, Technology & Society (MTS) Program]. At Northwestern, I am also part of the [http://tsb.northwestern.edu Technology & Social Behavior Program], courtesy appointed in the Sociology Department, a faculty associate of the Institute for Policy Research, and the SONIC lab. Elsewhere, I am a faculty associate of the [http://cyber.law.harvard.edu Berkman Klein Center for Internet and Society] at Harvard University. A good place to find more information is [http://aaronshaw.org my website]. If you'd like to get in touch, please [mailto:aaronshaw@northwestern.edu send me an email] (and don't be shy about re-sending if I don't reply).<br />
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=== Sneha Narayan (Carleton College) ===<br />
<br />
[[File:Snehaphoto.jpg|thumb|200px|Sneha hanging out by Lake Michigan]]<br />
I'm an Assistant Professor of Computer Science at [https://www.carleton.edu/ Carleton College]. Before that, I did my PhD in the Technology and Social Behavior program at Northwestern University, advised by Aaron Shaw (whose bio you can find by scrolling up a couple of sections). I grew up in Bangalore, India, studied mathematics at Oberlin College, and received a masters degree in Sociology and Social Anthropology from Central European University, Budapest.<br />
<br />
I've spent many years living in housing co-ops, and volunteering on the boards of co-operative organizations. My involvement in the co-op movement led to my interest in learning more (and producing knowledge) about participatory, volunteer-run endeavors such as peer production projects and online collaboration communities. My research focuses on understanding how newcomers join and become embedded in volunteer-run organizations, and what kinds of technological interventions might affect their continued participation in these communities. For (slightly) more information about all this, you can check out my [http://www.snehanarayan.com/ homepage].<br />
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<br />
== Graduate Students ==<br />
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=== Kaylea Champion (University of Washington) ===<br />
<br />
I am investigating how society creates (or fails to create) humane online environments -- those which enable connection, exploration, and collaboration. <br />
<br />
After growing up in Oregon, I spent two decades in Chicago, primarily at the University of Chicago as an academic technology director and consultant. I have a BA in Near Eastern Languages and Civilizations and an MS in Computer Science, both from the University of Chicago. I also hold an MA in Critical & Creative Thinking from the University of Massachusetts, Boston.<br />
<br />
My husband, three kids, and I live in Shoreline, WA, which seems to be Seattle's version of Evanston. I'm particularly fond of visiting museums, tromping in the woods, cooking for crowds, smashing goblins, and scribbling fiction.<br />
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=== Regina Cheng (University of Washington) ===<br />
<br />
[[File:Regina hime.JPG|thumb|200px|Regina with her new feline best friend, [https://www.instagram.com/hime_theprincesscat/ Hime].]]<br />
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I'm a PhD candidate in the Human-Centered Design and Engineering department at University of Washington, co-advised by Mako Hill and Jennifer Turns. I describe my research goal as to understand and support collaborative informal learning in online communities of creators. I am interested in studying how different types of collaborative activities (e.g. feedback exchange, collaborative sense-making) lead to different learning outcomes, and designing for more effective collaboration to facilitate learning. Right now I am especially interested in the domain of data science learning among non-technical population.<br />
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Outside research, I like cats, drawing (mostly fanart these days), reading, cooking, hiking, hapkidoing, and preaching about my mother tongue, [https://en.wikipedia.org/wiki/Hangzhou_dialect Hangzhou dialect] <br />
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=== Carl Colglazier (Northwestern University) ===<br />
<br />
I'm a first-year Ph.D. stduent in [https://tsb.northwestern.edu/ Technology and Social Behavior] at Northwestern. I'm interested in networks, information, sociotechnical systems, and how they are intertwined. My website is [https://carlcolglazier.com/ here].<br />
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=== Stefania Druga (University of Washington) ===<br />
<br />
[[File:Stef2019.jpg|thumb|200px|Stef in Summer of 2019, [//anoxic.me/huli Fancy].]]<br />
<br />
I'm a first-year Ph.D. student in the Information School at the University of Washington, co-advised by Jason Yip and Alexis Hiniker. I am the co-founder of Cognimates and HacKIDemia. My research focuses on how children interact with and make sense of the growing collection of “smart” inter-connected playthings in the world around them together with their parents. I am exploring how families, as they play with these new smart assistants and applications, develop new ways of thinking about intelligence, emotion, and social interaction. Based on these studies, I am designing new tools and activities to introduce families to machine learning and data science in a playful way. <br />
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Outside research, I like climbing, dogs, reading, dancing and learning new languages. <br />
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=== Floor Fiers (Northwestern University) ===<br />
<br />
[[File:Small.FloorFiers.jpg|thumb|200px|Floor on her way back to the US in the midst of the pandemic]]<br />
<br />
Hi there! I am is a PhD student in the [https://communication.northwestern.edu/programs/phd_media_technology_society Media, Technology and Society program ] at Northwestern Uni. Academically speaking, I am interested in the field of digital inequality, particularly as it relates to online labor markets and the gig economy. Outside academia, I love (cold water) swimming and rollerblading, and I spend way too much time on organizing two music & theater festivals in the Netherlands.<br />
<br />
Originally from the Netherlands, I first came to the US attend the [https://www.uwc.org/ United World College ] (Montezuma, NM), after which I pursued a BA in Sociology from [https://www.stlawu.edu/ St. Lawrence University ] (Canton, NY). During the pandemic, I worked remotely from the University of Zurich's [https://www.ikmz.uzh.ch/en/research/divisions/internet-use-and-society/team.html Internet & Society division]. For more background, see [https://www.floorfiers.com my website].<br />
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=== Emilia Gan (University of Washington) ===<br />
<br />
[[File:EGan.jpg|200px|thumb|Emilia G.]]<br />
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I'm a fifth-year PhD student in the [https://www.cs.washington.edu/ Paul G. Allen School of Computer Science & Engineering] at the University of Washington (Seattle). Currently, I am working on analyzing data from the [https://scratch.mit.edu/ Scratch programming platform]. Previous [https://mako.cc/academic/gan_hill_dasgupta-gender_feedback_sharing-CSCW18.pdf research] on Scratch involved looking at gender differences in the way kids use the Scratch Online Community. <br />
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Before starting graduate school in CS, I earned an MS ([https://globalhealth.washington.edu/education-training/phd-pathobiology Pathobiology]) from UW. I initially started learning how to program with the thought of using these skills for analyzing large biological data sets, but I eventually realized everything I was doing was pointing me away from biology and towards computer science. <br />
<br />
Before starting graduate school at UW, I homeschooled with my kids for over a decade, and before that I earned an MD from the [https://www.umassmed.edu/ University of Massachusetts Medical School] and a BS in Materials Science and Engineering from [https://dmse.mit.edu/ MIT].<br />
<br />
[https://emilia.cloud/ Personal Website]<br />
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=== Wm Salt Hale (University of Washington) ===<br />
[[File:Salt_Xmas.jpg|thumb|200px|Salt shedding the holiday cheer (2016)]]<br />
<br />
Growing up in Seattle during the early 90s offered many technological opportunities, most of which I took advantage of. As an avid GNU/Linux user for over 20 years, I have been exposed to numerous technology orientated communities on various levels.<br />
<br />
During high school I entered the Running Start program, completing an Associate's degree in Computer Science from South Seattle College. After which I transfered to the University of Washington, pursuing the same major. It was not a fit, instead I developed a number of businesses, traveled, and spoke at various conferences, conventions, events, faires, and festivals.<br />
<br />
Upon returning to the University of Washington to complete my Batchelor's degree in Communication, I connected with [[Mako]] and was shown a world of academia previously unimagined. After another year of traveling, I have decided to return to the UW Department of Comm yet again and am just beginning to delve deeper into the intersection of Technology and Society in the MA/PhD program.<br />
<br />
I am extremely interested in: Free/Libre/Open Source Software (FLOSS) and Culture; Hackers, Makers, and Breakers; and Computer-Mediated Communication using real-time synchronous systems. Along with numerous hobbies including: urban hiking (walking), dancing (folk, east coast swing, lindy, blues), windsports (windsurfing, kiteboarding, sailing), bicycling, boffering, cooking, driving, event planning, gaming, programming, public speaking, reading, robotics, skiing, and travel.<br />
<br />
Up to date information and links to various profiles around the web can be found on ''my'' IndieWeb presence, [http://www.altsalt.net/ The Alt World of Salt].<br />
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=== Sohyeon Hwang (Northwestern University) ===<br />
<br />
[[File:Sohyeonhwang.jpg|thumb|200px|Sohyeon and her dog-child, Tubby.]]<br />
<br />
I'm a third-year PhD student in the Media, Technology, and Society program at Northwestern University, advised by Aaron Shaw. My research interests broadly circle around online governance, mostly around ideas of heterogeneity, scale, and polycentric + decentralized models. I focus on the complexities arising in governance, such as how online groups diversely interpret, innovate beyond, subvert, and co-opt socio-technical affordances to manage themselves.<br />
<br />
I am pretty methods-agnostic, doing both computational/quantitative approaches as well as qualitative work here and there. You can find more information at my [https://www.sohyeonhwang.com site].<br />
<br />
Outside of work, I like to eat french fries and take (blurry) film photos. <br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Charles Kiene (University of Washington) ===<br />
<br />
[[File:Ch2.jpg|thumb|200px|Charlie.]]<br />
<br />
I am a MA/PhD student in the Communication Department at the University of Washington. <br />
<br />
From Reddit subcommunities to Discord servers to ''World of Warcraft'' guilds, my interests lie in the strategies of governance and organizing in online communities, especially when they experience massive change, like user influxes, platform shifts, new technologies, mergers, or an exodus of community members. I'm also interested in the technologies online communities use to organize, such as bots for automating redundant aspects of moderation work, communication applications and the role of voice calls, and other forms of end-user programming that allow online groups to govern and defend their digital boundaries. My future work seeks to evaluate how online communities manage and make sense of internal conflicts and disputes over self-governance, as well as conflicts with other communities and how social boundaries are maintained or change from interactions with mutual or competitive outside groups.<br />
<br />
I use a variety of research strategies including both qualitative and quantitative methods, but most of my past work has involved in-depth interviewing and ethnography.<br />
<br />
Details at [[User:Healspersecond]]<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Jim Maddock (Northwestern) ===<br />
[[File:maddock_cheese_sandwhich.jpg|thumb|200px|Jim eats a cheese sandwich while riding a cow in the Swiss Alps]]<br />
<br />
I'm a PhD Student in the Computer Science and Communications departments at Northwestern University. I currently work with Darren Gergle and Aaron Shaw, studying collaboration and coordination dynamics within social computing systems, such as Wikipedia and Zooniverse. Throughout my tenure as a graduate student I've also interned at MSR India, Google, and Mozilla.<br />
<br />
<br />
I first became interested in HCI during my undergraduate degree at the University of Washington. I earned a degree in Human Centered Design and Engineering, where I worked with Professor Kate Starbird to understand rumoring behavior in crisis situations. I also studied Medieval European history.<br />
<br />
When I'm not working on research, I'm probably riding my bike or planning a backpacking trip. You can find more about my research at my [http://jmaddock.net/ website].<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Nathan TeBlunthuis (University of Washington) ===<br />
{{User:groceryheist/bio}}<br />
<br />
<br />
=== Nick Vincent (Northwestern) ===<br />
My research focuses on studying the relationships between human-generated data and computing technologies to mitigate negative impacts of these technologies. I am especially interested in research that (1) makes people aware of the value of their data and (2) helps people leverage the value of their data. My work relates to concepts such as "data dignity", "data as labor", "data leverage", and "data dividends".<br />
Here's my website: https://www.nickmvincent.com<br />
<br />
== Undergraduate Students ==<br />
<br />
== Affiliate Researchers ==<br />
<div style="clear:both;"><br />
=== Mad Price Ball (Open Humans Foundation) ===<br />
[[File:Mad-portrait-photo-201910.jpg|200px|thumb|Mad eagerly tearing apart [https://twitter.com/madprime/status/979833858039271425 another terrible blockchain idea].]]<br />
<br />
I am Executive Director of Open Humans Foundation and co-founder of [https://www.openhumans.org Open Humans]. My research involvement is more "meta" these days: I help others do it. With Open Humans, we try to enable a new approach for research in health and human subjects research, focusing on personal data. Our work is generally "open" and strives to enable peer production, enabling individuals to create and share tools for getting personal data, analyzing it, and potentially contributing it to aggregate projects (from patient groups to citizen scientists, as well as traditional academic studies). I'm also a Shuttleworth Foundation Fellow (alum) and a member of the BoD of MyData Global.<br />
<br />
Open Humans was inspired by my dual histories in genomics research and free/open culture. My PhD was in biotech and postdoc work involved running George Church's Personal Genome Project, which invited people to donate genome & health data to science by making it public – where I learned a lot about personal data and human subjects research. I'm also familiar with free/open culture folks for well over a decade, contributing here and there; one of my favorite past projects was helping create an offline copy of Wikipedia for OLPC distributed in Peru & Uruguay (my role was creating the article list, mostly based on traffic & connectivity data).<br />
<br />
I live in San Diego, but online you can find me on [http://twitter.com/madprime @madprime on Twitter], in the [http://slackin.openhumans.org Open Humans slack group], and sometimes IRC (madprime) – or reach me by email (mad) at openhumans.org.<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Tilman Bayer ===<br />
[[File:Tilman at Internet Archive 2018.jpg|thumb|170px|Tilman sitting in the [https://en.wikipedia.org/wiki/Internet_Archive Internet Archive's] pews, piously contemplating the world's knowledge]]<br />
I am a longtime Wikipedia contributor (as [[:w:User:HaeB|User:HaeB]]) and editor of the [https://meta.wikimedia.org/wiki/Research:Newsletter Wikimedia Research Newsletter], a monthly publication surveying and reviewing recent academic research about Wikipedia and other Wikimedia projects, which I co-founded in 2011 with my then-colleague Dario Taraborelli at the Wikimedia Foundation. I am also one of the two maintainers of the associated [https://twitter.com/wikiresearch @WikiResearch] Twitter feed. For the past several years, I have joined Mako, Aaron and others in presenting an annual [https://wikimania2018.wikimedia.org/wiki/Program/State_of_Wikimedia_Research_2017-2018 "State of Wikimedia Research"] overview at the Wikimania community conference, where I have also presented on other data and research topics such as the question [https://upload.wikimedia.org/wikipedia/commons/e/e1/Which_parts_of_a_%28Wikipedia%29_article_are_actually_being_read_%28Wikimania_2018%29.pdf which parts of a Wikipedia article people actually read]. <br />
<br />
My work as a data analyst on the Wikimedia Foundation's [https://www.mediawiki.org/w/index.php?title=Product_Analytics&oldid=3173327 Product Analytics team] included controlled experiments and exploratory data analysis to support the development of new software features for Wikipedia readers and contributors, and the analysis of core readership metrics like pageviews. With the Foundation's web team, I drove the implementation of a new metric designed to better understand reader engagement, based on an instrumentation of time spent on page (dwell time). This became the subject of a [https://meta.wikimedia.org/wiki/Research:Reading_time research project] with Nate TeBlunthuis and my then-colleague Olga Vasileva, with findings e.g. about differences in reading behavior between users in the Global South and the Global North.<br />
<br />
My academic background is in pure mathematics, with degrees from the University of Cambridge and the University of Bonn. I am based in San Francisco and can be reached via Gmail ("HaeBwiki") and as "HaeB" on IRC (Freenode).<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Bastian Greshake Tzovaras (Center for Research & Interdisciplinarity, Université Paris Descartes) ===<br />
[[File:BastianGreshakeTzovaras.jpg|200px|thumb|Bastian, being so old-timey that his beard has grown.]]<br />
<br />
Despite having an academic background in biology/bioinformatics, I've been active in peer-produced citizen science since around 2011. I'm one of the co-founders of the crowdsourced, open data repository openSNP ([https://opensnp.org]), which collects personal genomics data sets from users of Direct-To-Consumer genetic testing companies to put them into the public domain. Since 2017 I'm also the Director of Research for Open Humans (https://www.openhumans.org), an ecosystem for participatory citizen science that aims to allow people to analyze and learn from their own personal data as well as given members the opportunity to share their data with (citizen science) research projects. Among other things we have piloted a JupyterHub-based approach to give people their own virtual machines that allow them to write, run and share data analysis notebooks without having to share any personal information (see [https://exploratory.openhumans.org]).<br />
<br />
Since 2019 I'm a research fellow at the Center for Research & Interdisciplinarity in Paris ([https://cri-paris.org/]), where I will study how the ideas of peer-production can be translated to facilitate co-created citizen science projects in which participants are fully involved in all stages of research, from start to finish. Lately a lot of focus there has been on how we can scale up the individualistic quantified self experiments people do to larger cohorts. I also teach students the basics of citizen science and self-tracking. <br />
<br />
Last but not least I'm involved in community building and mentoring in bioinformatics and for open projects in general: I'm a board member of the Open Bioinformatics Foundation ([https://www.open-bio.org/]), have mentored for Mozilla's Open Leadership Cohorts, Outreachy & Google Summer of Code.<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Andrés Monroy-Hernández (Snap Research) ===<br />
[[File:andresmh.jpg|thumb|180px|🚀]]<br />
<br />
I'm a researcher at [https://www.snap.com/ Snap Inc.] and an affiliate faculty at the University of Washington. My work focuses on the study and design of social computing systems. Some areas I've worked on are crowdsourcing, peer production, remixing, civic tech, urban computing, and online learning.<br />
<br />
Some projects I've worked on lately include [http://calendar.help Calendar.help], a hybrid intelligence scheduling assistant partly powered by crowds; Narcotweets, a research project studying how people use social media during war and political uprisings; and the [http://scratch.mit.edu Scratch Online Community], a website where millions of young people learn to program and remix games and animations. <br />
<br />
You can find me at [http://twitter.com/andresmh @andresmh] or at [http://andresmh.com/ www.andresmh.com].<br />
</div><br />
<br />
<div style="clear:both;"><br />
=== Jonathan T. Morgan (Crowdstrike) ===<br />
<br />
[[File:Jtm_profile_pic.jpg|thumb|200px|Jonathan in his preferred horizontal orientation.]]<br />
<br />
I'm a UX researcher at CrowdStrike and an affiliate faculty member in the UW department of Human Centered Design & Engineering. Most of my research involves understanding the sociotechnical mechanisms through which people who use complex collaborative software systems coordinate their work across time and space. You can find out more about me and my work [https://meta.wikimedia.org/wiki/User:Jmorgan_(WMF) here] and [http://jtmorgan.net/ here].<br />
<br />
I am a founding mentor for the [[Community_Data_Science_Workshops|Community Data Science Workshops]], and I also develop and teach UW courses on related topics, like [[Human_Centered_Data_Science|Human Centered Data Science]]. <br />
<br />
I am a voracious and omnivorous reader, and a passionately amateurish musician. When I'm away from the keyboard, you can usually find me exploring the beaches and forests of Puget Sound with my wife and my dog, [[w:Ozymandias|Ozymandias]].<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Morten Warncke-Wang (Wikimedia Foundation) ===<br />
[[File:Warncke-Wang, Morten - Dec 2017.jpg|200px|thumb|Morten prior to growing a scientifically sound beard.]]<br />
<br />
I've been participating in online and peer production communities for over 20 years, and recently (December 2016) got a PhD studying them. My research focus has been on content quality in peer production communities like Wikipedia and OpenStreetMap: what is high quality content, how is it created, can we build tools to judge it, and is it produced where there is demand for it? In addition to research publications, this work has also led to a Python library for predicting Wikipedia article quality ([https://github.com/wiki-ai/articlequality articlequality]) that is publicly available on Wikipedia through the [https://www.mediawiki.org/wiki/ORES ORES API]. I am also a Research Fellow with the [https://research.wikimedia.org Wikimedia Foundation's Research group].<br />
<br />
Another one of my interests is using recommender systems to help contributors find work to do. In Wikipedia this manifests in my maintenance of [https://en.wikipedia.org/wiki/User:SuggestBot SuggestBot]. The bot can recommend articles to work on based on a user's edit history, or they can supply articles or categories they want to base the suggestions on. SuggestBot is currently available in seven languages.<br />
<br />
I've participated as a mentor and instructor in some of the Community Data Science Workshops. Apart from these things, I also like reading (both books and magazines), watching movies, playing [https://en.wikipedia.org/wiki/Squash_(sport) squash], and attempting to make music.<br />
</div><br />
<br />
== Friends and Community Members ==<br />
<br />
<br />
<div style="clear:both;"><br />
=== Alice Ferrazzi ===<br />
[[File:107572.jpeg|thumb|180px|Alice Ferrazzi]]<br />
<br />
I'm a researcher and community member who collaborates and helps the CDSC in various ways. My research work focuses on the study of operating systems kernel where I work mostly in live patch systems. One of my projects is [https://wiki.gentoo.org/wiki/Elivepatch Elivepatch].<br />
<br />
I'm the Gentoo Kernel Project Leader, mainly focused in kernel release automatization. You can find me at [http://twitter.com/aliceinwire @aliceinwire] or at [http://aliceinwire.net/ www.aliceinwire.net]. My Gentoo profile is at [https://wiki.gentoo.org/wiki/User:Aliceinwire User:Aliceinwire]. I am on IRC (OFTC) as alicef_.<br />
<br />
<br />
<br />
<div style="clear:both;"><br />
=== Samuel Klein ===<br />
[[File:Orienteering tunnels.jpg|thumb|180px|right|Samuel Klein on the right (with a surprise Aaron shaw on the left).]]<br />
I'm a wikimedian, urban spelunker, and founding member of MIT's [http://kfg.mit.edu Knowledge Futures Group]. One of my projects is the Innovation Information Initiative, a data collab for patent and prior art datasets. <br />
<br />
Occasionally in IRC as _sj_. [[User:Sj|Sj]] ([[User talk:Sj|talk]]) 15:54, 17 August 2019 (EDT)<br />
<br />
<br />
<div style="clear:both;"><br />
=== Abel Serrano Juste ===<br />
<br />
[[File:Abeserra.jpeg|thumb|200px|Abel Serrano Juste]]<br />
<br />
Interested in how technology can serve communities of people for good. I see free software as an implicit requirement for this.<br />
<br />
I've been working for two years in the University Complutense of Madrid doing data analysis on collaborative online communities (CBPP), more specifically, on wikis. You can see my publications and more info about me in [https://akronix.es/ my homepage].<br />
<br />
I hold a Bachelor's Degree in Computer Science by the UCM and currently I'm enrolled in a Master's Degree of Data Science by the UOC.<br />
<br />
Also, I like bikes, nature, hiking, traveling, and sharing my life with beautiful people.<br />
<br />
<br />
<div style="clear:both;"><br />
=== Sejal Khatri ===<br />
<br />
[[File:Sejal_Khatri.jpg|thumb|200px|Sejal]]<br />
<br />
I recently graduated from the Information School at the University of Washington, Seattle. My specialization was in User Experience Research and Design in the Information Management program at iSchool. I did my undergrad in Computer Science at SPPU in Pune, India, and then interned for Wikimedia Foundation as a UX Engineer. My current research interests revolve around online communities, peer-production, and open source software. When I'm not working, I participate in design jams and hackathons where I get the opportunity to turn curiosities and concerns into design interventions. <br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
=== Kat Walsh ===<br />
<br />
[[File:katwalsh_purple.jpg|thumb|200px|Kat Walsh, with freshly purpled hair]]<br />
<br />
I'm a lawyer working in copyright, speech, policy, and nonprofit leadership around various Free and Open projects and communities, currently working with individual clients including Creative Commons. I got into open communities through volunteering for Wikimedia, first as an editor, then in community dispute resolution, and then as a board member for several years. I've also been on the board of the Free Software Foundation. <br />
<br />
I enjoy collaborating with academic researchers on work in peer production communities and their copyright/"intellectual property", dispute resolution, governance, and legal policy issues. I am located just north of San Francisco, where I enjoy playing my bassoon, viola, and occasionally some other things in a delightfully weird collection of musical groups, and lifting heavy objects for no particular reason.<br />
<br />
</div><br />
<br />
<div style="clear:both;"><br />
<br />
== Alumni ==<br />
<br />
'''NOBODY HAS EVER LEFT'''</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Group_Tasks&diff=223143CommunityData:Group Tasks2021-10-08T16:48:47Z<p>Nickmvincent: Create and populate new "Group Tasks" page</p>
<hr />
<div>== Operations == <br />
* send around the scheduling poll each quarter<br />
* take notes in the etherpad (during mtgs and such)<br />
* announce/reminders for meetings, agendas<br />
* organize retreats (virtual and non)<br />
* plan social events<br />
* invite speakers for soft blocks / planning, scheduling workshop sessions (new, but still)<br />
<br />
== Maintenance ==<br />
* Wiki Gardening<br />
* Software Updating<br />
* Sysadmin/appadmin work related to research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Managing research / collaboration infrastructure+tools (wiki, email lists, calendar, git, hyak, kibo, web servers/sites)<br />
* Financial administration (reimbursements, payroll/hiring, travel, etc.)<br />
* communication/outreach<br />
<br />
<br />
== Group Nurturing ==<br />
* train people on systems & tools (hyak/overleaf/R/etc.)<br />
* newcomer orientations, recruitment, hiring<br />
* organize feedback sessions for RAs</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=CommunityData:Resources&diff=223142CommunityData:Resources2021-10-08T16:44:59Z<p>Nickmvincent: Adding Group Tasks new page</p>
<hr />
<div>This page collects resources for Community Data Science Collective members.<br />
<br />
If you're new to the collective, check out the [https://wiki.communitydata.science/CommunityData:Introduction_to_CDSC_Resources Introduction to CDSC Resources].<br />
<br />
== Non-Technical Resources ==<br />
<br />
* [[Schedule]] — Deadlines, events, and similar<br />
* [[CommunityData:Workshop]] - Weekly workshop sessions for sharing work and getting feedback<br />
* [[CommunityData:Jargon]] — Jargon and Common Shorthand<br />
* [[CommunityData:Planning document]] — Details on producing Matsuzaki-style planning documents<br />
* [[CommunityData:Logos]] — Like our visual branding, not like λόγος. Although we should always make sure we're good in that department too. very clear pointers. Save yourself the trouble and learn to follow these today!<br />
* [[Community Data Science Lab (UW)]] — Directions to the lab space at UW. This is something you can share with visitors.<br />
* [[Community Data Science Lab (NU) Pandemic research plan]] — Pandemic research plan for CDSC NU created as part of Northwestern's response to the COVID-19 pandemic.<br />
* [[CommunityData:General examinations motivating questions]] — A set of questions borrowed and adapted from [https://www.hcde.washington.edu/turns Jennifer Turns] that are a useful ways to start preparing for general examinations.<br />
<br />
== Communication Infrastructure ==<br />
<br />
* [[CommunityData:Email]] — Information on email lists, email aliases and their management.<br />
* [[CommunityData:IRC]] — How to get set up on our chat system, [[:wikipedia:IRC|IRC]]<br />
* [[CommunityData:Jitsi]] — Some etiquette/usability tips for Jitsi, our preferred video conference tool. <br />
* [[CommunityData:Blog and social media]] — Writing/editing blogposts, tweets, and social media<br />
<br />
* [[CommunityData:Blog post schedule]] — What's up next?<br />
<br />
== Research Infrastructure ==<br />
<br />
* [[CommunityData:Code]] — List of software projects maintained by the collective.<br />
* [[CommunityData:Exporting from Python to R]]<br />
* [[CommunityData:Git]] — Getting set up on the git server<br />
* [[CommunityData:Otter.ai]] — Audio-to-text transcription software<br />
* [[CommunityData:Taguette]] — Qualitative coding analysis software<br />
* [[CommunityData:Tmux]] — Using tmux (terminal multiplexer) to keep a persistent session on a server. <br />
* [[CommunityData:Zotero]] — How to use our shared Zotero directory.<br />
* [[CommunityData:Etherpad]] — We use [[:wikipedia:Etherpad|Etherpad]] for collaborative real-time note-taking and such. This page has some information about that as well details about how to make sure your pad is backedup.<br />
* [[CommunityData:MySQL]] How to use MySQL databases on Kibo.<br />
<br />
== Papers, Presentations, and Templates ==<br />
<br />
Stuff related to getting setup and/or troubleshooting things related to LaTeX and papers:<br />
<br />
* [[CommunityData:TeX]] — Installing our LaTeX templates<br />
* [[CommunityData:Beamer]] — Installing/using [[Mako]]'s Beamer templates<br />
* [[CommunityData:Knitr]] — Using Knitr with Tex to build graphs, tables, insert and format numbers in tex documents. <br />
* [[CommunityData:Embedding fonts in PDFs]] — <code>ggplot2</code> creates PDFs with fonts that are not embedded which, in turn, causes the ACM to bounce our papers back. This page describes how to fix it.<br />
* [[CommunityData:Build papers]] — Both the TeX and Beamer templates above come along with a Makefile that makes some assumptions about your workflow. Learn about that here.<br />
<br />
A few of us use HTML-based presentation. Information on that is here:<br />
<br />
* [[CommunityData:reveal.js]] — Using RMarkdown to create reveal.js HTML presentations<br />
<br />
== Computation, Severs, and HPC ==<br />
<br />
* [[CommunityData:Compute Overview and Resource Matching]] -- What we have and what it's good for<br />
* [[CommunityData:Hyak]] — Using the Hyak supercomputer system at UW for research (several pages are linked from the top of that page)<br />
* [[CommunityData:Hyak_tutorial]] - Tutorial for new people to learn how to use Hyak.<br />
* [[CommunityData:Kibo]] — Getting started with the Kibo system at NU for research.<br />
* [[CommunityData:MySQL]] — Creating MySQL databases on Kibo<br />
* [[CommunityData:Northwestern VPN]] — Connecting to the Northwestern VPN<br />
* [[CommunityData:Backups (nada)]] — Details on what is, and what isn't, backed up from nada.<br />
<br />
== Research and Data ==<br />
<br />
* [[CommunityData:ORES]] - Using ORES with wikipedia data<br />
* [[CommunityData:Wikia data]] — Documents information about how to get and validate wikia dumps.<br />
<br />
Project Pages:<br />
<br />
* [[CommunityData:Message Walls]] -- Documents information about how to get and validate wikia dumps.<br />
<br />
== Future Meetings and Conferences ==<br />
<br />
* [[CommunityData:Meetup April 2020]]<br />
* [[CommunityData:UW Weekly Meeting]]<br />
* [[CommunityData:Critique and Feedback Session]]<br />
<br />
== Past Meetups ==<br />
<br />
Group meetups:<br />
<br />
* <strike>[[CommunityData:Meetup April 2020]]</strike> (cancelled due to [[COVID]])<br />
* [[CommunityData:Meetup September 2019]]<br />
* [[CommunityData:Meetup March 2019]]<br />
* [[CommunityData:Meetup September 2018]]<br />
* [[CommunityData:Meetup April 2018]]<br />
* [[CommunityData:Meetup April 2018: Organizational notes]]<br />
* [[CommunityData:Meetup July 2017]]<br />
<br />
Other meetups:<br />
<br />
* [[CommunityData:CSCW 2019]]<br />
* [[Sociotechnocanonicon|Sociotechnocanonicon Great Books Discussion Series]]<br />
<br />
== University of Washington Resources ==<br />
<br />
* [[CommunityData:Related seminars at UW]]<br />
* [[CommunityData:IRB training for Scratch Research at UW]]<br />
<br />
== Northwestern Resources ==<br />
<br />
* [[CommunityData:NU grant reimbursement]]<br />
<br />
== Diversions == <br />
* [[CommunityData:Light events]] — The light in the lab at UW is funny. We have three fluorescent lights. On flipping the light switch, only two turn on. The third turns on ''eventually''. We are studying this arcane phenomenon.<br />
<br />
* [[CommunityData:GameIDs]] — A directory containing the game IDs for CDSC members to connect with each other across various gaming platforms.<br />
<br />
== Group Tasks == <br />
* [[CommunityData:Group Tasks]] - A collection of different tasks that benefit the group.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=219731User:Aaronshaw/AdvisingOH2021-01-28T20:19:00Z<p>Nickmvincent: /* February 4 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Winter 2021) quarter signups ==<br />
<br />
=== January 28 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — Nick H<br />
<br />
=== February 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 18 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — --<br />
* 1430-1500 — «available»<br />
<br />
=== February 25 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — --<br />
* 1430-1500 — «available»<br />
<br />
=== March 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=219579User:Aaronshaw/AdvisingOH2021-01-21T19:22:26Z<p>Nickmvincent: /* Current (Winter 2021) quarter signups */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Winter 2021) quarter signups ==<br />
<br />
=== January 14 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick H.<br />
* 1400-1430 — Floor (if nobody else wants the slot; I know it's not my week :) )<br />
* 1430-1500 — «available»<br />
<br />
=== January 21 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== January 28 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — --<br />
* 1430-1500 — «available»<br />
<br />
=== February 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 18 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — --<br />
* 1430-1500 — «available»<br />
<br />
=== February 25 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — --<br />
* 1430-1500 — «available»<br />
<br />
=== March 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=219578User:Aaronshaw/AdvisingOH2021-01-21T19:21:56Z<p>Nickmvincent: /* January 28 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Winter 2021) quarter signups ==<br />
<br />
=== January 14 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick H.<br />
* 1400-1430 — Floor (if nobody else wants the slot; I know it's not my week :) )<br />
* 1430-1500 — «available»<br />
<br />
=== January 21 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== January 28 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick V<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 18 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 25 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== March 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=219443User:Aaronshaw/AdvisingOH2021-01-13T20:07:39Z<p>Nickmvincent: /* March 4 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Winter 2021) quarter signups ==<br />
<br />
=== January 14 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick H.<br />
* 1400-1430 — Floor (if nobody else wants the slot; I know it's not my week :) )<br />
* 1430-1500 — «available»<br />
<br />
=== January 21 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== January 28 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 18 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 25 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== March 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=219442User:Aaronshaw/AdvisingOH2021-01-13T20:07:31Z<p>Nickmvincent: /* February 18 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Winter 2021) quarter signups ==<br />
<br />
=== January 14 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick H.<br />
* 1400-1430 — Floor (if nobody else wants the slot; I know it's not my week :) )<br />
* 1430-1500 — «available»<br />
<br />
=== January 21 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== January 28 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 18 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 25 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=219441User:Aaronshaw/AdvisingOH2021-01-13T20:07:16Z<p>Nickmvincent: /* February 4 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Winter 2021) quarter signups ==<br />
<br />
=== January 14 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick H.<br />
* 1400-1430 — Floor (if nobody else wants the slot; I know it's not my week :) )<br />
* 1430-1500 — «available»<br />
<br />
=== January 21 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== January 28 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== February 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 18 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 25 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=User:Aaronshaw/AdvisingOH&diff=219440User:Aaronshaw/AdvisingOH2021-01-13T20:07:07Z<p>Nickmvincent: /* January 21 */</p>
<hr />
<div>Welcome to my remote (advising) office hours scheduling page!<br />
<br />
== Instructions ==<br />
* Pick a date you'd like to book an OH appointment from the options below.<br />
* Review the available slots for that date. Note that all time slots correspond to current US Central Time in Chicago, Illinois.<br />
* Click the blue "edit" link next to the date.<br />
* Delete the corresponding "«available»" and replace it with your name (no [https://en.wikipedia.org/wiki/Guillemet Guillemets] needed).<br />
* If there is something you hope I will read or prepare ahead of our meeting, please include a topic and share that information with me 24 hours before the meeting [mailto:aaronshaw@northwestern.edu via email].<br />
* Show up to your meeting with me in my office hours jitsi channel: [[https://meet.jit.si/aaronoffice]]. If a password is required, it will be the name of the channel ("aaronoffice").<br />
<br />
== Current (Winter 2021) quarter signups ==<br />
<br />
=== January 14 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — Nick H.<br />
* 1400-1430 — Floor (if nobody else wants the slot; I know it's not my week :) )<br />
* 1430-1500 — «available»<br />
<br />
=== January 21 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — Nick V<br />
* 1430-1500 — «available»<br />
<br />
=== January 28 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 18 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== February 25 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 4 ===<br />
* 1300-1330 — Floor<br />
* 1330-1400 — Jim<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»<br />
<br />
=== March 11 ===<br />
* 1300-1330 — Sohyeon<br />
* 1330-1400 — «available»<br />
* 1400-1430 — «available»<br />
* 1430-1500 — «available»</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)/w11_session_plan&diff=208708Statistics and Statistical Programming (Fall 2020)/w11 session plan2020-11-24T19:00:48Z<p>Nickmvincent: /* 11/24 */</p>
<hr />
<div>===11/24===<br />
* Problem Set 8<br />
** Mario kart dataset<br />
** datasaurus<br />
** Trick or treating (again)<br />
* Additional topics for discussion<br />
** Interaction terms<br />
** robust standard errors<br />
* Post course assessment<br />
* Final project assignments<br />
* +/delta discussion re: course<br />
* Next steps?</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)/w11_session_plan&diff=208707Statistics and Statistical Programming (Fall 2020)/w11 session plan2020-11-24T19:00:20Z<p>Nickmvincent: Created page with "===11/24=== * pset8 ** mario kart ** datasaurus ** Trick or treating (again) * Additional topics for discussion ** Interaction terms ** robust standard errors * Post course as..."</p>
<hr />
<div>===11/24===<br />
* pset8<br />
** mario kart<br />
** datasaurus<br />
** Trick or treating (again)<br />
* Additional topics for discussion<br />
** Interaction terms<br />
** robust standard errors<br />
* Post course assessment<br />
* Final project assignments<br />
* +/delta discussion re: course<br />
* Next steps?</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&diff=208706Statistics and Statistical Programming (Fall 2020)2020-11-24T18:58:51Z<p>Nickmvincent: /* November 24: Applied multiple and logistic regression */</p>
<hr />
<div><div style="float:right;" width=30%; class="toclimit-3">__TOC__</div><br />
<br />
;Statistics and Statistical Programming<br />
:Media, Technology & Society (MTS) 525 and Communication Studies 395<br />
:Tuesdays & Thursdays 1-2:50pm CT<br />
:Fall 2020<br />
:Northwestern University<br />
<br />
;Course websites<br />
: [https://canvas.northwestern.edu/courses/122522 Canvas] for [https://canvas.northwestern.edu/courses/122522/announcements announcements], [https://canvas.northwestern.edu/courses/122522/assignments assignments], and some [https://canvas.northwestern.edu/courses/122522/files files].<br />
: [https://northwestern.zoom.us Zoom] for synchronous course meetings.<br />
: [https://discord.com Discord] for discussions and chat.<br />
: [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020) This wiki page] for nearly everything else.<br />
<br />
;'''Instructor:''' [http://aaronshaw.org Aaron Shaw] ([mailto:aaronshaw@northwestern.edu aaronshaw@northwestern.edu])<br />
:Office Hours: Thursday 10am-12pm and by appointment<br />
:Please use [[User:Aaronshaw/OH|office hours signups (with location information)]]<br />
:Also usually available via chat during "business hours."<br />
<br />
:'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu])<br />
::Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.<br />
::I am happy to try out alternative communication software for OH!<br />
<br />
<br><br />
[[File:Datasaurus.gif|left|450px|frame|Image from [https://www.autodeskresearch.com/publications/samestats Matejka and Fitzmaurice, ''CHI'', 2017]|link=https://www.autodeskresearch.com/publications/samestats]]<br />
<br clear=all><br />
<br />
== Course information ==<br />
=== Overview and learning objectives ===<br />
<br />
This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, human computer interaction (HCI), and adjacent disciplines.<br />
<br />
I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:<br />
* Design and execute a quantitative research project that involves statistical inference, start to finish.<br />
* Read, modify, and create short programs in the R statistical programming language.<br />
* Feel comfortable reading and interpreting papers that use basic statistical techniques.<br />
* Feel prepared to enroll in more specialized and advanced statistics courses.<br />
<br />
The course will cover a number of techniques, likely including the following: t-tests; chi-squared tests; ANOVA; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.<br />
<br />
The course materials will consist of readings, problem sets, assessment exercises, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.<br />
<br />
You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover most of the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is not assumed.<br />
<br />
'''Why this course? Why statistical programming? Why R?'''<br />
<br />
Many comparable courses in statistics and quantitative methods do not emphasize statistical programming. So why bother? By learning statistical programming you will gain a deeper understanding of both the principles behind your analysis techniques as well as the tools you use to apply those techniques. In addition, a solid grasp of statistical programming will prepare you to create reproducible research, avoid common errors, and enable both greater durability and validity of your work. <br />
<br />
Other programming languages are also well suited to statistics, including Stata and Python. I do most of my work with R, so that guides my choice for the course. That said, I opt to use and teach with R for a few reasons:<br />
* R is freely available and open source.<br />
* R is the most widely used package in statistics and several social scientific fields.<br />
* R (along with Stata) will be used in most of the advanced stats classes I hope you will take after this course.<br />
* R is better general purpose programming language than Stata which means that R programming skills will let you solve non-statistical problems and may make it easier to learn other programming languages like Python.<br />
<br />
=== Format and structure ===<br />
<!---<br />
I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go.<br />
---><br />
<br />
This course will proceed in a '''remote''' format that includes ''asynchronous'' and ''synchronous'' elements (more on those below). In general, the organization of the course adopts a "flipped" approach where participants consume, discuss, and process instructional materials outside of "class" and we use synchronous meetings to answer questions, address challenges or concerns, work through solutions, and hold semi-structured discussions. <br />
<br />
The course introduces ''both'' basic statistical concepts as well as applications of those concepts through statistical programming. As a result, we will usually dedicate part of each week to a particular set of concepts and part of each week to applied data analysis and/or interpretation. A brief description of how I expect it all to work follows below. We'll talk about it more during the first class session.<br />
<br />
====Asynchronous elements of the course====<br />
<br />
These include all readings, recorded lectures/slides, tutorials, textbook exercises, problem sets, and other assignments. I expect you to complete (or at least attempt to complete!) these outside of our class meeting times. I also strongly encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible.<br />
<br />
We will use Discord for everyday discussions and chat related to the course. In general, the teaching team will try to keep an eye on the various server channels during "business hours." To the extent that we can respond to questions and concerns there, we'll do so. We'll also use the discussion channels to identify topics that might benefit from synchronous conversation during the course meetings. Hopefully, writing and talking about questions and concerns outside of the synchronous course meetings will help support accountability, learning, and more effective use of our meeting time.<br />
<br />
For nearly all of the "instructional" material introducing particular statistical concepts and techniques, you are assigned materials from the OpenIntro textbook and lecture materials created by the textbook authors. Please note that this means I will not deliver lectures during our class meetings. Please also note that this means you are responsible for coordinating your working groups and any collaborative work with other members of the class outside of our class meeting times.<br />
<br />
====Synchronous elements of the course====<br />
<br />
The synchronous elements of the course will be the two weekly class meetings that will happen via video conference (Zoom). These are scheduled to run for a maximum of 110 minutes. Each session will include multiple short breaks. <br />
<br />
We will use the class meetings to discuss and work through any questions or challenges you encounter in the materials assigned for that day. This means that I encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible. Doing so will give the teaching team time to sift, sort, and organize the questions into a hopefully-cohesive plan for each class session that is tailored to the specific concerns you encounter in the material. Obviously, we anticipate that questions will arise during the class sessions too as well and we'll do our best to adapt as we go.<br />
<br />
A couple of other notes about the synchronous course meetings:<br />
* Aaron plans to record the course meetings and have them available to class participants only via Zoom/Canvas. Please get in touch if you have concerns or requests about this. <br />
* The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts.<br />
* You might want to create/acquire something like [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera] to facilitate sharing hand-written notes/drawings during class.<br />
<br />
In addition, because randomness is extremely important in statistics, I may occasionally '''randomly assign''' different working groups to share and discuss their solutions to selected textbook exercises or problem set questions during class. These random assignments will be announced ahead of time so that the group has an opportunity to prepare. The idea here is to structure some participation in the synchronous sessions to ensure an equitable distribution of the responsibility to discuss questions, answers, points of confusion, and alternatives.<br />
<br />
==== Working groups ==== <br />
<br />
At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you may meet outside of class time to discuss, complete, and/or review your weekly assignments (as well as some of the research project assignments). The groups will rotate at least once during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer support, and accountability. While the specifics of exactly when and how you work with your working group will largely be up to you, the teaching team will provide [[Statistics_and_Statistical_Programming_(Fall_2020)/Working_groups_template|suggestions in the form of a template]] that you can use as a starting point.<br />
<br />
As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators. <br />
<br />
<!---<br />
Although the day-to-day routine will vary, each class session will generally include the following:<br />
* Quick updates about assignments, projects, and meta-discussion about the class.<br />
* Discussion of '''programming challenges''' due that day (and related to the previous week's R lecture materials).<br />
* Discussion of '''statistics questions''' related to new material in Diez, Barr, and Çetinkaya-Rundel.<br />
* Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''.<br />
---><br />
<br />
=== Textbook, readings, and resources ===<br />
<br />
This class will use a freely-licensed textbook:<br />
<br />
* Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2019. [https://www.openintro.org/book/os/ ''OpenIntro Statistics'']. 4th edition. OpenIntro, Inc.<br />
<br />
The texbook (in any format) is required for the course. You can [https://www.openintro.org/go?id=os4&referrer=/book/os/index.php download it] at no cost and purchase hard copy versions in either [https://www.openintro.org/go?id=os4_color_pb&referrer=/book/os/index.php full color ($60)] or in [https://www.openintro.org/go?id=os4_bw_pb&referrer=/book/os/index.php black and white ($20)]. The B&W version is very affordable and I strongly recommend buying a hard copy for the purposes of the course and subsequent reference use. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).<br />
<br />
I will also assigning several chapters from the following:<br />
<br />
* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. ([https://search.library.northwestern.edu/primo-explore/fulldisplay?docid=01NWU_ALMA51732460650002441&context=L&vid=NULVNEW&search_scope=NWU&tab=default_tab&lang=en_US Safari online via NU libraries])<br />
<br />
This book provides a readable conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library (you'll need to sign-in and/or use the NU VPN to access it), but you may find it helpful to purchase as well.<br />
<br />
A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:<br />
<br />
* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton UP. ([https://kieranhealy.org/publications/dataviz/ via Healy's website])<br />
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon])<br />
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon])<br />
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources])<br />
* Wickham, Hadly and Grolemund, Garret. 2017. ''R for Data Science''. Sebastopol, CA: O'Reilly. ([https://r4ds.had.co.nz/ Online version]).<br />
<br />
There are also some invaluable non-textbook resources:<br />
<br />
* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!<br />
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.<br />
* [http://rseek.org/ Rseek] — Rseek is a modified version of Google that just searches R websites online. Sometimes, R is hard to search because R is a common letter. This has become much easier over time as R has become more popular, but it can still be an issue sometimes and Rseek is a good solution.<br />
* [https://ggplot2.tidyverse.org/ ggplot2 documentation] — ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.<br />
* [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R.<br />
* [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.<br />
<br />
Computing resources:<br />
* If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]].<br />
<br />
=== Weekly (minor) assignments ===<br />
<br />
In order to support continuous progress towards the learning goals for the course, I have assigned some textbook exercises or a problem set ahead of every class. These assignments will provide the basis on which the teaching team will assess and provide feedback on your participation and engagement with the course material.<br />
<br />
The first week or so of the course is textbook-focused to get us warmed up. Starting in week 2, we will do more statistical programming and apply the textbook concepts using R and RStudio. In general, we will cover the problem sets in the first session of the week and the textbook materials in the second session. <br />
<br />
==== Textbook exercises ====<br />
The focus is on self-assessment of your understanding of the textbook material and you do not need to hand in anything. I expect that you will work on the exercises, review and discuss solutions, and submit any questions ahead of or during class. Please note that solutions to odd-numbered problems appear in the back of the book. The teaching team will distribute solutions to even-numbered problems as well.<br />
<br />
==== Problem sets ====<br />
The course will include problem sets and these may incorporate several kinds of questions:<br />
<br />
* '''Statistics questions''' about statistical concepts and principles.<br />
* '''Programming challenges''' that you should solve using R.<br />
* '''Empirical paper questions''' about other assigned readings. <br />
<br />
For the problem sets, I ask that you submit your work [https://canvas.northwestern.edu/courses/122522/assignments via Canvas 24 hours before class] (i.e., Monday afternoon for our Tuesday class sessions). Details of exactly how this will work will be elaborated during the first class. For the programming challenges, you should submit code and text for your solutions (again, more on how later). If you get completely stuck on a problem, that's okay, but please provide whatever you have.<br />
<br />
Problem sets will be evaluated on a complete/incomplete basis. Although the problem sets will not be assigned a letter grade, they are a central focus of the course and completing them will support your mastery of the material in multiple ways. Working through them on schedule will also make it possible for you to participate in the synchronous course meetings and online discussions of course material effectively. Your ability to do so will figure prominently in your participation grade for the course (see the section on grading and assessment below).<br />
<br />
=== Research project (major) assignments ===<br />
<br />
==== Overview ====<br />
As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:<br />
<br />
* '''Design and describe a plan for a study''' — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.<br />
* '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a analysis of a previously collected dataset.<br />
* '''Engage in descriptive data analysis''' — Use R to calculate descriptive statistics and visualizations to describe your data.<br />
* '''Motivate and test at least one hypothesis about relationships between two or more variables''' — I'm happy to discuss alternatives to formal hypothesis testing procedures (even if some of them are beyond the scope of this course). <br />
* '''Report and interpret your findings''' — You will do this in both a short paper and a short (recorded) presentation.<br />
* '''Ensure that your work is replicable''' — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.<br />
<br />
''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement.<br />
<br />
There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas by 5pm CT on the day they are due.<br />
<br />
<br />
==== Research project plan and dataset identification ====<br />
<br />
;Due date: October 9, 2020, 5pm CT<br />
;Maximum length: 500 words (~1-2 pages)<br />
<br />
Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:<br />
<br />
* An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.<br />
* An identification of the dataset you will use and a description of the rows and columns or type(s) of data it will include. If you do not currently have access to these data, explain why and when you will.<br />
* A short (several sentences?) description of how the project will fit into your career trajectory.<br />
<br />
<br />
===== Notes on finding a dataset =====<br />
<br />
In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:<br />
<br />
* Ask your advisor for a dataset they have collected and used in previous papers. Are there other variables you could use? Other relationships you could analyze?<br />
* If there's an important study you loved, you can send a polite email to the author(s) asking if they are willing and able to share an archival or replication version of the dataset used in their paper. Be very polite and make it clear that this is starting as a class project, but that it might turn into a paper for publication. Make your timeline clear. In Communication and HCI, replication datasets are still very rare, so be prepared for a negative answer and/or questions about your motives in conducting the analysis.<br />
* Do some Google Scholar and normal internet searching for datasets in your research area. You'll probably be surprised at what's available.<br />
* Take a look at datasets available in the [https://dataverse.harvard.edu/ Harvard Dataverse] (a very large collection of social science research data) or one of the other members of the [http://dataverse.org/ Dataverse network].<br />
* Look at the collection of social scientific datasets at [https://www.icpsr.umich.edu/icpsrweb/ICPSR/ ICPSR at the University of Michigan] (NU is a member). There are an enormous number of very rich datasets.<br />
* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.<br />
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.<br />
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.<br />
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].<br />
* The NY Times is publishing a [https://github.com/nytimes/covid-19-data COVID-19 data repository] that includes county-level metrics for deaths, mask usage, and other pandemic-related data. The release a lot of it as frequently updated .csv files and the repository includes documentation of the measurements, data collection details, and more.<br />
* The Community Data Science Collective and colleagues have created a [[COVID-19_Digital_Observatory| COVID-19 digital observatory]] (hosted in part right here on this wiki!) that publishes a bunch of pandemic-related data as csv and json files.<br />
* The [https://openpolicing.stanford.edu Stanford Open Policing project] has published a huge archive of policing data related mostly to traffic stops in states and many cities of the U.S. We'll use at least one of these files for a problem set.<br />
<br />
==== Research project planning document ====<br />
<br />
;Due date: October 30, 2020, 5pm CT<br />
;Suggested length: ~5 pages<br />
<br />
The project planning document is a shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found [[CommunityData:Planning document|on this wiki page]].<br />
<br />
I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course):<br />
* [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work.<br />
* [https://canvas.northwestern.edu/files/9421229/download?download_frd=1 One by Jim Maddock] created as part of a qualifying exam early in 2019. Jim doesn't provide dummy tables or anticipated findings/contributions, but he has an especially phenomenal explanation of the conceptual relationships and processes he wants to test. <br />
* [https://canvas.northwestern.edu/files/9439379/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation.<br />
<br />
==== Research project presentation ====<br />
<br />
;Presentation due date: December 3, 2020, 5pm CT<br />
;Maximum length: 10 minutes<br />
<br />
<!-- TODO revisit old presentations page to update/adapt <br />
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]]<br />
---><br />
You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [https://canvas.northwestern.edu/files/9439377/download?download_frd=1 Creating a Successful Scholarly Presentation] (file posted to Canvas) may be useful.<br />
<br />
Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter.<br />
<br />
==== Research project paper ====<br />
<br />
;Paper due date: December 8, 2020, 5pm CT<br />
;Maximum length: 6000 words (~20 pages)<br />
<br />
I expect you to produce a short, high quality research paper that you might revise, extend, and submit for publication and/or a dissertation milestone. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the [[structure of a quantitative empirical research paper]].<br />
<br />
As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.<br />
<br />
Because the emphasis in this class is on statistics and methods and because I'm probably not an expert in the substance of your research domain, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.<br />
<br />
I have a strong preference for you to write the paper individually, but I'm open to the idea that you may want to work with others in the class. Please contact me ''before'' you attempt to pursue a collaborative final paper.<br />
<br />
I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.<br />
<br />
<br />
==== Human subjects research, IRB, and ethics ====<br />
In general, you are responsible for making sure that you're on the right side of the IRB requirements and that your work meets applicable ethical norms and standards.<br />
<br />
Class projects generally do not need IRB approval, but research for publications, dissertations, and sometimes even pilot studies do fall under IRB purview. You should ''not'' plan to seek IRB approval/determination retroactively. If your study may involve human subjects and you may ever publish it in any form, you will need IRB oversight of some sort.<br />
<br />
Secondary analysis of anonymized data is generally not considered human subjects research, but I strongly suggest that you get a determination from [https://irb.northwestern.edu/ the Northwestern IRB] before you start. For work that is not considered human subjects research, this can often happen in a few hours or days. If you need to list a faculty sponsor or Principal Investigator, that should ideally be your advisor. If that doesn't make sense for some reason, please talk to me.<br />
<br />
Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching.<br />
<br />
=== Grading and assessment ===<br />
<br />
I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course.<br />
<br />
* Weekly participation: 40%<br />
* Proposal identification: 5%<br />
* Final project planning document: 5%<br />
* Final project presentation: 10%<br />
* Final project paper: 40%<br />
<br />
The teaching team will jointly and holistically evaluate your participation along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of [https://mako.cc/teaching/assessment.html Benjamin Mako Hill's assessment page] and [https://reagle.org/joseph/zwiki/Teaching/Assessment/Participation.html Joseph Reagle's participation assessment rubric]. Exceptional participation means excelling along all four dimensions. Please note that participation ≠ talking/typing more and I encourage all of us to seek [https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Balance_in_Discussion.html balance in our discussions].<br />
<br />
The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities.<br />
<br />
=== Policies ===<br />
<br />
==== General course policies ====<br />
<br />
[[User:Aaronshaw/Classroom_policies|General policies]] on a wide variety of topics including classroom equity, attendance, academic integrity, accommodations, late assignments, and more are provided [[User:Aaronshaw/Classroom_policies|on Aaron's class policies page]]. Below are some policy statements specific to this course and quarter.<br />
<br />
==== Teaching and learning in a pandemic ====<br />
<br />
The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways. <br />
<br />
On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and/or distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the past six months and I expect that some of you have too. It is a difficult time.<br />
<br />
I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.<br />
<br />
==== Expectations for synchronous remote sessions ====<br />
<br />
The following are some baseline expectations for our synchronous remote class sessions. I expect that these can and will evolve. Please feel free to ask questions, suggest changes, or raise concerns during the quarter. I welcome all input.<br />
* All members of the class are expected to create a supportive and welcoming environment that is respectful of the conditions under which we are participating in this class.<br />
* All members of the class are expected to take reasonable steps to create an effective teaching/learning environment for themselves and others.<br />
<br />
And here are suggested protocols for any video/audio portions of our class:<br />
* Please mute your microphone whenever you're not speaking and learn to use [https://en.wikipedia.org/wiki/Push-to-talk "push-to-talk"] if/when possible.<br />
* Video is optional for all students at all times, although if you're willing/able to keep the instructor company in the video channel that would be nice.<br />
* If you need to excuse yourself at any time and for any reason you may do so.<br />
* Children, family, pets, roommates, and others with whom you may share your workspace are welcome to join our class as needed.<br />
<br />
==== Syllabus revisions ====<br />
<br />
This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following:<br />
<br />
# '''Assignments and readings are ''frozen'' 1 week before they are due.''' I will not add readings or assignments less than one week before they are due. If I forget to add something or fill in a "To Be Determined" less than one week before it's due, it is dropped. If you plan to read or work more than one week ahead, contact me first.<br />
# '''Substantial changes to the syllabus or course materials will be announced.''' Please closely monitor your email and/or [https://canvas.northwestern.edu the announcements section on the course website on Canvas]. When I make changes, these changes will be recorded in [https://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&action=history the edit history of this page] so that you can track what has changed. I will also do my best to summarize these changes in an announcement on Canvas that will be emailed to everybody in the class.<br />
# '''The course design may adapt throughout the quarter.''' As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again.<br />
<br />
==== Statistics and power ====<br />
<br />
The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my [[User:Aaronshaw/Classroom_policies|general classroom policies]] for more on some of these topics.<br />
<br />
== Schedule (with all the details) ==<br />
<br />
When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n.<br />
<br />
=== Week 1 (9/17) ===<br />
==== September 17: Intro and setup ====<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w01_session_plan|Session plan]]<br />
<br />
<blockquote>''Note: Aaron doesn't actually expect you to complete these before class on September 17''</blockquote><br />
<br />
'''Required'''<br />
* Read this syllabus, discuss any questions/concerns with the teaching team.<br />
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email). Estimated time to do this is 30-40 minutes. '''Submission deadline: September 18, 11:00pm Chicago time'''<br />
* Confirm course registration and access to [https://www.openintro.org/book/os/ the textbook] (pdf download available for $0 and b&w paperbacks for $20) as well as any software and web-services you'll need for course (Zoom, Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email.<br />
* Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set #0] <br />
<br />
'''Recommended'''<br />
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:<br />
** '''From Aaron:''' The [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Week 01 R tutorial] (you should also download the [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.rmd .rmd version of the tutorial] that you can open and read/edit in RStudio). These are accompanied by the R and Rstudio intro screencasts ([https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Part 1] and [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Part 2]) Aaron created for the 2019 version of the course. <br />
** Modern Dive [https://moderndive.netlify.app/index.html Statistical inference via data science] Chapter 1: [https://moderndive.netlify.app/1-getting-started.html Getting started with R].<br />
** [https://rladiessydney.org/courses/ryouwithme/ RYouWithMe] course [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ "Basic basics" 1 & 2] (and maybe 3 if you're feeling ambitious).<br />
** Verzani §1 (Getting started).<br />
** Healy §2 (Get started).<br />
<br />
=== Week 2 (9/22, 9/24) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]<br />
==== September 22: Data and variables ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)].<br />
* Complete '''exercises from OpenIntro §1:''' 1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
==== September 24: Numerical and categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). <br />
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)].<br />
* Complete '''exercises from OpenIntro §2:''' 2.12, 2.13, 2.16, 2.20, 2.23, 2.30 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
=== Week 3 (9/29, 10/1) ===<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]<br />
<br />
==== September 29: R fundamentals: Import, transform, tidy, and describe data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w03-R_tutorial.html Week 3 R tutorial] (note that you can access .rmd or .pdf versions by replacing the suffix of the URL accordingly).<br />
* Additional material from any of the recommended R learning resources suggested last week or elsewhere in the syllabus. In particular, you may find the ModernDive, RYouWithMe, Healy, and/or Wickham and Grolemund resources valuable.<br />
<!---<br />
'''Resources'''<br />
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper]<br />
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project]<br />
---><br />
<br />
==== October 1: Probability ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&v=rG-SLQ2uF8U Probability introduction] and [https://www.youtube.com/watch?v=HxEz4ZHUY5Y&list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&index=2 Probability trees] OpenIntro lectures (just videos 1 and 2 in the playlist).<br />
* Complete '''exercises from OpenIntro §3:''' 3.12, 3.15, 3.22, 3.28, 3.34, 3.38<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)]<br />
<br />
=== Week 4 (10/6, 10/8) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]<br />
<br />
==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====<br />
'''Required'''<br />
* Read the paper below as well as the attendant [https://www.pnas.org/content/111/29/10779.1 "Expression of editorial concern"] and [https://www.pnas.org/content/111/29/10779.2 "Correction"] that were subsequently appended to it.<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w04-R_tutorial.html Week 4 R tutorial] (as usual, also available as .rmd or .pdf)<br />
<br />
==== October 8: Distributions ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 normal and binomial distributions] OpenIntro lectures (videos 1-3 in the playlist).<br />
* Complete '''exercises from OpenIntro §4:''' 4.4, 4.6, 4.15, 4.22<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)]<br />
<br />
==== October 9: [[#Research project plan and dataset identification|Research project plan and dataset identification]] due by 5pm CT ====<br />
*'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' (due by 5pm CT)<br />
<br />
=== Week 5 (10/13, 10/15) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]<br />
==== October 13: Descriptive analysis and visualization of data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05-R_tutorial.html Week 5 R tutorial] and [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05a-R_tutorial.html Week 5 R tutorial supplement] (both, as usual, also available as .rmd or .pdf).<br />
<br />
==== October 15: Foundations for (frequentist) inference ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). <br />
* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete [https://www.openintro.org/book/stat/why05/ Why .05?] OpenIntro video/exercise.<br />
* Complete '''exercises from OpenIntro §5:''' 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36<br />
<br />
'''Resources'''<br />
* Kelly M., [https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2013.00693.x Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level?] ''Significance'' 10:5. 2013.<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter4 Seeing Theory §4 (Frequentist Inference)]<br />
<br />
=== Week 6 (10/20, 10/22) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]<br />
==== October 20: Reinforced foundations for inference ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|problem set #4]] <br />
* Read Reinhart, §1.<br />
* Revisit the Kramer et al. (2014) paper we read a few weeks ago:<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]] <br />
<br />
==== October 22: Inference for categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete '''exercises from OpenIntro §6:''' 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious)<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo].<br />
<br />
=== Week 7 (10/27, 10/29) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]<br />
==== October 27: Applied inference for categorical data ====<br />
'''Required'''<br />
* Read Reinhart, §4 and §5 (both are quite short).<br />
* Skim the following (all are referenced in the problem set)<br />
** Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. PLoS ONE 13(1): e0189693. [https://doi.org/10.1371/journal.pone.0189693 https://doi.org/10.1371/journal.pone.0189693]<br />
** Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]]<br />
** Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. ''American Behavioral Scientist''. 56(4): 459-487. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|problem set #5]]<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w06-R_tutorial.html Week 06 R tutorial] (it's very short!)<br />
<br />
==== October 29: Inference for numerical data (part 1) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-4 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.12, 7.24, 7.26<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo].<br />
<br />
==== October 30: [[#Research project planning document|Research project planning document]] due 5pm CT====<br />
* Submit via [https://canvas.northwestern.edu/courses/122522/assignments/787297 Canvas] (due by 5pm CT)<br />
<br />
=== Week 8 (11/3, 11/5) ===<br />
==== November 3: U.S. election day (no class meeting) ====<br />
<br />
==== November 4: Interactive self-assessment due ====<br />
* Please submit results [https://canvas.northwestern.edu/courses/122522/assignments/799630 (via Canvas)] from the [https://communitydata.science/~ads/teaching/2020/stats/assessment/interactive_assessment.rmd interactive self-assessment] by 5pm CT.<br />
<br />
==== November 5: Inference for numerical data (part 2) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 4-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.42, 7.44, 7.46<br />
<br />
'''Resources'''<br />
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (useful if you think you'll be doing more ANOVAs).<br />
<br />
=== Week 9 (11/10, 11/12) ===<br />
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan|Session plans]]<br />
<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset6|problem set #6]]<br />
<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w09-R_tutorial.html Week 09 R tutorial]<br />
<br />
==== November 12: Linear regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).<br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §8:''' 8.6, 8.36, 8.40, 8.44<br />
* Complete '''exercises from OpenIntro supplement:''' 4 and 5 (answers provided in the supplement).<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)]<br />
<br />
=== Week 10 (11/17, 11/19) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w10_session_plan|Session plans]]<br />
==== November 17: Applied linear regression ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset7|Problem set #7]]<br />
<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w10-R_tutorial.html Week 10 R tutorial]<br />
==== November 19: Multiple and logistic regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4) <br />
** '''Disclaimer:''' Aaron doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that. <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement).<br />
* Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §9:''' 9.4, 9.13, 9.16, 9.18, <br />
<br />
'''Resources'''<br />
<br />
=== Week 11 (11/24) ===<br />
==== November 24: Applied multiple and logistic regression ====<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w11_session_plan|Session plans]]<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset8|Problem set #8]]<br />
'''Resources'''<br />
* Mako Hill created (and Aaron updated) a brief tutorial on [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]<br />
<br />
=== Week 12+ ===<br />
==== December 1: Post-course assessment of statistical concepts due by 11pm CT ====<br />
Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment] (access code TBA VIA email). Submission deadline: December 1, 11:00pm Chicago time.<br />
==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====<br />
<br />
==== December 10: [[#Research project paper|Research project paper]] due by 5pm CT ====<br />
<br />
== Credit and Notes ==<br />
<br />
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)/w10_session_plan&diff=208560Statistics and Statistical Programming (Fall 2020)/w10 session plan2020-11-17T19:03:20Z<p>Nickmvincent: /* 11/17 Agenda */</p>
<hr />
<div>== 11/17 Agenda ==<br />
* Planning documents<br />
** In general, very good. Mostly we wanted more details about the measures, variables, and how you plan to analyze them. It will really benefit you to get very specific about this sooner rather than later! <br />
** Please come meet with us as you continue to develop these projects. This is a small class. We can provide very "bespoke" attention to your projects. Take advantage of this! We have office hours and can make additional time/appointments as-needed!<br />
** In Aaron's case, I'm also happy to meet after thanksgiving, but the price of admission to any such meeting is a draft table and/or figure with (preliminary) results! ;)<br />
** Another thing I observed that you might consider is identifying a "model paper" that can help serve as a rough template for the kinds of analysis you plan to do and how to report them. Note that Aaron and Nick can help you assess whether your model paper meets whatever expectations/hopes we might have for how you analyze your data and report your results.<br />
** The next deadlines on this project are a (recorded) presentation and the paper itself. We'll talk a bit more about these (likely on Thursday?), but for now please note that I have asked you to submit data + code alongside your finished paper (if possible). If for some reason, doing so is not possible, please get in touch.<br />
* PS7: Univariate regression and the "bread and peace" model of U.S. elections.<br />
** SQ1: walk through interpretation of results.<br />
** SQ2: how normal do residuals need to be??? how to diagnose issues??<br />
<br />
Additional comments from Nick:<br />
* Revisiting confidence intervals for regression coefficients.<br />
** What precisely do they mean?<br />
** Can we use 1.96 x SE?<br />
* If we flip the independent and dependent variables in a bivariate regression analysis: what does that tell us?<br />
* Review exactly what we’re looking for in residual plots.<br />
* (Optional thing Nick thought might be interesting) Everyone suggested a great number of factors our model might be missing (voting public’s perceptions of the economy, attributes of the candidates, effects of pandemic, etc.). How could we go about capturing these? Should we make a new linear regression model and post it on Twitter?</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)/w10_session_plan&diff=208558Statistics and Statistical Programming (Fall 2020)/w10 session plan2020-11-17T18:21:53Z<p>Nickmvincent: Created page with "== 11/17 Agenda == * Revisiting confidence intervals for regression coefficients. ** What precisely do they mean? ** Can we use 1.96 x SE? * If we flip the independent and dep..."</p>
<hr />
<div>== 11/17 Agenda ==<br />
* Revisiting confidence intervals for regression coefficients.<br />
** What precisely do they mean?<br />
** Can we use 1.96 x SE?<br />
* If we flip the independent and dependent variables in a bivariate regression analysis: what does that tell us?<br />
* Review exactly what we’re looking for in residual plots.<br />
* (Optional thing Nick thought might be interesting) Everyone suggested a great number of factors our model might be missing (voting public’s perceptions of the economy, attributes of the candidates, effects of pandemic, etc.). How could we go about capturing these? Should we make a new linear regression model and post it on Twitter?</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&diff=208557Statistics and Statistical Programming (Fall 2020)2020-11-17T18:20:50Z<p>Nickmvincent: /* Week 10 (11/17, 11/19) */ add session plans</p>
<hr />
<div><div style="float:right;" width=30%; class="toclimit-3">__TOC__</div><br />
<br />
;Statistics and Statistical Programming<br />
:Media, Technology & Society (MTS) 525 and Communication Studies 395<br />
:Tuesdays & Thursdays 1-2:50pm CT<br />
:Fall 2020<br />
:Northwestern University<br />
<br />
;Course websites<br />
: [https://canvas.northwestern.edu/courses/122522 Canvas] for [https://canvas.northwestern.edu/courses/122522/announcements announcements], [https://canvas.northwestern.edu/courses/122522/assignments assignments], and some [https://canvas.northwestern.edu/courses/122522/files files].<br />
: [https://northwestern.zoom.us Zoom] for synchronous course meetings.<br />
: [https://discord.com Discord] for discussions and chat.<br />
: [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020) This wiki page] for nearly everything else.<br />
<br />
;'''Instructor:''' [http://aaronshaw.org Aaron Shaw] ([mailto:aaronshaw@northwestern.edu aaronshaw@northwestern.edu])<br />
:Office Hours: Thursday 10am-12pm and by appointment<br />
:Please use [[User:Aaronshaw/OH|office hours signups (with location information)]]<br />
:Also usually available via chat during "business hours."<br />
<br />
:'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu])<br />
::Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.<br />
::I am happy to try out alternative communication software for OH!<br />
<br />
<br><br />
[[File:Datasaurus.gif|left|450px|frame|Image from [https://www.autodeskresearch.com/publications/samestats Matejka and Fitzmaurice, ''CHI'', 2017]|link=https://www.autodeskresearch.com/publications/samestats]]<br />
<br clear=all><br />
<br />
== Course information ==<br />
=== Overview and learning objectives ===<br />
<br />
This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, human computer interaction (HCI), and adjacent disciplines.<br />
<br />
I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:<br />
* Design and execute a quantitative research project that involves statistical inference, start to finish.<br />
* Read, modify, and create short programs in the R statistical programming language.<br />
* Feel comfortable reading and interpreting papers that use basic statistical techniques.<br />
* Feel prepared to enroll in more specialized and advanced statistics courses.<br />
<br />
The course will cover a number of techniques, likely including the following: t-tests; chi-squared tests; ANOVA; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.<br />
<br />
The course materials will consist of readings, problem sets, assessment exercises, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.<br />
<br />
You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover most of the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is not assumed.<br />
<br />
'''Why this course? Why statistical programming? Why R?'''<br />
<br />
Many comparable courses in statistics and quantitative methods do not emphasize statistical programming. So why bother? By learning statistical programming you will gain a deeper understanding of both the principles behind your analysis techniques as well as the tools you use to apply those techniques. In addition, a solid grasp of statistical programming will prepare you to create reproducible research, avoid common errors, and enable both greater durability and validity of your work. <br />
<br />
Other programming languages are also well suited to statistics, including Stata and Python. I do most of my work with R, so that guides my choice for the course. That said, I opt to use and teach with R for a few reasons:<br />
* R is freely available and open source.<br />
* R is the most widely used package in statistics and several social scientific fields.<br />
* R (along with Stata) will be used in most of the advanced stats classes I hope you will take after this course.<br />
* R is better general purpose programming language than Stata which means that R programming skills will let you solve non-statistical problems and may make it easier to learn other programming languages like Python.<br />
<br />
=== Format and structure ===<br />
<!---<br />
I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go.<br />
---><br />
<br />
This course will proceed in a '''remote''' format that includes ''asynchronous'' and ''synchronous'' elements (more on those below). In general, the organization of the course adopts a "flipped" approach where participants consume, discuss, and process instructional materials outside of "class" and we use synchronous meetings to answer questions, address challenges or concerns, work through solutions, and hold semi-structured discussions. <br />
<br />
The course introduces ''both'' basic statistical concepts as well as applications of those concepts through statistical programming. As a result, we will usually dedicate part of each week to a particular set of concepts and part of each week to applied data analysis and/or interpretation. A brief description of how I expect it all to work follows below. We'll talk about it more during the first class session.<br />
<br />
====Asynchronous elements of the course====<br />
<br />
These include all readings, recorded lectures/slides, tutorials, textbook exercises, problem sets, and other assignments. I expect you to complete (or at least attempt to complete!) these outside of our class meeting times. I also strongly encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible.<br />
<br />
We will use Discord for everyday discussions and chat related to the course. In general, the teaching team will try to keep an eye on the various server channels during "business hours." To the extent that we can respond to questions and concerns there, we'll do so. We'll also use the discussion channels to identify topics that might benefit from synchronous conversation during the course meetings. Hopefully, writing and talking about questions and concerns outside of the synchronous course meetings will help support accountability, learning, and more effective use of our meeting time.<br />
<br />
For nearly all of the "instructional" material introducing particular statistical concepts and techniques, you are assigned materials from the OpenIntro textbook and lecture materials created by the textbook authors. Please note that this means I will not deliver lectures during our class meetings. Please also note that this means you are responsible for coordinating your working groups and any collaborative work with other members of the class outside of our class meeting times.<br />
<br />
====Synchronous elements of the course====<br />
<br />
The synchronous elements of the course will be the two weekly class meetings that will happen via video conference (Zoom). These are scheduled to run for a maximum of 110 minutes. Each session will include multiple short breaks. <br />
<br />
We will use the class meetings to discuss and work through any questions or challenges you encounter in the materials assigned for that day. This means that I encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible. Doing so will give the teaching team time to sift, sort, and organize the questions into a hopefully-cohesive plan for each class session that is tailored to the specific concerns you encounter in the material. Obviously, we anticipate that questions will arise during the class sessions too as well and we'll do our best to adapt as we go.<br />
<br />
A couple of other notes about the synchronous course meetings:<br />
* Aaron plans to record the course meetings and have them available to class participants only via Zoom/Canvas. Please get in touch if you have concerns or requests about this. <br />
* The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts.<br />
* You might want to create/acquire something like [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera] to facilitate sharing hand-written notes/drawings during class.<br />
<br />
In addition, because randomness is extremely important in statistics, I may occasionally '''randomly assign''' different working groups to share and discuss their solutions to selected textbook exercises or problem set questions during class. These random assignments will be announced ahead of time so that the group has an opportunity to prepare. The idea here is to structure some participation in the synchronous sessions to ensure an equitable distribution of the responsibility to discuss questions, answers, points of confusion, and alternatives.<br />
<br />
==== Working groups ==== <br />
<br />
At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you may meet outside of class time to discuss, complete, and/or review your weekly assignments (as well as some of the research project assignments). The groups will rotate at least once during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer support, and accountability. While the specifics of exactly when and how you work with your working group will largely be up to you, the teaching team will provide [[Statistics_and_Statistical_Programming_(Fall_2020)/Working_groups_template|suggestions in the form of a template]] that you can use as a starting point.<br />
<br />
As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators. <br />
<br />
<!---<br />
Although the day-to-day routine will vary, each class session will generally include the following:<br />
* Quick updates about assignments, projects, and meta-discussion about the class.<br />
* Discussion of '''programming challenges''' due that day (and related to the previous week's R lecture materials).<br />
* Discussion of '''statistics questions''' related to new material in Diez, Barr, and Çetinkaya-Rundel.<br />
* Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''.<br />
---><br />
<br />
=== Textbook, readings, and resources ===<br />
<br />
This class will use a freely-licensed textbook:<br />
<br />
* Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2019. [https://www.openintro.org/book/os/ ''OpenIntro Statistics'']. 4th edition. OpenIntro, Inc.<br />
<br />
The texbook (in any format) is required for the course. You can [https://www.openintro.org/go?id=os4&referrer=/book/os/index.php download it] at no cost and purchase hard copy versions in either [https://www.openintro.org/go?id=os4_color_pb&referrer=/book/os/index.php full color ($60)] or in [https://www.openintro.org/go?id=os4_bw_pb&referrer=/book/os/index.php black and white ($20)]. The B&W version is very affordable and I strongly recommend buying a hard copy for the purposes of the course and subsequent reference use. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).<br />
<br />
I will also assigning several chapters from the following:<br />
<br />
* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. ([https://search.library.northwestern.edu/primo-explore/fulldisplay?docid=01NWU_ALMA51732460650002441&context=L&vid=NULVNEW&search_scope=NWU&tab=default_tab&lang=en_US Safari online via NU libraries])<br />
<br />
This book provides a readable conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library (you'll need to sign-in and/or use the NU VPN to access it), but you may find it helpful to purchase as well.<br />
<br />
A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:<br />
<br />
* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton UP. ([https://kieranhealy.org/publications/dataviz/ via Healy's website])<br />
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon])<br />
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon])<br />
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources])<br />
* Wickham, Hadly and Grolemund, Garret. 2017. ''R for Data Science''. Sebastopol, CA: O'Reilly. ([https://r4ds.had.co.nz/ Online version]).<br />
<br />
There are also some invaluable non-textbook resources:<br />
<br />
* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!<br />
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.<br />
* [http://rseek.org/ Rseek] — Rseek is a modified version of Google that just searches R websites online. Sometimes, R is hard to search because R is a common letter. This has become much easier over time as R has become more popular, but it can still be an issue sometimes and Rseek is a good solution.<br />
* [https://ggplot2.tidyverse.org/ ggplot2 documentation] — ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.<br />
* [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R.<br />
* [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.<br />
<br />
Computing resources:<br />
* If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]].<br />
<br />
=== Weekly (minor) assignments ===<br />
<br />
In order to support continuous progress towards the learning goals for the course, I have assigned some textbook exercises or a problem set ahead of every class. These assignments will provide the basis on which the teaching team will assess and provide feedback on your participation and engagement with the course material.<br />
<br />
The first week or so of the course is textbook-focused to get us warmed up. Starting in week 2, we will do more statistical programming and apply the textbook concepts using R and RStudio. In general, we will cover the problem sets in the first session of the week and the textbook materials in the second session. <br />
<br />
==== Textbook exercises ====<br />
The focus is on self-assessment of your understanding of the textbook material and you do not need to hand in anything. I expect that you will work on the exercises, review and discuss solutions, and submit any questions ahead of or during class. Please note that solutions to odd-numbered problems appear in the back of the book. The teaching team will distribute solutions to even-numbered problems as well.<br />
<br />
==== Problem sets ====<br />
The course will include problem sets and these may incorporate several kinds of questions:<br />
<br />
* '''Statistics questions''' about statistical concepts and principles.<br />
* '''Programming challenges''' that you should solve using R.<br />
* '''Empirical paper questions''' about other assigned readings. <br />
<br />
For the problem sets, I ask that you submit your work [https://canvas.northwestern.edu/courses/122522/assignments via Canvas 24 hours before class] (i.e., Monday afternoon for our Tuesday class sessions). Details of exactly how this will work will be elaborated during the first class. For the programming challenges, you should submit code and text for your solutions (again, more on how later). If you get completely stuck on a problem, that's okay, but please provide whatever you have.<br />
<br />
Problem sets will be evaluated on a complete/incomplete basis. Although the problem sets will not be assigned a letter grade, they are a central focus of the course and completing them will support your mastery of the material in multiple ways. Working through them on schedule will also make it possible for you to participate in the synchronous course meetings and online discussions of course material effectively. Your ability to do so will figure prominently in your participation grade for the course (see the section on grading and assessment below).<br />
<br />
=== Research project (major) assignments ===<br />
<br />
==== Overview ====<br />
As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:<br />
<br />
* '''Design and describe a plan for a study''' — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.<br />
* '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a analysis of a previously collected dataset.<br />
* '''Engage in descriptive data analysis''' — Use R to calculate descriptive statistics and visualizations to describe your data.<br />
* '''Motivate and test at least one hypothesis about relationships between two or more variables''' — I'm happy to discuss alternatives to formal hypothesis testing procedures (even if some of them are beyond the scope of this course). <br />
* '''Report and interpret your findings''' — You will do this in both a short paper and a short (recorded) presentation.<br />
* '''Ensure that your work is replicable''' — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.<br />
<br />
''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement.<br />
<br />
There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas by 5pm CT on the day they are due.<br />
<br />
<br />
==== Research project plan and dataset identification ====<br />
<br />
;Due date: October 9, 2020, 5pm CT<br />
;Maximum length: 500 words (~1-2 pages)<br />
<br />
Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:<br />
<br />
* An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.<br />
* An identification of the dataset you will use and a description of the rows and columns or type(s) of data it will include. If you do not currently have access to these data, explain why and when you will.<br />
* A short (several sentences?) description of how the project will fit into your career trajectory.<br />
<br />
<br />
===== Notes on finding a dataset =====<br />
<br />
In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:<br />
<br />
* Ask your advisor for a dataset they have collected and used in previous papers. Are there other variables you could use? Other relationships you could analyze?<br />
* If there's an important study you loved, you can send a polite email to the author(s) asking if they are willing and able to share an archival or replication version of the dataset used in their paper. Be very polite and make it clear that this is starting as a class project, but that it might turn into a paper for publication. Make your timeline clear. In Communication and HCI, replication datasets are still very rare, so be prepared for a negative answer and/or questions about your motives in conducting the analysis.<br />
* Do some Google Scholar and normal internet searching for datasets in your research area. You'll probably be surprised at what's available.<br />
* Take a look at datasets available in the [https://dataverse.harvard.edu/ Harvard Dataverse] (a very large collection of social science research data) or one of the other members of the [http://dataverse.org/ Dataverse network].<br />
* Look at the collection of social scientific datasets at [https://www.icpsr.umich.edu/icpsrweb/ICPSR/ ICPSR at the University of Michigan] (NU is a member). There are an enormous number of very rich datasets.<br />
* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.<br />
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.<br />
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.<br />
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].<br />
* The NY Times is publishing a [https://github.com/nytimes/covid-19-data COVID-19 data repository] that includes county-level metrics for deaths, mask usage, and other pandemic-related data. The release a lot of it as frequently updated .csv files and the repository includes documentation of the measurements, data collection details, and more.<br />
* The Community Data Science Collective and colleagues have created a [[COVID-19_Digital_Observatory| COVID-19 digital observatory]] (hosted in part right here on this wiki!) that publishes a bunch of pandemic-related data as csv and json files.<br />
* The [https://openpolicing.stanford.edu Stanford Open Policing project] has published a huge archive of policing data related mostly to traffic stops in states and many cities of the U.S. We'll use at least one of these files for a problem set.<br />
<br />
==== Research project planning document ====<br />
<br />
;Due date: October 30, 2020, 5pm CT<br />
;Suggested length: ~5 pages<br />
<br />
The project planning document is a shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found [[CommunityData:Planning document|on this wiki page]].<br />
<br />
I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course):<br />
* [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work.<br />
* [https://canvas.northwestern.edu/files/9421229/download?download_frd=1 One by Jim Maddock] created as part of a qualifying exam early in 2019. Jim doesn't provide dummy tables or anticipated findings/contributions, but he has an especially phenomenal explanation of the conceptual relationships and processes he wants to test. <br />
* [https://canvas.northwestern.edu/files/9439379/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation.<br />
<br />
==== Research project presentation ====<br />
<br />
;Presentation due date: December 3, 2020, 5pm CT<br />
;Maximum length: 10 minutes<br />
<br />
<!-- TODO revisit old presentations page to update/adapt <br />
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]]<br />
---><br />
You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [https://canvas.northwestern.edu/files/9439377/download?download_frd=1 Creating a Successful Scholarly Presentation] (file posted to Canvas) may be useful.<br />
<br />
Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter.<br />
<br />
==== Research project paper ====<br />
<br />
;Paper due date: December 8, 2020, 5pm CT<br />
;Maximum length: 6000 words (~20 pages)<br />
<br />
I expect you to produce a short, high quality research paper that you might revise, extend, and submit for publication and/or a dissertation milestone. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the [[structure of a quantitative empirical research paper]].<br />
<br />
As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.<br />
<br />
Because the emphasis in this class is on statistics and methods and because I'm probably not an expert in the substance of your research domain, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.<br />
<br />
I have a strong preference for you to write the paper individually, but I'm open to the idea that you may want to work with others in the class. Please contact me ''before'' you attempt to pursue a collaborative final paper.<br />
<br />
I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.<br />
<br />
<br />
==== Human subjects research, IRB, and ethics ====<br />
In general, you are responsible for making sure that you're on the right side of the IRB requirements and that your work meets applicable ethical norms and standards.<br />
<br />
Class projects generally do not need IRB approval, but research for publications, dissertations, and sometimes even pilot studies do fall under IRB purview. You should ''not'' plan to seek IRB approval/determination retroactively. If your study may involve human subjects and you may ever publish it in any form, you will need IRB oversight of some sort.<br />
<br />
Secondary analysis of anonymized data is generally not considered human subjects research, but I strongly suggest that you get a determination from [https://irb.northwestern.edu/ the Northwestern IRB] before you start. For work that is not considered human subjects research, this can often happen in a few hours or days. If you need to list a faculty sponsor or Principal Investigator, that should ideally be your advisor. If that doesn't make sense for some reason, please talk to me.<br />
<br />
Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching.<br />
<br />
=== Grading and assessment ===<br />
<br />
I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course.<br />
<br />
* Weekly participation: 40%<br />
* Proposal identification: 5%<br />
* Final project planning document: 5%<br />
* Final project presentation: 10%<br />
* Final project paper: 40%<br />
<br />
The teaching team will jointly and holistically evaluate your participation along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of [https://mako.cc/teaching/assessment.html Benjamin Mako Hill's assessment page] and [https://reagle.org/joseph/zwiki/Teaching/Assessment/Participation.html Joseph Reagle's participation assessment rubric]. Exceptional participation means excelling along all four dimensions. Please note that participation ≠ talking/typing more and I encourage all of us to seek [https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Balance_in_Discussion.html balance in our discussions].<br />
<br />
The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities.<br />
<br />
=== Policies ===<br />
<br />
==== General course policies ====<br />
<br />
[[User:Aaronshaw/Classroom_policies|General policies]] on a wide variety of topics including classroom equity, attendance, academic integrity, accommodations, late assignments, and more are provided [[User:Aaronshaw/Classroom_policies|on Aaron's class policies page]]. Below are some policy statements specific to this course and quarter.<br />
<br />
==== Teaching and learning in a pandemic ====<br />
<br />
The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways. <br />
<br />
On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and/or distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the past six months and I expect that some of you have too. It is a difficult time.<br />
<br />
I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.<br />
<br />
==== Expectations for synchronous remote sessions ====<br />
<br />
The following are some baseline expectations for our synchronous remote class sessions. I expect that these can and will evolve. Please feel free to ask questions, suggest changes, or raise concerns during the quarter. I welcome all input.<br />
* All members of the class are expected to create a supportive and welcoming environment that is respectful of the conditions under which we are participating in this class.<br />
* All members of the class are expected to take reasonable steps to create an effective teaching/learning environment for themselves and others.<br />
<br />
And here are suggested protocols for any video/audio portions of our class:<br />
* Please mute your microphone whenever you're not speaking and learn to use [https://en.wikipedia.org/wiki/Push-to-talk "push-to-talk"] if/when possible.<br />
* Video is optional for all students at all times, although if you're willing/able to keep the instructor company in the video channel that would be nice.<br />
* If you need to excuse yourself at any time and for any reason you may do so.<br />
* Children, family, pets, roommates, and others with whom you may share your workspace are welcome to join our class as needed.<br />
<br />
==== Syllabus revisions ====<br />
<br />
This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following:<br />
<br />
# '''Assignments and readings are ''frozen'' 1 week before they are due.''' I will not add readings or assignments less than one week before they are due. If I forget to add something or fill in a "To Be Determined" less than one week before it's due, it is dropped. If you plan to read or work more than one week ahead, contact me first.<br />
# '''Substantial changes to the syllabus or course materials will be announced.''' Please closely monitor your email and/or [https://canvas.northwestern.edu the announcements section on the course website on Canvas]. When I make changes, these changes will be recorded in [https://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&action=history the edit history of this page] so that you can track what has changed. I will also do my best to summarize these changes in an announcement on Canvas that will be emailed to everybody in the class.<br />
# '''The course design may adapt throughout the quarter.''' As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again.<br />
<br />
==== Statistics and power ====<br />
<br />
The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my [[User:Aaronshaw/Classroom_policies|general classroom policies]] for more on some of these topics.<br />
<br />
== Schedule (with all the details) ==<br />
<br />
When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n.<br />
<br />
=== Week 1 (9/17) ===<br />
==== September 17: Intro and setup ====<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w01_session_plan|Session plan]]<br />
<br />
<blockquote>''Note: Aaron doesn't actually expect you to complete these before class on September 17''</blockquote><br />
<br />
'''Required'''<br />
* Read this syllabus, discuss any questions/concerns with the teaching team.<br />
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email). Estimated time to do this is 30-40 minutes. '''Submission deadline: September 18, 11:00pm Chicago time'''<br />
* Confirm course registration and access to [https://www.openintro.org/book/os/ the textbook] (pdf download available for $0 and b&w paperbacks for $20) as well as any software and web-services you'll need for course (Zoom, Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email.<br />
* Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set #0] <br />
<br />
'''Recommended'''<br />
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:<br />
** '''From Aaron:''' The [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Week 01 R tutorial] (you should also download the [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.rmd .rmd version of the tutorial] that you can open and read/edit in RStudio). These are accompanied by the R and Rstudio intro screencasts ([https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Part 1] and [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Part 2]) Aaron created for the 2019 version of the course. <br />
** Modern Dive [https://moderndive.netlify.app/index.html Statistical inference via data science] Chapter 1: [https://moderndive.netlify.app/1-getting-started.html Getting started with R].<br />
** [https://rladiessydney.org/courses/ryouwithme/ RYouWithMe] course [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ "Basic basics" 1 & 2] (and maybe 3 if you're feeling ambitious).<br />
** Verzani §1 (Getting started).<br />
** Healy §2 (Get started).<br />
<br />
=== Week 2 (9/22, 9/24) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]<br />
==== September 22: Data and variables ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)].<br />
* Complete '''exercises from OpenIntro §1:''' 1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
==== September 24: Numerical and categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). <br />
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)].<br />
* Complete '''exercises from OpenIntro §2:''' 2.12, 2.13, 2.16, 2.20, 2.23, 2.30 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
=== Week 3 (9/29, 10/1) ===<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]<br />
<br />
==== September 29: R fundamentals: Import, transform, tidy, and describe data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w03-R_tutorial.html Week 3 R tutorial] (note that you can access .rmd or .pdf versions by replacing the suffix of the URL accordingly).<br />
* Additional material from any of the recommended R learning resources suggested last week or elsewhere in the syllabus. In particular, you may find the ModernDive, RYouWithMe, Healy, and/or Wickham and Grolemund resources valuable.<br />
<!---<br />
'''Resources'''<br />
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper]<br />
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project]<br />
---><br />
<br />
==== October 1: Probability ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&v=rG-SLQ2uF8U Probability introduction] and [https://www.youtube.com/watch?v=HxEz4ZHUY5Y&list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&index=2 Probability trees] OpenIntro lectures (just videos 1 and 2 in the playlist).<br />
* Complete '''exercises from OpenIntro §3:''' 3.12, 3.15, 3.22, 3.28, 3.34, 3.38<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)]<br />
<br />
=== Week 4 (10/6, 10/8) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]<br />
<br />
==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====<br />
'''Required'''<br />
* Read the paper below as well as the attendant [https://www.pnas.org/content/111/29/10779.1 "Expression of editorial concern"] and [https://www.pnas.org/content/111/29/10779.2 "Correction"] that were subsequently appended to it.<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w04-R_tutorial.html Week 4 R tutorial] (as usual, also available as .rmd or .pdf)<br />
<br />
==== October 8: Distributions ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 normal and binomial distributions] OpenIntro lectures (videos 1-3 in the playlist).<br />
* Complete '''exercises from OpenIntro §4:''' 4.4, 4.6, 4.15, 4.22<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)]<br />
<br />
==== October 9: [[#Research project plan and dataset identification|Research project plan and dataset identification]] due by 5pm CT ====<br />
*'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' (due by 5pm CT)<br />
<br />
=== Week 5 (10/13, 10/15) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]<br />
==== October 13: Descriptive analysis and visualization of data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05-R_tutorial.html Week 5 R tutorial] and [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05a-R_tutorial.html Week 5 R tutorial supplement] (both, as usual, also available as .rmd or .pdf).<br />
<br />
==== October 15: Foundations for (frequentist) inference ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). <br />
* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete [https://www.openintro.org/book/stat/why05/ Why .05?] OpenIntro video/exercise.<br />
* Complete '''exercises from OpenIntro §5:''' 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36<br />
<br />
'''Resources'''<br />
* Kelly M., [https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2013.00693.x Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level?] ''Significance'' 10:5. 2013.<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter4 Seeing Theory §4 (Frequentist Inference)]<br />
<br />
=== Week 6 (10/20, 10/22) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]<br />
==== October 20: Reinforced foundations for inference ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|problem set #4]] <br />
* Read Reinhart, §1.<br />
* Revisit the Kramer et al. (2014) paper we read a few weeks ago:<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]] <br />
<br />
==== October 22: Inference for categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete '''exercises from OpenIntro §6:''' 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious)<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo].<br />
<br />
=== Week 7 (10/27, 10/29) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]<br />
==== October 27: Applied inference for categorical data ====<br />
'''Required'''<br />
* Read Reinhart, §4 and §5 (both are quite short).<br />
* Skim the following (all are referenced in the problem set)<br />
** Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. PLoS ONE 13(1): e0189693. [https://doi.org/10.1371/journal.pone.0189693 https://doi.org/10.1371/journal.pone.0189693]<br />
** Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]]<br />
** Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. ''American Behavioral Scientist''. 56(4): 459-487. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|problem set #5]]<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w06-R_tutorial.html Week 06 R tutorial] (it's very short!)<br />
<br />
==== October 29: Inference for numerical data (part 1) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-4 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.12, 7.24, 7.26<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo].<br />
<br />
==== October 30: [[#Research project planning document|Research project planning document]] due 5pm CT====<br />
* Submit via [https://canvas.northwestern.edu/courses/122522/assignments/787297 Canvas] (due by 5pm CT)<br />
<br />
=== Week 8 (11/3, 11/5) ===<br />
==== November 3: U.S. election day (no class meeting) ====<br />
<br />
==== November 4: Interactive self-assessment due ====<br />
* Please submit results [https://canvas.northwestern.edu/courses/122522/assignments/799630 (via Canvas)] from the [https://communitydata.science/~ads/teaching/2020/stats/assessment/interactive_assessment.rmd interactive self-assessment] by 5pm CT.<br />
<br />
==== November 5: Inference for numerical data (part 2) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 4-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.42, 7.44, 7.46<br />
<br />
'''Resources'''<br />
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (useful if you think you'll be doing more ANOVAs).<br />
<br />
=== Week 9 (11/10, 11/12) ===<br />
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan|Session plans]]<br />
<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset6|problem set #6]]<br />
<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w09-R_tutorial.html Week 09 R tutorial]<br />
<br />
==== November 12: Linear regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).<br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §8:''' 8.6, 8.36, 8.40, 8.44<br />
* Complete '''exercises from OpenIntro supplement:''' 4 and 5 (answers provided in the supplement).<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)]<br />
<br />
=== Week 10 (11/17, 11/19) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w10_session_plan|Session plans]]<br />
==== November 17: Applied linear regression ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset7|Problem set #7]]<br />
<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w10-R_tutorial.html Week 10 R tutorial]<br />
==== November 19: Multiple and logistic regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4) <br />
** '''Disclaimer:''' Aaron doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that. <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement).<br />
* Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §9:''' 9.4, 9.13, 9.16, 9.18, <br />
<br />
'''Resources'''<br />
<br />
=== Week 11 (11/24) ===<br />
==== November 24: Applied multiple and logistic regression ====<br />
'''Required'''<br />
* Complete Problem set #8<br />
'''Resources'''<br />
* Mako Hill created an example of [https://communitydata.science/~mako/2017-COM521/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]<br />
<br />
=== Week 12+ ===<br />
==== December 1: Post-course assessment of statistical concepts due by 11pm CT ====<br />
Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment] (access code TBA VIA email). Submission deadline: December 1, 11:00pm Chicago time.<br />
==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====<br />
<br />
==== December 10: [[#Research project paper|Research project paper]] due by 5pm CT ====<br />
<br />
== Credit and Notes ==<br />
<br />
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan&diff=208312Statistics and Statistical Programming (Fall 2020)/w09 session plan2020-11-10T18:30:24Z<p>Nickmvincent: /* 11/10 Agenda */ Add Thursday-related items</p>
<hr />
<div>== 11/10 Agenda ==<br />
* Problem Sets<br />
** Quick review on indexing data<br />
*** Many ways to do the same thing<br />
*** Where to look to double check?<br />
** Tons of data viz options in the solutions<br />
*** Histogram bin width<br />
** Questions on SQs?<br />
*** Even though we weren't sure assumptions were totally met for ANOVA, we soldiered on anyway. Thoughts?<br />
** Questions on EQs?<br />
* Thursday<br />
** Linear regression (one X, one Y).<br />
** Supplement is focused on interpretation/prediction.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan&diff=208310Statistics and Statistical Programming (Fall 2020)/w09 session plan2020-11-10T18:20:14Z<p>Nickmvincent: Created page with "== 11/10 Agenda == * Problem Sets ** Quick review on indexing data *** Many ways to do the same thing *** Where to look to double check? ** Tons of data viz options in the sol..."</p>
<hr />
<div>== 11/10 Agenda ==<br />
* Problem Sets<br />
** Quick review on indexing data<br />
*** Many ways to do the same thing<br />
*** Where to look to double check?<br />
** Tons of data viz options in the solutions<br />
*** Histogram bin width<br />
** Questions on SQs?<br />
*** Even though we weren't sure assumptions were totally met for ANOVA, we soldiered on anyway. Thoughts?<br />
** Questions on EQs?</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&diff=208309Statistics and Statistical Programming (Fall 2020)2020-11-10T18:19:38Z<p>Nickmvincent: /* November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) */</p>
<hr />
<div><div style="float:right;" width=30%; class="toclimit-3">__TOC__</div><br />
<br />
;Statistics and Statistical Programming<br />
:Media, Technology & Society (MTS) 525 and Communication Studies 395<br />
:Tuesdays & Thursdays 1-2:50pm CT<br />
:Fall 2020<br />
:Northwestern University<br />
<br />
;Course websites<br />
: [https://canvas.northwestern.edu/courses/122522 Canvas] for [https://canvas.northwestern.edu/courses/122522/announcements announcements], [https://canvas.northwestern.edu/courses/122522/assignments assignments], and some [https://canvas.northwestern.edu/courses/122522/files files].<br />
: [https://northwestern.zoom.us Zoom] for synchronous course meetings.<br />
: [https://discord.com Discord] for discussions and chat.<br />
: [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020) This wiki page] for nearly everything else.<br />
<br />
;'''Instructor:''' [http://aaronshaw.org Aaron Shaw] ([mailto:aaronshaw@northwestern.edu aaronshaw@northwestern.edu])<br />
:Office Hours: Thursday 10am-12pm and by appointment<br />
:Please use [[User:Aaronshaw/OH|office hours signups (with location information)]]<br />
:Also usually available via chat during "business hours."<br />
<br />
:'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu])<br />
::Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.<br />
::I am happy to try out alternative communication software for OH!<br />
<br />
<br><br />
[[File:Datasaurus.gif|left|450px|frame|Image from [https://www.autodeskresearch.com/publications/samestats Matejka and Fitzmaurice, ''CHI'', 2017]|link=https://www.autodeskresearch.com/publications/samestats]]<br />
<br clear=all><br />
<br />
== Course information ==<br />
=== Overview and learning objectives ===<br />
<br />
This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, human computer interaction (HCI), and adjacent disciplines.<br />
<br />
I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:<br />
* Design and execute a quantitative research project that involves statistical inference, start to finish.<br />
* Read, modify, and create short programs in the R statistical programming language.<br />
* Feel comfortable reading and interpreting papers that use basic statistical techniques.<br />
* Feel prepared to enroll in more specialized and advanced statistics courses.<br />
<br />
The course will cover a number of techniques, likely including the following: t-tests; chi-squared tests; ANOVA; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.<br />
<br />
The course materials will consist of readings, problem sets, assessment exercises, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.<br />
<br />
You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover most of the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is not assumed.<br />
<br />
'''Why this course? Why statistical programming? Why R?'''<br />
<br />
Many comparable courses in statistics and quantitative methods do not emphasize statistical programming. So why bother? By learning statistical programming you will gain a deeper understanding of both the principles behind your analysis techniques as well as the tools you use to apply those techniques. In addition, a solid grasp of statistical programming will prepare you to create reproducible research, avoid common errors, and enable both greater durability and validity of your work. <br />
<br />
Other programming languages are also well suited to statistics, including Stata and Python. I do most of my work with R, so that guides my choice for the course. That said, I opt to use and teach with R for a few reasons:<br />
* R is freely available and open source.<br />
* R is the most widely used package in statistics and several social scientific fields.<br />
* R (along with Stata) will be used in most of the advanced stats classes I hope you will take after this course.<br />
* R is better general purpose programming language than Stata which means that R programming skills will let you solve non-statistical problems and may make it easier to learn other programming languages like Python.<br />
<br />
=== Format and structure ===<br />
<!---<br />
I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go.<br />
---><br />
<br />
This course will proceed in a '''remote''' format that includes ''asynchronous'' and ''synchronous'' elements (more on those below). In general, the organization of the course adopts a "flipped" approach where participants consume, discuss, and process instructional materials outside of "class" and we use synchronous meetings to answer questions, address challenges or concerns, work through solutions, and hold semi-structured discussions. <br />
<br />
The course introduces ''both'' basic statistical concepts as well as applications of those concepts through statistical programming. As a result, we will usually dedicate part of each week to a particular set of concepts and part of each week to applied data analysis and/or interpretation. A brief description of how I expect it all to work follows below. We'll talk about it more during the first class session.<br />
<br />
====Asynchronous elements of the course====<br />
<br />
These include all readings, recorded lectures/slides, tutorials, textbook exercises, problem sets, and other assignments. I expect you to complete (or at least attempt to complete!) these outside of our class meeting times. I also strongly encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible.<br />
<br />
We will use Discord for everyday discussions and chat related to the course. In general, the teaching team will try to keep an eye on the various server channels during "business hours." To the extent that we can respond to questions and concerns there, we'll do so. We'll also use the discussion channels to identify topics that might benefit from synchronous conversation during the course meetings. Hopefully, writing and talking about questions and concerns outside of the synchronous course meetings will help support accountability, learning, and more effective use of our meeting time.<br />
<br />
For nearly all of the "instructional" material introducing particular statistical concepts and techniques, you are assigned materials from the OpenIntro textbook and lecture materials created by the textbook authors. Please note that this means I will not deliver lectures during our class meetings. Please also note that this means you are responsible for coordinating your working groups and any collaborative work with other members of the class outside of our class meeting times.<br />
<br />
====Synchronous elements of the course====<br />
<br />
The synchronous elements of the course will be the two weekly class meetings that will happen via video conference (Zoom). These are scheduled to run for a maximum of 110 minutes. Each session will include multiple short breaks. <br />
<br />
We will use the class meetings to discuss and work through any questions or challenges you encounter in the materials assigned for that day. This means that I encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible. Doing so will give the teaching team time to sift, sort, and organize the questions into a hopefully-cohesive plan for each class session that is tailored to the specific concerns you encounter in the material. Obviously, we anticipate that questions will arise during the class sessions too as well and we'll do our best to adapt as we go.<br />
<br />
A couple of other notes about the synchronous course meetings:<br />
* Aaron plans to record the course meetings and have them available to class participants only via Zoom/Canvas. Please get in touch if you have concerns or requests about this. <br />
* The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts.<br />
* You might want to create/acquire something like [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera] to facilitate sharing hand-written notes/drawings during class.<br />
<br />
In addition, because randomness is extremely important in statistics, I may occasionally '''randomly assign''' different working groups to share and discuss their solutions to selected textbook exercises or problem set questions during class. These random assignments will be announced ahead of time so that the group has an opportunity to prepare. The idea here is to structure some participation in the synchronous sessions to ensure an equitable distribution of the responsibility to discuss questions, answers, points of confusion, and alternatives.<br />
<br />
==== Working groups ==== <br />
<br />
At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you may meet outside of class time to discuss, complete, and/or review your weekly assignments (as well as some of the research project assignments). The groups will rotate at least once during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer support, and accountability. While the specifics of exactly when and how you work with your working group will largely be up to you, the teaching team will provide [[Statistics_and_Statistical_Programming_(Fall_2020)/Working_groups_template|suggestions in the form of a template]] that you can use as a starting point.<br />
<br />
As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators. <br />
<br />
<!---<br />
Although the day-to-day routine will vary, each class session will generally include the following:<br />
* Quick updates about assignments, projects, and meta-discussion about the class.<br />
* Discussion of '''programming challenges''' due that day (and related to the previous week's R lecture materials).<br />
* Discussion of '''statistics questions''' related to new material in Diez, Barr, and Çetinkaya-Rundel.<br />
* Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''.<br />
---><br />
<br />
=== Textbook, readings, and resources ===<br />
<br />
This class will use a freely-licensed textbook:<br />
<br />
* Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2019. [https://www.openintro.org/book/os/ ''OpenIntro Statistics'']. 4th edition. OpenIntro, Inc.<br />
<br />
The texbook (in any format) is required for the course. You can [https://www.openintro.org/go?id=os4&referrer=/book/os/index.php download it] at no cost and purchase hard copy versions in either [https://www.openintro.org/go?id=os4_color_pb&referrer=/book/os/index.php full color ($60)] or in [https://www.openintro.org/go?id=os4_bw_pb&referrer=/book/os/index.php black and white ($20)]. The B&W version is very affordable and I strongly recommend buying a hard copy for the purposes of the course and subsequent reference use. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).<br />
<br />
I will also assigning several chapters from the following:<br />
<br />
* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. ([https://search.library.northwestern.edu/primo-explore/fulldisplay?docid=01NWU_ALMA51732460650002441&context=L&vid=NULVNEW&search_scope=NWU&tab=default_tab&lang=en_US Safari online via NU libraries])<br />
<br />
This book provides a readable conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library (you'll need to sign-in and/or use the NU VPN to access it), but you may find it helpful to purchase as well.<br />
<br />
A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:<br />
<br />
* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton UP. ([https://kieranhealy.org/publications/dataviz/ via Healy's website])<br />
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon])<br />
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon])<br />
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources])<br />
* Wickham, Hadly and Grolemund, Garret. 2017. ''R for Data Science''. Sebastopol, CA: O'Reilly. ([https://r4ds.had.co.nz/ Online version]).<br />
<br />
There are also some invaluable non-textbook resources:<br />
<br />
* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!<br />
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.<br />
* [http://rseek.org/ Rseek] — Rseek is a modified version of Google that just searches R websites online. Sometimes, R is hard to search because R is a common letter. This has become much easier over time as R has become more popular, but it can still be an issue sometimes and Rseek is a good solution.<br />
* [https://ggplot2.tidyverse.org/ ggplot2 documentation] — ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.<br />
* [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R.<br />
* [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.<br />
<br />
Computing resources:<br />
* If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]].<br />
<br />
=== Weekly (minor) assignments ===<br />
<br />
In order to support continuous progress towards the learning goals for the course, I have assigned some textbook exercises or a problem set ahead of every class. These assignments will provide the basis on which the teaching team will assess and provide feedback on your participation and engagement with the course material.<br />
<br />
The first week or so of the course is textbook-focused to get us warmed up. Starting in week 2, we will do more statistical programming and apply the textbook concepts using R and RStudio. In general, we will cover the problem sets in the first session of the week and the textbook materials in the second session. <br />
<br />
==== Textbook exercises ====<br />
The focus is on self-assessment of your understanding of the textbook material and you do not need to hand in anything. I expect that you will work on the exercises, review and discuss solutions, and submit any questions ahead of or during class. Please note that solutions to odd-numbered problems appear in the back of the book. The teaching team will distribute solutions to even-numbered problems as well.<br />
<br />
==== Problem sets ====<br />
The course will include problem sets and these may incorporate several kinds of questions:<br />
<br />
* '''Statistics questions''' about statistical concepts and principles.<br />
* '''Programming challenges''' that you should solve using R.<br />
* '''Empirical paper questions''' about other assigned readings. <br />
<br />
For the problem sets, I ask that you submit your work [https://canvas.northwestern.edu/courses/122522/assignments via Canvas 24 hours before class] (i.e., Monday afternoon for our Tuesday class sessions). Details of exactly how this will work will be elaborated during the first class. For the programming challenges, you should submit code and text for your solutions (again, more on how later). If you get completely stuck on a problem, that's okay, but please provide whatever you have.<br />
<br />
Problem sets will be evaluated on a complete/incomplete basis. Although the problem sets will not be assigned a letter grade, they are a central focus of the course and completing them will support your mastery of the material in multiple ways. Working through them on schedule will also make it possible for you to participate in the synchronous course meetings and online discussions of course material effectively. Your ability to do so will figure prominently in your participation grade for the course (see the section on grading and assessment below).<br />
<br />
=== Research project (major) assignments ===<br />
<br />
==== Overview ====<br />
As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:<br />
<br />
* '''Design and describe a plan for a study''' — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.<br />
* '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a analysis of a previously collected dataset.<br />
* '''Engage in descriptive data analysis''' — Use R to calculate descriptive statistics and visualizations to describe your data.<br />
* '''Motivate and test at least one hypothesis about relationships between two or more variables''' — I'm happy to discuss alternatives to formal hypothesis testing procedures (even if some of them are beyond the scope of this course). <br />
* '''Report and interpret your findings''' — You will do this in both a short paper and a short (recorded) presentation.<br />
* '''Ensure that your work is replicable''' — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.<br />
<br />
''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement.<br />
<br />
There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas by 5pm CT on the day they are due.<br />
<br />
<br />
==== Research project plan and dataset identification ====<br />
<br />
;Due date: October 9, 2020, 5pm CT<br />
;Maximum length: 500 words (~1-2 pages)<br />
<br />
Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:<br />
<br />
* An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.<br />
* An identification of the dataset you will use and a description of the rows and columns or type(s) of data it will include. If you do not currently have access to these data, explain why and when you will.<br />
* A short (several sentences?) description of how the project will fit into your career trajectory.<br />
<br />
<br />
===== Notes on finding a dataset =====<br />
<br />
In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:<br />
<br />
* Ask your advisor for a dataset they have collected and used in previous papers. Are there other variables you could use? Other relationships you could analyze?<br />
* If there's an important study you loved, you can send a polite email to the author(s) asking if they are willing and able to share an archival or replication version of the dataset used in their paper. Be very polite and make it clear that this is starting as a class project, but that it might turn into a paper for publication. Make your timeline clear. In Communication and HCI, replication datasets are still very rare, so be prepared for a negative answer and/or questions about your motives in conducting the analysis.<br />
* Do some Google Scholar and normal internet searching for datasets in your research area. You'll probably be surprised at what's available.<br />
* Take a look at datasets available in the [https://dataverse.harvard.edu/ Harvard Dataverse] (a very large collection of social science research data) or one of the other members of the [http://dataverse.org/ Dataverse network].<br />
* Look at the collection of social scientific datasets at [https://www.icpsr.umich.edu/icpsrweb/ICPSR/ ICPSR at the University of Michigan] (NU is a member). There are an enormous number of very rich datasets.<br />
* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.<br />
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.<br />
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.<br />
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].<br />
* The NY Times is publishing a [https://github.com/nytimes/covid-19-data COVID-19 data repository] that includes county-level metrics for deaths, mask usage, and other pandemic-related data. The release a lot of it as frequently updated .csv files and the repository includes documentation of the measurements, data collection details, and more.<br />
* The Community Data Science Collective and colleagues have created a [[COVID-19_Digital_Observatory| COVID-19 digital observatory]] (hosted in part right here on this wiki!) that publishes a bunch of pandemic-related data as csv and json files.<br />
* The [https://openpolicing.stanford.edu Stanford Open Policing project] has published a huge archive of policing data related mostly to traffic stops in states and many cities of the U.S. We'll use at least one of these files for a problem set.<br />
<br />
==== Research project planning document ====<br />
<br />
;Due date: October 30, 2020, 5pm CT<br />
;Suggested length: ~5 pages<br />
<br />
The project planning document is a shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found [[CommunityData:Planning document|on this wiki page]].<br />
<br />
I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course):<br />
* [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work.<br />
* [https://canvas.northwestern.edu/files/9421229/download?download_frd=1 One by Jim Maddock] created as part of a qualifying exam early in 2019. Jim doesn't provide dummy tables or anticipated findings/contributions, but he has an especially phenomenal explanation of the conceptual relationships and processes he wants to test. <br />
* [https://canvas.northwestern.edu/files/9439379/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation.<br />
<br />
==== Research project presentation ====<br />
<br />
;Presentation due date: December 3, 2020, 5pm CT<br />
;Maximum length: 10 minutes<br />
<br />
<!-- TODO revisit old presentations page to update/adapt <br />
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]]<br />
---><br />
You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [https://canvas.northwestern.edu/files/9439377/download?download_frd=1 Creating a Successful Scholarly Presentation] (file posted to Canvas) may be useful.<br />
<br />
Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter.<br />
<br />
==== Research project paper ====<br />
<br />
;Paper due date: December 8, 2020, 5pm CT<br />
;Maximum length: 6000 words (~20 pages)<br />
<br />
I expect you to produce a short, high quality research paper that you might revise, extend, and submit for publication and/or a dissertation milestone. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the [[structure of a quantitative empirical research paper]].<br />
<br />
As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.<br />
<br />
Because the emphasis in this class is on statistics and methods and because I'm probably not an expert in the substance of your research domain, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.<br />
<br />
I have a strong preference for you to write the paper individually, but I'm open to the idea that you may want to work with others in the class. Please contact me ''before'' you attempt to pursue a collaborative final paper.<br />
<br />
I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.<br />
<br />
<br />
==== Human subjects research, IRB, and ethics ====<br />
In general, you are responsible for making sure that you're on the right side of the IRB requirements and that your work meets applicable ethical norms and standards.<br />
<br />
Class projects generally do not need IRB approval, but research for publications, dissertations, and sometimes even pilot studies do fall under IRB purview. You should ''not'' plan to seek IRB approval/determination retroactively. If your study may involve human subjects and you may ever publish it in any form, you will need IRB oversight of some sort.<br />
<br />
Secondary analysis of anonymized data is generally not considered human subjects research, but I strongly suggest that you get a determination from [https://irb.northwestern.edu/ the Northwestern IRB] before you start. For work that is not considered human subjects research, this can often happen in a few hours or days. If you need to list a faculty sponsor or Principal Investigator, that should ideally be your advisor. If that doesn't make sense for some reason, please talk to me.<br />
<br />
Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching.<br />
<br />
=== Grading and assessment ===<br />
<br />
I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course.<br />
<br />
* Weekly participation: 40%<br />
* Proposal identification: 5%<br />
* Final project planning document: 5%<br />
* Final project presentation: 10%<br />
* Final project paper: 40%<br />
<br />
The teaching team will jointly and holistically evaluate your participation along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of [https://mako.cc/teaching/assessment.html Benjamin Mako Hill's assessment page] and [https://reagle.org/joseph/zwiki/Teaching/Assessment/Participation.html Joseph Reagle's participation assessment rubric]. Exceptional participation means excelling along all four dimensions. Please note that participation ≠ talking/typing more and I encourage all of us to seek [https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Balance_in_Discussion.html balance in our discussions].<br />
<br />
The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities.<br />
<br />
=== Policies ===<br />
<br />
==== General course policies ====<br />
<br />
[[User:Aaronshaw/Classroom_policies|General policies]] on a wide variety of topics including classroom equity, attendance, academic integrity, accommodations, late assignments, and more are provided [[User:Aaronshaw/Classroom_policies|on Aaron's class policies page]]. Below are some policy statements specific to this course and quarter.<br />
<br />
==== Teaching and learning in a pandemic ====<br />
<br />
The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways. <br />
<br />
On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and/or distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the past six months and I expect that some of you have too. It is a difficult time.<br />
<br />
I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.<br />
<br />
==== Expectations for synchronous remote sessions ====<br />
<br />
The following are some baseline expectations for our synchronous remote class sessions. I expect that these can and will evolve. Please feel free to ask questions, suggest changes, or raise concerns during the quarter. I welcome all input.<br />
* All members of the class are expected to create a supportive and welcoming environment that is respectful of the conditions under which we are participating in this class.<br />
* All members of the class are expected to take reasonable steps to create an effective teaching/learning environment for themselves and others.<br />
<br />
And here are suggested protocols for any video/audio portions of our class:<br />
* Please mute your microphone whenever you're not speaking and learn to use [https://en.wikipedia.org/wiki/Push-to-talk "push-to-talk"] if/when possible.<br />
* Video is optional for all students at all times, although if you're willing/able to keep the instructor company in the video channel that would be nice.<br />
* If you need to excuse yourself at any time and for any reason you may do so.<br />
* Children, family, pets, roommates, and others with whom you may share your workspace are welcome to join our class as needed.<br />
<br />
==== Syllabus revisions ====<br />
<br />
This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following:<br />
<br />
# '''Assignments and readings are ''frozen'' 1 week before they are due.''' I will not add readings or assignments less than one week before they are due. If I forget to add something or fill in a "To Be Determined" less than one week before it's due, it is dropped. If you plan to read or work more than one week ahead, contact me first.<br />
# '''Substantial changes to the syllabus or course materials will be announced.''' Please closely monitor your email and/or [https://canvas.northwestern.edu the announcements section on the course website on Canvas]. When I make changes, these changes will be recorded in [https://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&action=history the edit history of this page] so that you can track what has changed. I will also do my best to summarize these changes in an announcement on Canvas that will be emailed to everybody in the class.<br />
# '''The course design may adapt throughout the quarter.''' As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again.<br />
<br />
==== Statistics and power ====<br />
<br />
The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my [[User:Aaronshaw/Classroom_policies|general classroom policies]] for more on some of these topics.<br />
<br />
== Schedule (with all the details) ==<br />
<br />
When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n.<br />
<br />
=== Week 1 (9/17) ===<br />
==== September 17: Intro and setup ====<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w01_session_plan|Session plan]]<br />
<br />
<blockquote>''Note: Aaron doesn't actually expect you to complete these before class on September 17''</blockquote><br />
<br />
'''Required'''<br />
* Read this syllabus, discuss any questions/concerns with the teaching team.<br />
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email). Estimated time to do this is 30-40 minutes. '''Submission deadline: September 18, 11:00pm Chicago time'''<br />
* Confirm course registration and access to [https://www.openintro.org/book/os/ the textbook] (pdf download available for $0 and b&w paperbacks for $20) as well as any software and web-services you'll need for course (Zoom, Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email.<br />
* Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set #0] <br />
<br />
'''Recommended'''<br />
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:<br />
** '''From Aaron:''' The [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Week 01 R tutorial] (you should also download the [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.rmd .rmd version of the tutorial] that you can open and read/edit in RStudio). These are accompanied by the R and Rstudio intro screencasts ([https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Part 1] and [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Part 2]) Aaron created for the 2019 version of the course. <br />
** Modern Dive [https://moderndive.netlify.app/index.html Statistical inference via data science] Chapter 1: [https://moderndive.netlify.app/1-getting-started.html Getting started with R].<br />
** [https://rladiessydney.org/courses/ryouwithme/ RYouWithMe] course [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ "Basic basics" 1 & 2] (and maybe 3 if you're feeling ambitious).<br />
** Verzani §1 (Getting started).<br />
** Healy §2 (Get started).<br />
<br />
=== Week 2 (9/22, 9/24) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]<br />
==== September 22: Data and variables ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)].<br />
* Complete '''exercises from OpenIntro §1:''' 1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
==== September 24: Numerical and categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). <br />
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)].<br />
* Complete '''exercises from OpenIntro §2:''' 2.12, 2.13, 2.16, 2.20, 2.23, 2.30 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
=== Week 3 (9/29, 10/1) ===<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]<br />
<br />
==== September 29: R fundamentals: Import, transform, tidy, and describe data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w03-R_tutorial.html Week 3 R tutorial] (note that you can access .rmd or .pdf versions by replacing the suffix of the URL accordingly).<br />
* Additional material from any of the recommended R learning resources suggested last week or elsewhere in the syllabus. In particular, you may find the ModernDive, RYouWithMe, Healy, and/or Wickham and Grolemund resources valuable.<br />
<!---<br />
'''Resources'''<br />
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper]<br />
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project]<br />
---><br />
<br />
==== October 1: Probability ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&v=rG-SLQ2uF8U Probability introduction] and [https://www.youtube.com/watch?v=HxEz4ZHUY5Y&list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&index=2 Probability trees] OpenIntro lectures (just videos 1 and 2 in the playlist).<br />
* Complete '''exercises from OpenIntro §3:''' 3.12, 3.15, 3.22, 3.28, 3.34, 3.38<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)]<br />
<br />
=== Week 4 (10/6, 10/8) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]<br />
<br />
==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====<br />
'''Required'''<br />
* Read the paper below as well as the attendant [https://www.pnas.org/content/111/29/10779.1 "Expression of editorial concern"] and [https://www.pnas.org/content/111/29/10779.2 "Correction"] that were subsequently appended to it.<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w04-R_tutorial.html Week 4 R tutorial] (as usual, also available as .rmd or .pdf)<br />
<br />
==== October 8: Distributions ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 normal and binomial distributions] OpenIntro lectures (videos 1-3 in the playlist).<br />
* Complete '''exercises from OpenIntro §4:''' 4.4, 4.6, 4.15, 4.22<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)]<br />
<br />
==== October 9: [[#Research project plan and dataset identification|Research project plan and dataset identification]] due by 5pm CT ====<br />
*'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' (due by 5pm CT)<br />
<br />
=== Week 5 (10/13, 10/15) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]<br />
==== October 13: Descriptive analysis and visualization of data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05-R_tutorial.html Week 5 R tutorial] and [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05a-R_tutorial.html Week 5 R tutorial supplement] (both, as usual, also available as .rmd or .pdf).<br />
<br />
==== October 15: Foundations for (frequentist) inference ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). <br />
* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete [https://www.openintro.org/book/stat/why05/ Why .05?] OpenIntro video/exercise.<br />
* Complete '''exercises from OpenIntro §5:''' 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36<br />
<br />
'''Resources'''<br />
* Kelly M., [https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2013.00693.x Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level?] ''Significance'' 10:5. 2013.<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter4 Seeing Theory §4 (Frequentist Inference)]<br />
<br />
=== Week 6 (10/20, 10/22) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]<br />
==== October 20: Reinforced foundations for inference ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|problem set #4]] <br />
* Read Reinhart, §1.<br />
* Revisit the Kramer et al. (2014) paper we read a few weeks ago:<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]] <br />
<br />
==== October 22: Inference for categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete '''exercises from OpenIntro §6:''' 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious)<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo].<br />
<br />
=== Week 7 (10/27, 10/29) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]<br />
==== October 27: Applied inference for categorical data ====<br />
'''Required'''<br />
* Read Reinhart, §4 and §5 (both are quite short).<br />
* Skim the following (all are referenced in the problem set)<br />
** Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. PLoS ONE 13(1): e0189693. [https://doi.org/10.1371/journal.pone.0189693 https://doi.org/10.1371/journal.pone.0189693]<br />
** Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]]<br />
** Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. ''American Behavioral Scientist''. 56(4): 459-487. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|problem set #5]]<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w06-R_tutorial.html Week 06 R tutorial] (it's very short!)<br />
<br />
==== October 29: Inference for numerical data (part 1) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-4 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.12, 7.24, 7.26<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo].<br />
<br />
==== October 30: [[#Research project planning document|Research project planning document]] due 5pm CT====<br />
* Submit via [https://canvas.northwestern.edu/courses/122522/assignments/787297 Canvas] (due by 5pm CT)<br />
<br />
=== Week 8 (11/3, 11/5) ===<br />
==== November 3: U.S. election day (no class meeting) ====<br />
<br />
==== November 4: Interactive self-assessment due ====<br />
* Please submit results [https://canvas.northwestern.edu/courses/122522/assignments/799630 (via Canvas)] from the [https://communitydata.science/~ads/teaching/2020/stats/assessment/interactive_assessment.rmd interactive self-assessment] by 5pm CT.<br />
<br />
==== November 5: Inference for numerical data (part 2) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 4-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.42, 7.44, 7.46<br />
<br />
'''Resources'''<br />
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (useful if you think you'll be doing more ANOVAs).<br />
<br />
=== Week 9 (11/10, 11/12) ===<br />
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan|Session plans]]<br />
<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset6|problem set #6]]<br />
<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w09-R_tutorial.html Week 09 R tutorial]<br />
<br />
==== November 12: Linear regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).<br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §8:''' 8.6, 8.36, 8.40, 8.44<br />
* Complete '''exercises from OpenIntro supplement:''' 4 and 5 (answers provided in the supplement).<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)]<br />
<br />
=== Week 10 (11/17, 11/19) ===<br />
==== November 17: Applied linear regression ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset7|Problem set #7]]<br />
<br />
'''Resources'''<br />
<br />
==== November 19: Multiple and logistic regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4) <br />
** '''Disclaimer:''' Aaron doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that. <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement).<br />
* Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §9:''''<br />
* Complete '''exercises from OpenIntro supplements:''''<br />
<br />
'''Resources'''<br />
<br />
=== Week 11 (11/24) ===<br />
==== November 24: Applied multiple and logistic regression ====<br />
'''Required'''<br />
* Complete Problem set #8<br />
'''Resources'''<br />
* Mako Hill created an example of [https://communitydata.science/~mako/2017-COM521/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]<br />
<br />
=== Week 12+ ===<br />
==== December 1: Post-course assessment of statistical concepts due by 11pm CT ====<br />
Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment] (access code TBA VIA email). Submission deadline: December 1, 11:00pm Chicago time.<br />
==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====<br />
<br />
==== December 10: [[#Research project paper|Research project paper]] due by 5pm CT ====<br />
<br />
== Credit and Notes ==<br />
<br />
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&diff=208049Statistics and Statistical Programming (Fall 2020)2020-10-30T20:59:44Z<p>Nickmvincent: /* October 30: Research project planning document due 5pm CT */ add direct link</p>
<hr />
<div><div style="float:right;" width=30%; class="toclimit-3">__TOC__</div><br />
<br />
;Statistics and Statistical Programming<br />
:Media, Technology & Society (MTS) 525 and Communication Studies 395<br />
:Tuesdays & Thursdays 1-2:50pm CT<br />
:Fall 2020<br />
:Northwestern University<br />
<br />
;Course websites<br />
: [https://canvas.northwestern.edu/courses/122522 Canvas] for [https://canvas.northwestern.edu/courses/122522/announcements announcements], [https://canvas.northwestern.edu/courses/122522/assignments assignments], and some [https://canvas.northwestern.edu/courses/122522/files files].<br />
: [https://northwestern.zoom.us Zoom] for synchronous course meetings.<br />
: [https://discord.com Discord] for discussions and chat.<br />
: [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020) This wiki page] for nearly everything else.<br />
<br />
;'''Instructor:''' [http://aaronshaw.org Aaron Shaw] ([mailto:aaronshaw@northwestern.edu aaronshaw@northwestern.edu])<br />
:Office Hours: Thursday 10am-12pm and by appointment<br />
:Please use [[User:Aaronshaw/OH|office hours signups (with location information)]]<br />
:Also usually available via chat during "business hours."<br />
<br />
:'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu])<br />
::Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.<br />
::I am happy to try out alternative communication software for OH!<br />
<br />
<br><br />
[[File:Datasaurus.gif|left|450px|frame|Image from [https://www.autodeskresearch.com/publications/samestats Matejka and Fitzmaurice, ''CHI'', 2017]|link=https://www.autodeskresearch.com/publications/samestats]]<br />
<br clear=all><br />
<br />
== Course information ==<br />
=== Overview and learning objectives ===<br />
<br />
This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, human computer interaction (HCI), and adjacent disciplines.<br />
<br />
I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:<br />
* Design and execute a quantitative research project that involves statistical inference, start to finish.<br />
* Read, modify, and create short programs in the R statistical programming language.<br />
* Feel comfortable reading and interpreting papers that use basic statistical techniques.<br />
* Feel prepared to enroll in more specialized and advanced statistics courses.<br />
<br />
The course will cover a number of techniques, likely including the following: t-tests; chi-squared tests; ANOVA; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.<br />
<br />
The course materials will consist of readings, problem sets, assessment exercises, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.<br />
<br />
You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover most of the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is not assumed.<br />
<br />
'''Why this course? Why statistical programming? Why R?'''<br />
<br />
Many comparable courses in statistics and quantitative methods do not emphasize statistical programming. So why bother? By learning statistical programming you will gain a deeper understanding of both the principles behind your analysis techniques as well as the tools you use to apply those techniques. In addition, a solid grasp of statistical programming will prepare you to create reproducible research, avoid common errors, and enable both greater durability and validity of your work. <br />
<br />
Other programming languages are also well suited to statistics, including Stata and Python. I do most of my work with R, so that guides my choice for the course. That said, I opt to use and teach with R for a few reasons:<br />
* R is freely available and open source.<br />
* R is the most widely used package in statistics and several social scientific fields.<br />
* R (along with Stata) will be used in most of the advanced stats classes I hope you will take after this course.<br />
* R is better general purpose programming language than Stata which means that R programming skills will let you solve non-statistical problems and may make it easier to learn other programming languages like Python.<br />
<br />
=== Format and structure ===<br />
<!---<br />
I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go.<br />
---><br />
<br />
This course will proceed in a '''remote''' format that includes ''asynchronous'' and ''synchronous'' elements (more on those below). In general, the organization of the course adopts a "flipped" approach where participants consume, discuss, and process instructional materials outside of "class" and we use synchronous meetings to answer questions, address challenges or concerns, work through solutions, and hold semi-structured discussions. <br />
<br />
The course introduces ''both'' basic statistical concepts as well as applications of those concepts through statistical programming. As a result, we will usually dedicate part of each week to a particular set of concepts and part of each week to applied data analysis and/or interpretation. A brief description of how I expect it all to work follows below. We'll talk about it more during the first class session.<br />
<br />
====Asynchronous elements of the course====<br />
<br />
These include all readings, recorded lectures/slides, tutorials, textbook exercises, problem sets, and other assignments. I expect you to complete (or at least attempt to complete!) these outside of our class meeting times. I also strongly encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible.<br />
<br />
We will use Discord for everyday discussions and chat related to the course. In general, the teaching team will try to keep an eye on the various server channels during "business hours." To the extent that we can respond to questions and concerns there, we'll do so. We'll also use the discussion channels to identify topics that might benefit from synchronous conversation during the course meetings. Hopefully, writing and talking about questions and concerns outside of the synchronous course meetings will help support accountability, learning, and more effective use of our meeting time.<br />
<br />
For nearly all of the "instructional" material introducing particular statistical concepts and techniques, you are assigned materials from the OpenIntro textbook and lecture materials created by the textbook authors. Please note that this means I will not deliver lectures during our class meetings. Please also note that this means you are responsible for coordinating your working groups and any collaborative work with other members of the class outside of our class meeting times.<br />
<br />
====Synchronous elements of the course====<br />
<br />
The synchronous elements of the course will be the two weekly class meetings that will happen via video conference (Zoom). These are scheduled to run for a maximum of 110 minutes. Each session will include multiple short breaks. <br />
<br />
We will use the class meetings to discuss and work through any questions or challenges you encounter in the materials assigned for that day. This means that I encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible. Doing so will give the teaching team time to sift, sort, and organize the questions into a hopefully-cohesive plan for each class session that is tailored to the specific concerns you encounter in the material. Obviously, we anticipate that questions will arise during the class sessions too as well and we'll do our best to adapt as we go.<br />
<br />
A couple of other notes about the synchronous course meetings:<br />
* Aaron plans to record the course meetings and have them available to class participants only via Zoom/Canvas. Please get in touch if you have concerns or requests about this. <br />
* The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts.<br />
* You might want to create/acquire something like [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera] to facilitate sharing hand-written notes/drawings during class.<br />
<br />
In addition, because randomness is extremely important in statistics, I may occasionally '''randomly assign''' different working groups to share and discuss their solutions to selected textbook exercises or problem set questions during class. These random assignments will be announced ahead of time so that the group has an opportunity to prepare. The idea here is to structure some participation in the synchronous sessions to ensure an equitable distribution of the responsibility to discuss questions, answers, points of confusion, and alternatives.<br />
<br />
==== Working groups ==== <br />
<br />
At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you may meet outside of class time to discuss, complete, and/or review your weekly assignments (as well as some of the research project assignments). The groups will rotate at least once during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer support, and accountability. While the specifics of exactly when and how you work with your working group will largely be up to you, the teaching team will provide [[Statistics_and_Statistical_Programming_(Fall_2020)/Working_groups_template|suggestions in the form of a template]] that you can use as a starting point.<br />
<br />
As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators. <br />
<br />
<!---<br />
Although the day-to-day routine will vary, each class session will generally include the following:<br />
* Quick updates about assignments, projects, and meta-discussion about the class.<br />
* Discussion of '''programming challenges''' due that day (and related to the previous week's R lecture materials).<br />
* Discussion of '''statistics questions''' related to new material in Diez, Barr, and Çetinkaya-Rundel.<br />
* Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''.<br />
---><br />
<br />
=== Textbook, readings, and resources ===<br />
<br />
This class will use a freely-licensed textbook:<br />
<br />
* Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2019. [https://www.openintro.org/book/os/ ''OpenIntro Statistics'']. 4th edition. OpenIntro, Inc.<br />
<br />
The texbook (in any format) is required for the course. You can [https://www.openintro.org/go?id=os4&referrer=/book/os/index.php download it] at no cost and purchase hard copy versions in either [https://www.openintro.org/go?id=os4_color_pb&referrer=/book/os/index.php full color ($60)] or in [https://www.openintro.org/go?id=os4_bw_pb&referrer=/book/os/index.php black and white ($20)]. The B&W version is very affordable and I strongly recommend buying a hard copy for the purposes of the course and subsequent reference use. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).<br />
<br />
I will also assigning several chapters from the following:<br />
<br />
* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. ([https://search.library.northwestern.edu/primo-explore/fulldisplay?docid=01NWU_ALMA51732460650002441&context=L&vid=NULVNEW&search_scope=NWU&tab=default_tab&lang=en_US Safari online via NU libraries])<br />
<br />
This book provides a readable conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library (you'll need to sign-in and/or use the NU VPN to access it), but you may find it helpful to purchase as well.<br />
<br />
A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:<br />
<br />
* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton UP. ([https://kieranhealy.org/publications/dataviz/ via Healy's website])<br />
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon])<br />
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon])<br />
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources])<br />
* Wickham, Hadly and Grolemund, Garret. 2017. ''R for Data Science''. Sebastopol, CA: O'Reilly. ([https://r4ds.had.co.nz/ Online version]).<br />
<br />
There are also some invaluable non-textbook resources:<br />
<br />
* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!<br />
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.<br />
* [http://rseek.org/ Rseek] — Rseek is a modified version of Google that just searches R websites online. Sometimes, R is hard to search because R is a common letter. This has become much easier over time as R has become more popular, but it can still be an issue sometimes and Rseek is a good solution.<br />
* [https://ggplot2.tidyverse.org/ ggplot2 documentation] — ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.<br />
* [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R.<br />
* [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.<br />
<br />
Computing resources:<br />
* If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]].<br />
<br />
=== Weekly (minor) assignments ===<br />
<br />
In order to support continuous progress towards the learning goals for the course, I have assigned some textbook exercises or a problem set ahead of every class. These assignments will provide the basis on which the teaching team will assess and provide feedback on your participation and engagement with the course material.<br />
<br />
The first week or so of the course is textbook-focused to get us warmed up. Starting in week 2, we will do more statistical programming and apply the textbook concepts using R and RStudio. In general, we will cover the problem sets in the first session of the week and the textbook materials in the second session. <br />
<br />
==== Textbook exercises ====<br />
The focus is on self-assessment of your understanding of the textbook material and you do not need to hand in anything. I expect that you will work on the exercises, review and discuss solutions, and submit any questions ahead of or during class. Please note that solutions to odd-numbered problems appear in the back of the book. The teaching team will distribute solutions to even-numbered problems as well.<br />
<br />
==== Problem sets ====<br />
The course will include problem sets and these may incorporate several kinds of questions:<br />
<br />
* '''Statistics questions''' about statistical concepts and principles.<br />
* '''Programming challenges''' that you should solve using R.<br />
* '''Empirical paper questions''' about other assigned readings. <br />
<br />
For the problem sets, I ask that you submit your work [https://canvas.northwestern.edu/courses/122522/assignments via Canvas 24 hours before class] (i.e., Monday afternoon for our Tuesday class sessions). Details of exactly how this will work will be elaborated during the first class. For the programming challenges, you should submit code and text for your solutions (again, more on how later). If you get completely stuck on a problem, that's okay, but please provide whatever you have.<br />
<br />
Problem sets will be evaluated on a complete/incomplete basis. Although the problem sets will not be assigned a letter grade, they are a central focus of the course and completing them will support your mastery of the material in multiple ways. Working through them on schedule will also make it possible for you to participate in the synchronous course meetings and online discussions of course material effectively. Your ability to do so will figure prominently in your participation grade for the course (see the section on grading and assessment below).<br />
<br />
=== Research project (major) assignments ===<br />
<br />
==== Overview ====<br />
As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:<br />
<br />
* '''Design and describe a plan for a study''' — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.<br />
* '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a analysis of a previously collected dataset.<br />
* '''Engage in descriptive data analysis''' — Use R to calculate descriptive statistics and visualizations to describe your data.<br />
* '''Motivate and test at least one hypothesis about relationships between two or more variables''' — I'm happy to discuss alternatives to formal hypothesis testing procedures (even if some of them are beyond the scope of this course). <br />
* '''Report and interpret your findings''' — You will do this in both a short paper and a short (recorded) presentation.<br />
* '''Ensure that your work is replicable''' — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.<br />
<br />
''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement.<br />
<br />
There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas by 5pm CT on the day they are due.<br />
<br />
<br />
==== Research project plan and dataset identification ====<br />
<br />
;Due date: October 9, 2020, 5pm CT<br />
;Maximum length: 500 words (~1-2 pages)<br />
<br />
Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:<br />
<br />
* An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.<br />
* An identification of the dataset you will use and a description of the rows and columns or type(s) of data it will include. If you do not currently have access to these data, explain why and when you will.<br />
* A short (several sentences?) description of how the project will fit into your career trajectory.<br />
<br />
<br />
===== Notes on finding a dataset =====<br />
<br />
In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:<br />
<br />
* Ask your advisor for a dataset they have collected and used in previous papers. Are there other variables you could use? Other relationships you could analyze?<br />
* If there's an important study you loved, you can send a polite email to the author(s) asking if they are willing and able to share an archival or replication version of the dataset used in their paper. Be very polite and make it clear that this is starting as a class project, but that it might turn into a paper for publication. Make your timeline clear. In Communication and HCI, replication datasets are still very rare, so be prepared for a negative answer and/or questions about your motives in conducting the analysis.<br />
* Do some Google Scholar and normal internet searching for datasets in your research area. You'll probably be surprised at what's available.<br />
* Take a look at datasets available in the [https://dataverse.harvard.edu/ Harvard Dataverse] (a very large collection of social science research data) or one of the other members of the [http://dataverse.org/ Dataverse network].<br />
* Look at the collection of social scientific datasets at [https://www.icpsr.umich.edu/icpsrweb/ICPSR/ ICPSR at the University of Michigan] (NU is a member). There are an enormous number of very rich datasets.<br />
* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.<br />
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.<br />
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.<br />
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].<br />
* The NY Times is publishing a [https://github.com/nytimes/covid-19-data COVID-19 data repository] that includes county-level metrics for deaths, mask usage, and other pandemic-related data. The release a lot of it as frequently updated .csv files and the repository includes documentation of the measurements, data collection details, and more.<br />
* The Community Data Science Collective and colleagues have created a [[COVID-19_Digital_Observatory| COVID-19 digital observatory]] (hosted in part right here on this wiki!) that publishes a bunch of pandemic-related data as csv and json files.<br />
* The [https://openpolicing.stanford.edu Stanford Open Policing project] has published a huge archive of policing data related mostly to traffic stops in states and many cities of the U.S. We'll use at least one of these files for a problem set.<br />
<br />
==== Research project planning document ====<br />
<br />
;Due date: October 30, 2020, 5pm CT<br />
;Suggested length: ~5 pages<br />
<br />
The project planning document is a shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found [[CommunityData:Planning document|on this wiki page]].<br />
<br />
I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course):<br />
* [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work.<br />
* [https://canvas.northwestern.edu/files/9421229/download?download_frd=1 One by Jim Maddock] created as part of a qualifying exam early in 2019. Jim doesn't provide dummy tables or anticipated findings/contributions, but he has an especially phenomenal explanation of the conceptual relationships and processes he wants to test. <br />
* [https://canvas.northwestern.edu/files/9439379/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation.<br />
<br />
==== Research project presentation ====<br />
<br />
;Presentation due date: December 3, 2020, 5pm CT<br />
;Maximum length: 10 minutes<br />
<br />
<!-- TODO revisit old presentations page to update/adapt <br />
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]]<br />
---><br />
You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [https://canvas.northwestern.edu/files/9439377/download?download_frd=1 Creating a Successful Scholarly Presentation] (file posted to Canvas) may be useful.<br />
<br />
Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter.<br />
<br />
==== Research project paper ====<br />
<br />
;Paper due date: December 8, 2020, 5pm CT<br />
;Maximum length: 6000 words (~20 pages)<br />
<br />
I expect you to produce a short, high quality research paper that you might revise, extend, and submit for publication and/or a dissertation milestone. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the [[structure of a quantitative empirical research paper]].<br />
<br />
As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.<br />
<br />
Because the emphasis in this class is on statistics and methods and because I'm probably not an expert in the substance of your research domain, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.<br />
<br />
I have a strong preference for you to write the paper individually, but I'm open to the idea that you may want to work with others in the class. Please contact me ''before'' you attempt to pursue a collaborative final paper.<br />
<br />
I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.<br />
<br />
<br />
==== Human subjects research, IRB, and ethics ====<br />
In general, you are responsible for making sure that you're on the right side of the IRB requirements and that your work meets applicable ethical norms and standards.<br />
<br />
Class projects generally do not need IRB approval, but research for publications, dissertations, and sometimes even pilot studies do fall under IRB purview. You should ''not'' plan to seek IRB approval/determination retroactively. If your study may involve human subjects and you may ever publish it in any form, you will need IRB oversight of some sort.<br />
<br />
Secondary analysis of anonymized data is generally not considered human subjects research, but I strongly suggest that you get a determination from [https://irb.northwestern.edu/ the Northwestern IRB] before you start. For work that is not considered human subjects research, this can often happen in a few hours or days. If you need to list a faculty sponsor or Principal Investigator, that should ideally be your advisor. If that doesn't make sense for some reason, please talk to me.<br />
<br />
Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching.<br />
<br />
=== Grading and assessment ===<br />
<br />
I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course.<br />
<br />
* Weekly participation: 40%<br />
* Proposal identification: 5%<br />
* Final project planning document: 5%<br />
* Final project presentation: 10%<br />
* Final project paper: 40%<br />
<br />
The teaching team will jointly and holistically evaluate your participation along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of [https://mako.cc/teaching/assessment.html Benjamin Mako Hill's assessment page] and [https://reagle.org/joseph/zwiki/Teaching/Assessment/Participation.html Joseph Reagle's participation assessment rubric]. Exceptional participation means excelling along all four dimensions. Please note that participation ≠ talking/typing more and I encourage all of us to seek [https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Balance_in_Discussion.html balance in our discussions].<br />
<br />
The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities.<br />
<br />
=== Policies ===<br />
<br />
==== General course policies ====<br />
<br />
[[User:Aaronshaw/Classroom_policies|General policies]] on a wide variety of topics including classroom equity, attendance, academic integrity, accommodations, late assignments, and more are provided [[User:Aaronshaw/Classroom_policies|on Aaron's class policies page]]. Below are some policy statements specific to this course and quarter.<br />
<br />
==== Teaching and learning in a pandemic ====<br />
<br />
The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways. <br />
<br />
On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and/or distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the past six months and I expect that some of you have too. It is a difficult time.<br />
<br />
I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.<br />
<br />
==== Expectations for synchronous remote sessions ====<br />
<br />
The following are some baseline expectations for our synchronous remote class sessions. I expect that these can and will evolve. Please feel free to ask questions, suggest changes, or raise concerns during the quarter. I welcome all input.<br />
* All members of the class are expected to create a supportive and welcoming environment that is respectful of the conditions under which we are participating in this class.<br />
* All members of the class are expected to take reasonable steps to create an effective teaching/learning environment for themselves and others.<br />
<br />
And here are suggested protocols for any video/audio portions of our class:<br />
* Please mute your microphone whenever you're not speaking and learn to use [https://en.wikipedia.org/wiki/Push-to-talk "push-to-talk"] if/when possible.<br />
* Video is optional for all students at all times, although if you're willing/able to keep the instructor company in the video channel that would be nice.<br />
* If you need to excuse yourself at any time and for any reason you may do so.<br />
* Children, family, pets, roommates, and others with whom you may share your workspace are welcome to join our class as needed.<br />
<br />
==== Syllabus revisions ====<br />
<br />
This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following:<br />
<br />
# '''Assignments and readings are ''frozen'' 1 week before they are due.''' I will not add readings or assignments less than one week before they are due. If I forget to add something or fill in a "To Be Determined" less than one week before it's due, it is dropped. If you plan to read or work more than one week ahead, contact me first.<br />
# '''Substantial changes to the syllabus or course materials will be announced.''' Please closely monitor your email and/or [https://canvas.northwestern.edu the announcements section on the course website on Canvas]. When I make changes, these changes will be recorded in [https://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&action=history the edit history of this page] so that you can track what has changed. I will also do my best to summarize these changes in an announcement on Canvas that will be emailed to everybody in the class.<br />
# '''The course design may adapt throughout the quarter.''' As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again.<br />
<br />
==== Statistics and power ====<br />
<br />
The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my [[User:Aaronshaw/Classroom_policies|general classroom policies]] for more on some of these topics.<br />
<br />
== Schedule (with all the details) ==<br />
<br />
When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n.<br />
<br />
=== Week 1 (9/17) ===<br />
==== September 17: Intro and setup ====<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w01_session_plan|Session plan]]<br />
<br />
<blockquote>''Note: Aaron doesn't actually expect you to complete these before class on September 17''</blockquote><br />
<br />
'''Required'''<br />
* Read this syllabus, discuss any questions/concerns with the teaching team.<br />
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email). Estimated time to do this is 30-40 minutes. '''Submission deadline: September 18, 11:00pm Chicago time'''<br />
* Confirm course registration and access to [https://www.openintro.org/book/os/ the textbook] (pdf download available for $0 and b&w paperbacks for $20) as well as any software and web-services you'll need for course (Zoom, Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email.<br />
* Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set #0] <br />
<br />
'''Recommended'''<br />
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:<br />
** '''From Aaron:''' The [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Week 01 R tutorial] (you should also download the [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.rmd .rmd version of the tutorial] that you can open and read/edit in RStudio). These are accompanied by the R and Rstudio intro screencasts ([https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Part 1] and [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Part 2]) Aaron created for the 2019 version of the course. <br />
** Modern Dive [https://moderndive.netlify.app/index.html Statistical inference via data science] Chapter 1: [https://moderndive.netlify.app/1-getting-started.html Getting started with R].<br />
** [https://rladiessydney.org/courses/ryouwithme/ RYouWithMe] course [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ "Basic basics" 1 & 2] (and maybe 3 if you're feeling ambitious).<br />
** Verzani §1 (Getting started).<br />
** Healy §2 (Get started).<br />
<br />
=== Week 2 (9/22, 9/24) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]<br />
==== September 22: Data and variables ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)].<br />
* Complete '''exercises from OpenIntro §1:''' 1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
==== September 24: Numerical and categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). <br />
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)].<br />
* Complete '''exercises from OpenIntro §2:''' 2.12, 2.13, 2.16, 2.20, 2.23, 2.30 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
=== Week 3 (9/29, 10/1) ===<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]<br />
<br />
==== September 29: R fundamentals: Import, transform, tidy, and describe data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w03-R_tutorial.html Week 3 R tutorial] (note that you can access .rmd or .pdf versions by replacing the suffix of the URL accordingly).<br />
* Additional material from any of the recommended R learning resources suggested last week or elsewhere in the syllabus. In particular, you may find the ModernDive, RYouWithMe, Healy, and/or Wickham and Grolemund resources valuable.<br />
<!---<br />
'''Resources'''<br />
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper]<br />
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project]<br />
---><br />
<br />
==== October 1: Probability ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&v=rG-SLQ2uF8U Probability introduction] and [https://www.youtube.com/watch?v=HxEz4ZHUY5Y&list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&index=2 Probability trees] OpenIntro lectures (just videos 1 and 2 in the playlist).<br />
* Complete '''exercises from OpenIntro §3:''' 3.12, 3.15, 3.22, 3.28, 3.34, 3.38<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)]<br />
<br />
=== Week 4 (10/6, 10/8) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]<br />
<br />
==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====<br />
'''Required'''<br />
* Read the paper below as well as the attendant [https://www.pnas.org/content/111/29/10779.1 "Expression of editorial concern"] and [https://www.pnas.org/content/111/29/10779.2 "Correction"] that were subsequently appended to it.<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w04-R_tutorial.html Week 4 R tutorial] (as usual, also available as .rmd or .pdf)<br />
<br />
==== October 8: Distributions ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 normal and binomial distributions] OpenIntro lectures (videos 1-3 in the playlist).<br />
* Complete '''exercises from OpenIntro §4:''' 4.4, 4.6, 4.15, 4.22<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)]<br />
<br />
==== October 9: [[#Research project plan and dataset identification|Research project plan and dataset identification]] due by 5pm CT ====<br />
*'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' (due by 5pm CT)<br />
<br />
=== Week 5 (10/13, 10/15) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]<br />
==== October 13: Descriptive analysis and visualization of data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05-R_tutorial.html Week 5 R tutorial] and [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05a-R_tutorial.html Week 5 R tutorial supplement] (both, as usual, also available as .rmd or .pdf).<br />
<br />
==== October 15: Foundations for (frequentist) inference ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). <br />
* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete [https://www.openintro.org/book/stat/why05/ Why .05?] OpenIntro video/exercise.<br />
* Complete '''exercises from OpenIntro §5:''' 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36<br />
<br />
'''Resources'''<br />
* Kelly M., [https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2013.00693.x Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level?] ''Significance'' 10:5. 2013.<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter4 Seeing Theory §4 (Frequentist Inference)]<br />
<br />
=== Week 6 (10/20, 10/22) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]<br />
==== October 20: Reinforced foundations for inference ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|problem set #4]] <br />
* Read Reinhart, §1.<br />
* Revisit the Kramer et al. (2014) paper we read a few weeks ago:<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]] <br />
<br />
==== October 22: Inference for categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete '''exercises from OpenIntro §6:''' 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious)<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo].<br />
<br />
=== Week 7 (10/27, 10/29) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]<br />
==== October 27: Applied inference for categorical data ====<br />
'''Required'''<br />
* Read Reinhart, §4 and §5 (both are quite short).<br />
* Skim the following (all are referenced in the problem set)<br />
** Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. PLoS ONE 13(1): e0189693. [https://doi.org/10.1371/journal.pone.0189693 https://doi.org/10.1371/journal.pone.0189693]<br />
** Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]]<br />
** Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. ''American Behavioral Scientist''. 56(4): 459-487. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|problem set #5]]<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w06-R_tutorial.html Week 06 R tutorial] (it's very short!)<br />
<br />
==== October 29: Inference for numerical data (part 1) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-4 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.12, 7.24, 7.26<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo].<br />
<br />
==== October 30: [[#Research project planning document|Research project planning document]] due 5pm CT====<br />
* Submit via [https://canvas.northwestern.edu/courses/122522/assignments/787297 Canvas] (due by 5pm CT)<br />
<br />
=== Week 8 (11/3, 11/5) ===<br />
==== November 3: U.S. election day (no class meeting) ====<br />
<br />
==== November 4: Interactive self-assessment due ====<br />
* Please submit results [https://canvas.northwestern.edu/courses/122522/assignments/799630 (via Canvas)] from the [https://communitydata.science/~ads/teaching/2020/stats/assessment/interactive_assessment.rmd interactive self-assessment] by 5pm CT.<br />
<br />
==== November 5: Inference for numerical data (part 2) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 4-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.42, 7.44, 7.46<br />
<br />
'''Resources'''<br />
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (useful if you think you'll be doing more ANOVAs).<br />
<br />
=== Week 9 (11/10, 11/12) ===<br />
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====<br />
'''Required'''<br />
* Complete problem set #6<br />
<br />
'''Resources'''<br />
<br />
==== November 12: Linear regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).<br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §8:'''<br />
* Complete '''exercises from OpenIntro supplement:'''<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)]<br />
<br />
=== Week 10 (11/17, 11/19) ===<br />
==== November 17: <Topic> ====<br />
'''Required'''<br />
* Complete Problem set #7<br />
<br />
'''Resources'''<br />
<br />
==== November 19: Multiple and logistic regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4) <br />
** '''Disclaimer:''' Aaron doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that. <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement).<br />
* Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §9:''''<br />
* Complete '''exercises from OpenIntro supplements:''''<br />
<br />
'''Resources'''<br />
<br />
=== Week 11 (11/24) ===<br />
==== November 24: <Topic> and assessment ====<br />
'''Required'''<br />
* Complete Problem set #8<br />
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment of statistical concepts] (access code TBA VIA email). '''Submission deadline: December 1, 11:00pm Chicago time'''<br />
'''Resources'''<br />
* Mako Hill created an example of [https://communitydata.science/~mako/2017-COM521/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]<br />
<br />
=== Week 12+ ===<br />
==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====<br />
<br />
==== December 10: [[#Research project paper|Research project paper]] due by 5pm CT ====<br />
<br />
== Credit and Notes ==<br />
<br />
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.</div>Nickmvincenthttps://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&diff=208003Statistics and Statistical Programming (Fall 2020)2020-10-29T18:04:38Z<p>Nickmvincent: /* November 4: Interactive self-assessment due */ add canvas link</p>
<hr />
<div><div style="float:right;" width=30%; class="toclimit-3">__TOC__</div><br />
<br />
;Statistics and Statistical Programming<br />
:Media, Technology & Society (MTS) 525 and Communication Studies 395<br />
:Tuesdays & Thursdays 1-2:50pm CT<br />
:Fall 2020<br />
:Northwestern University<br />
<br />
;Course websites<br />
: [https://canvas.northwestern.edu/courses/122522 Canvas] for [https://canvas.northwestern.edu/courses/122522/announcements announcements], [https://canvas.northwestern.edu/courses/122522/assignments assignments], and some [https://canvas.northwestern.edu/courses/122522/files files].<br />
: [https://northwestern.zoom.us Zoom] for synchronous course meetings.<br />
: [https://discord.com Discord] for discussions and chat.<br />
: [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020) This wiki page] for nearly everything else.<br />
<br />
;'''Instructor:''' [http://aaronshaw.org Aaron Shaw] ([mailto:aaronshaw@northwestern.edu aaronshaw@northwestern.edu])<br />
:Office Hours: Thursday 10am-12pm and by appointment<br />
:Please use [[User:Aaronshaw/OH|office hours signups (with location information)]]<br />
:Also usually available via chat during "business hours."<br />
<br />
:'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu])<br />
::Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.<br />
::I am happy to try out alternative communication software for OH!<br />
<br />
<br><br />
[[File:Datasaurus.gif|left|450px|frame|Image from [https://www.autodeskresearch.com/publications/samestats Matejka and Fitzmaurice, ''CHI'', 2017]|link=https://www.autodeskresearch.com/publications/samestats]]<br />
<br clear=all><br />
<br />
== Course information ==<br />
=== Overview and learning objectives ===<br />
<br />
This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, human computer interaction (HCI), and adjacent disciplines.<br />
<br />
I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:<br />
* Design and execute a quantitative research project that involves statistical inference, start to finish.<br />
* Read, modify, and create short programs in the R statistical programming language.<br />
* Feel comfortable reading and interpreting papers that use basic statistical techniques.<br />
* Feel prepared to enroll in more specialized and advanced statistics courses.<br />
<br />
The course will cover a number of techniques, likely including the following: t-tests; chi-squared tests; ANOVA; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.<br />
<br />
The course materials will consist of readings, problem sets, assessment exercises, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.<br />
<br />
You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover most of the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is not assumed.<br />
<br />
'''Why this course? Why statistical programming? Why R?'''<br />
<br />
Many comparable courses in statistics and quantitative methods do not emphasize statistical programming. So why bother? By learning statistical programming you will gain a deeper understanding of both the principles behind your analysis techniques as well as the tools you use to apply those techniques. In addition, a solid grasp of statistical programming will prepare you to create reproducible research, avoid common errors, and enable both greater durability and validity of your work. <br />
<br />
Other programming languages are also well suited to statistics, including Stata and Python. I do most of my work with R, so that guides my choice for the course. That said, I opt to use and teach with R for a few reasons:<br />
* R is freely available and open source.<br />
* R is the most widely used package in statistics and several social scientific fields.<br />
* R (along with Stata) will be used in most of the advanced stats classes I hope you will take after this course.<br />
* R is better general purpose programming language than Stata which means that R programming skills will let you solve non-statistical problems and may make it easier to learn other programming languages like Python.<br />
<br />
=== Format and structure ===<br />
<!---<br />
I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go.<br />
---><br />
<br />
This course will proceed in a '''remote''' format that includes ''asynchronous'' and ''synchronous'' elements (more on those below). In general, the organization of the course adopts a "flipped" approach where participants consume, discuss, and process instructional materials outside of "class" and we use synchronous meetings to answer questions, address challenges or concerns, work through solutions, and hold semi-structured discussions. <br />
<br />
The course introduces ''both'' basic statistical concepts as well as applications of those concepts through statistical programming. As a result, we will usually dedicate part of each week to a particular set of concepts and part of each week to applied data analysis and/or interpretation. A brief description of how I expect it all to work follows below. We'll talk about it more during the first class session.<br />
<br />
====Asynchronous elements of the course====<br />
<br />
These include all readings, recorded lectures/slides, tutorials, textbook exercises, problem sets, and other assignments. I expect you to complete (or at least attempt to complete!) these outside of our class meeting times. I also strongly encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible.<br />
<br />
We will use Discord for everyday discussions and chat related to the course. In general, the teaching team will try to keep an eye on the various server channels during "business hours." To the extent that we can respond to questions and concerns there, we'll do so. We'll also use the discussion channels to identify topics that might benefit from synchronous conversation during the course meetings. Hopefully, writing and talking about questions and concerns outside of the synchronous course meetings will help support accountability, learning, and more effective use of our meeting time.<br />
<br />
For nearly all of the "instructional" material introducing particular statistical concepts and techniques, you are assigned materials from the OpenIntro textbook and lecture materials created by the textbook authors. Please note that this means I will not deliver lectures during our class meetings. Please also note that this means you are responsible for coordinating your working groups and any collaborative work with other members of the class outside of our class meeting times.<br />
<br />
====Synchronous elements of the course====<br />
<br />
The synchronous elements of the course will be the two weekly class meetings that will happen via video conference (Zoom). These are scheduled to run for a maximum of 110 minutes. Each session will include multiple short breaks. <br />
<br />
We will use the class meetings to discuss and work through any questions or challenges you encounter in the materials assigned for that day. This means that I encourage you to identify, submit, and discuss questions about the material '''before each class meeting''' whenever possible. Doing so will give the teaching team time to sift, sort, and organize the questions into a hopefully-cohesive plan for each class session that is tailored to the specific concerns you encounter in the material. Obviously, we anticipate that questions will arise during the class sessions too as well and we'll do our best to adapt as we go.<br />
<br />
A couple of other notes about the synchronous course meetings:<br />
* Aaron plans to record the course meetings and have them available to class participants only via Zoom/Canvas. Please get in touch if you have concerns or requests about this. <br />
* The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts.<br />
* You might want to create/acquire something like [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera] to facilitate sharing hand-written notes/drawings during class.<br />
<br />
In addition, because randomness is extremely important in statistics, I may occasionally '''randomly assign''' different working groups to share and discuss their solutions to selected textbook exercises or problem set questions during class. These random assignments will be announced ahead of time so that the group has an opportunity to prepare. The idea here is to structure some participation in the synchronous sessions to ensure an equitable distribution of the responsibility to discuss questions, answers, points of confusion, and alternatives.<br />
<br />
==== Working groups ==== <br />
<br />
At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you may meet outside of class time to discuss, complete, and/or review your weekly assignments (as well as some of the research project assignments). The groups will rotate at least once during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer support, and accountability. While the specifics of exactly when and how you work with your working group will largely be up to you, the teaching team will provide [[Statistics_and_Statistical_Programming_(Fall_2020)/Working_groups_template|suggestions in the form of a template]] that you can use as a starting point.<br />
<br />
As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators. <br />
<br />
<!---<br />
Although the day-to-day routine will vary, each class session will generally include the following:<br />
* Quick updates about assignments, projects, and meta-discussion about the class.<br />
* Discussion of '''programming challenges''' due that day (and related to the previous week's R lecture materials).<br />
* Discussion of '''statistics questions''' related to new material in Diez, Barr, and Çetinkaya-Rundel.<br />
* Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''.<br />
---><br />
<br />
=== Textbook, readings, and resources ===<br />
<br />
This class will use a freely-licensed textbook:<br />
<br />
* Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2019. [https://www.openintro.org/book/os/ ''OpenIntro Statistics'']. 4th edition. OpenIntro, Inc.<br />
<br />
The texbook (in any format) is required for the course. You can [https://www.openintro.org/go?id=os4&referrer=/book/os/index.php download it] at no cost and purchase hard copy versions in either [https://www.openintro.org/go?id=os4_color_pb&referrer=/book/os/index.php full color ($60)] or in [https://www.openintro.org/go?id=os4_bw_pb&referrer=/book/os/index.php black and white ($20)]. The B&W version is very affordable and I strongly recommend buying a hard copy for the purposes of the course and subsequent reference use. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).<br />
<br />
I will also assigning several chapters from the following:<br />
<br />
* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. ([https://search.library.northwestern.edu/primo-explore/fulldisplay?docid=01NWU_ALMA51732460650002441&context=L&vid=NULVNEW&search_scope=NWU&tab=default_tab&lang=en_US Safari online via NU libraries])<br />
<br />
This book provides a readable conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library (you'll need to sign-in and/or use the NU VPN to access it), but you may find it helpful to purchase as well.<br />
<br />
A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:<br />
<br />
* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton UP. ([https://kieranhealy.org/publications/dataviz/ via Healy's website])<br />
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon])<br />
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon])<br />
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources])<br />
* Wickham, Hadly and Grolemund, Garret. 2017. ''R for Data Science''. Sebastopol, CA: O'Reilly. ([https://r4ds.had.co.nz/ Online version]).<br />
<br />
There are also some invaluable non-textbook resources:<br />
<br />
* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!<br />
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.<br />
* [http://rseek.org/ Rseek] — Rseek is a modified version of Google that just searches R websites online. Sometimes, R is hard to search because R is a common letter. This has become much easier over time as R has become more popular, but it can still be an issue sometimes and Rseek is a good solution.<br />
* [https://ggplot2.tidyverse.org/ ggplot2 documentation] — ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.<br />
* [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R.<br />
* [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.<br />
<br />
Computing resources:<br />
* If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]].<br />
<br />
=== Weekly (minor) assignments ===<br />
<br />
In order to support continuous progress towards the learning goals for the course, I have assigned some textbook exercises or a problem set ahead of every class. These assignments will provide the basis on which the teaching team will assess and provide feedback on your participation and engagement with the course material.<br />
<br />
The first week or so of the course is textbook-focused to get us warmed up. Starting in week 2, we will do more statistical programming and apply the textbook concepts using R and RStudio. In general, we will cover the problem sets in the first session of the week and the textbook materials in the second session. <br />
<br />
==== Textbook exercises ====<br />
The focus is on self-assessment of your understanding of the textbook material and you do not need to hand in anything. I expect that you will work on the exercises, review and discuss solutions, and submit any questions ahead of or during class. Please note that solutions to odd-numbered problems appear in the back of the book. The teaching team will distribute solutions to even-numbered problems as well.<br />
<br />
==== Problem sets ====<br />
The course will include problem sets and these may incorporate several kinds of questions:<br />
<br />
* '''Statistics questions''' about statistical concepts and principles.<br />
* '''Programming challenges''' that you should solve using R.<br />
* '''Empirical paper questions''' about other assigned readings. <br />
<br />
For the problem sets, I ask that you submit your work [https://canvas.northwestern.edu/courses/122522/assignments via Canvas 24 hours before class] (i.e., Monday afternoon for our Tuesday class sessions). Details of exactly how this will work will be elaborated during the first class. For the programming challenges, you should submit code and text for your solutions (again, more on how later). If you get completely stuck on a problem, that's okay, but please provide whatever you have.<br />
<br />
Problem sets will be evaluated on a complete/incomplete basis. Although the problem sets will not be assigned a letter grade, they are a central focus of the course and completing them will support your mastery of the material in multiple ways. Working through them on schedule will also make it possible for you to participate in the synchronous course meetings and online discussions of course material effectively. Your ability to do so will figure prominently in your participation grade for the course (see the section on grading and assessment below).<br />
<br />
=== Research project (major) assignments ===<br />
<br />
==== Overview ====<br />
As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:<br />
<br />
* '''Design and describe a plan for a study''' — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.<br />
* '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a analysis of a previously collected dataset.<br />
* '''Engage in descriptive data analysis''' — Use R to calculate descriptive statistics and visualizations to describe your data.<br />
* '''Motivate and test at least one hypothesis about relationships between two or more variables''' — I'm happy to discuss alternatives to formal hypothesis testing procedures (even if some of them are beyond the scope of this course). <br />
* '''Report and interpret your findings''' — You will do this in both a short paper and a short (recorded) presentation.<br />
* '''Ensure that your work is replicable''' — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.<br />
<br />
''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement.<br />
<br />
There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas by 5pm CT on the day they are due.<br />
<br />
<br />
==== Research project plan and dataset identification ====<br />
<br />
;Due date: October 9, 2020, 5pm CT<br />
;Maximum length: 500 words (~1-2 pages)<br />
<br />
Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:<br />
<br />
* An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.<br />
* An identification of the dataset you will use and a description of the rows and columns or type(s) of data it will include. If you do not currently have access to these data, explain why and when you will.<br />
* A short (several sentences?) description of how the project will fit into your career trajectory.<br />
<br />
<br />
===== Notes on finding a dataset =====<br />
<br />
In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:<br />
<br />
* Ask your advisor for a dataset they have collected and used in previous papers. Are there other variables you could use? Other relationships you could analyze?<br />
* If there's an important study you loved, you can send a polite email to the author(s) asking if they are willing and able to share an archival or replication version of the dataset used in their paper. Be very polite and make it clear that this is starting as a class project, but that it might turn into a paper for publication. Make your timeline clear. In Communication and HCI, replication datasets are still very rare, so be prepared for a negative answer and/or questions about your motives in conducting the analysis.<br />
* Do some Google Scholar and normal internet searching for datasets in your research area. You'll probably be surprised at what's available.<br />
* Take a look at datasets available in the [https://dataverse.harvard.edu/ Harvard Dataverse] (a very large collection of social science research data) or one of the other members of the [http://dataverse.org/ Dataverse network].<br />
* Look at the collection of social scientific datasets at [https://www.icpsr.umich.edu/icpsrweb/ICPSR/ ICPSR at the University of Michigan] (NU is a member). There are an enormous number of very rich datasets.<br />
* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.<br />
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.<br />
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.<br />
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].<br />
* The NY Times is publishing a [https://github.com/nytimes/covid-19-data COVID-19 data repository] that includes county-level metrics for deaths, mask usage, and other pandemic-related data. The release a lot of it as frequently updated .csv files and the repository includes documentation of the measurements, data collection details, and more.<br />
* The Community Data Science Collective and colleagues have created a [[COVID-19_Digital_Observatory| COVID-19 digital observatory]] (hosted in part right here on this wiki!) that publishes a bunch of pandemic-related data as csv and json files.<br />
* The [https://openpolicing.stanford.edu Stanford Open Policing project] has published a huge archive of policing data related mostly to traffic stops in states and many cities of the U.S. We'll use at least one of these files for a problem set.<br />
<br />
==== Research project planning document ====<br />
<br />
;Due date: October 30, 2020, 5pm CT<br />
;Suggested length: ~5 pages<br />
<br />
The project planning document is a shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found [[CommunityData:Planning document|on this wiki page]].<br />
<br />
I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course):<br />
* [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work.<br />
* [https://canvas.northwestern.edu/files/9421229/download?download_frd=1 One by Jim Maddock] created as part of a qualifying exam early in 2019. Jim doesn't provide dummy tables or anticipated findings/contributions, but he has an especially phenomenal explanation of the conceptual relationships and processes he wants to test. <br />
* [https://canvas.northwestern.edu/files/9439379/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation.<br />
<br />
==== Research project presentation ====<br />
<br />
;Presentation due date: December 3, 2020, 5pm CT<br />
;Maximum length: 10 minutes<br />
<br />
<!-- TODO revisit old presentations page to update/adapt <br />
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]]<br />
---><br />
You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [https://canvas.northwestern.edu/files/9439377/download?download_frd=1 Creating a Successful Scholarly Presentation] (file posted to Canvas) may be useful.<br />
<br />
Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter.<br />
<br />
==== Research project paper ====<br />
<br />
;Paper due date: December 8, 2020, 5pm CT<br />
;Maximum length: 6000 words (~20 pages)<br />
<br />
I expect you to produce a short, high quality research paper that you might revise, extend, and submit for publication and/or a dissertation milestone. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the [[structure of a quantitative empirical research paper]].<br />
<br />
As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.<br />
<br />
Because the emphasis in this class is on statistics and methods and because I'm probably not an expert in the substance of your research domain, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.<br />
<br />
I have a strong preference for you to write the paper individually, but I'm open to the idea that you may want to work with others in the class. Please contact me ''before'' you attempt to pursue a collaborative final paper.<br />
<br />
I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.<br />
<br />
<br />
==== Human subjects research, IRB, and ethics ====<br />
In general, you are responsible for making sure that you're on the right side of the IRB requirements and that your work meets applicable ethical norms and standards.<br />
<br />
Class projects generally do not need IRB approval, but research for publications, dissertations, and sometimes even pilot studies do fall under IRB purview. You should ''not'' plan to seek IRB approval/determination retroactively. If your study may involve human subjects and you may ever publish it in any form, you will need IRB oversight of some sort.<br />
<br />
Secondary analysis of anonymized data is generally not considered human subjects research, but I strongly suggest that you get a determination from [https://irb.northwestern.edu/ the Northwestern IRB] before you start. For work that is not considered human subjects research, this can often happen in a few hours or days. If you need to list a faculty sponsor or Principal Investigator, that should ideally be your advisor. If that doesn't make sense for some reason, please talk to me.<br />
<br />
Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching.<br />
<br />
=== Grading and assessment ===<br />
<br />
I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course.<br />
<br />
* Weekly participation: 40%<br />
* Proposal identification: 5%<br />
* Final project planning document: 5%<br />
* Final project presentation: 10%<br />
* Final project paper: 40%<br />
<br />
The teaching team will jointly and holistically evaluate your participation along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of [https://mako.cc/teaching/assessment.html Benjamin Mako Hill's assessment page] and [https://reagle.org/joseph/zwiki/Teaching/Assessment/Participation.html Joseph Reagle's participation assessment rubric]. Exceptional participation means excelling along all four dimensions. Please note that participation ≠ talking/typing more and I encourage all of us to seek [https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Balance_in_Discussion.html balance in our discussions].<br />
<br />
The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities.<br />
<br />
=== Policies ===<br />
<br />
==== General course policies ====<br />
<br />
[[User:Aaronshaw/Classroom_policies|General policies]] on a wide variety of topics including classroom equity, attendance, academic integrity, accommodations, late assignments, and more are provided [[User:Aaronshaw/Classroom_policies|on Aaron's class policies page]]. Below are some policy statements specific to this course and quarter.<br />
<br />
==== Teaching and learning in a pandemic ====<br />
<br />
The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways. <br />
<br />
On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and/or distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the past six months and I expect that some of you have too. It is a difficult time.<br />
<br />
I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.<br />
<br />
==== Expectations for synchronous remote sessions ====<br />
<br />
The following are some baseline expectations for our synchronous remote class sessions. I expect that these can and will evolve. Please feel free to ask questions, suggest changes, or raise concerns during the quarter. I welcome all input.<br />
* All members of the class are expected to create a supportive and welcoming environment that is respectful of the conditions under which we are participating in this class.<br />
* All members of the class are expected to take reasonable steps to create an effective teaching/learning environment for themselves and others.<br />
<br />
And here are suggested protocols for any video/audio portions of our class:<br />
* Please mute your microphone whenever you're not speaking and learn to use [https://en.wikipedia.org/wiki/Push-to-talk "push-to-talk"] if/when possible.<br />
* Video is optional for all students at all times, although if you're willing/able to keep the instructor company in the video channel that would be nice.<br />
* If you need to excuse yourself at any time and for any reason you may do so.<br />
* Children, family, pets, roommates, and others with whom you may share your workspace are welcome to join our class as needed.<br />
<br />
==== Syllabus revisions ====<br />
<br />
This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following:<br />
<br />
# '''Assignments and readings are ''frozen'' 1 week before they are due.''' I will not add readings or assignments less than one week before they are due. If I forget to add something or fill in a "To Be Determined" less than one week before it's due, it is dropped. If you plan to read or work more than one week ahead, contact me first.<br />
# '''Substantial changes to the syllabus or course materials will be announced.''' Please closely monitor your email and/or [https://canvas.northwestern.edu the announcements section on the course website on Canvas]. When I make changes, these changes will be recorded in [https://wiki.communitydata.science/index.php?title=Statistics_and_Statistical_Programming_(Fall_2020)&action=history the edit history of this page] so that you can track what has changed. I will also do my best to summarize these changes in an announcement on Canvas that will be emailed to everybody in the class.<br />
# '''The course design may adapt throughout the quarter.''' As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again.<br />
<br />
==== Statistics and power ====<br />
<br />
The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my [[User:Aaronshaw/Classroom_policies|general classroom policies]] for more on some of these topics.<br />
<br />
== Schedule (with all the details) ==<br />
<br />
When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n.<br />
<br />
=== Week 1 (9/17) ===<br />
==== September 17: Intro and setup ====<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w01_session_plan|Session plan]]<br />
<br />
<blockquote>''Note: Aaron doesn't actually expect you to complete these before class on September 17''</blockquote><br />
<br />
'''Required'''<br />
* Read this syllabus, discuss any questions/concerns with the teaching team.<br />
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email). Estimated time to do this is 30-40 minutes. '''Submission deadline: September 18, 11:00pm Chicago time'''<br />
* Confirm course registration and access to [https://www.openintro.org/book/os/ the textbook] (pdf download available for $0 and b&w paperbacks for $20) as well as any software and web-services you'll need for course (Zoom, Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email.<br />
* Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set #0] <br />
<br />
'''Recommended'''<br />
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:<br />
** '''From Aaron:''' The [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Week 01 R tutorial] (you should also download the [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.rmd .rmd version of the tutorial] that you can open and read/edit in RStudio). These are accompanied by the R and Rstudio intro screencasts ([https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Part 1] and [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Part 2]) Aaron created for the 2019 version of the course. <br />
** Modern Dive [https://moderndive.netlify.app/index.html Statistical inference via data science] Chapter 1: [https://moderndive.netlify.app/1-getting-started.html Getting started with R].<br />
** [https://rladiessydney.org/courses/ryouwithme/ RYouWithMe] course [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ "Basic basics" 1 & 2] (and maybe 3 if you're feeling ambitious).<br />
** Verzani §1 (Getting started).<br />
** Healy §2 (Get started).<br />
<br />
=== Week 2 (9/22, 9/24) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]<br />
==== September 22: Data and variables ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)].<br />
* Complete '''exercises from OpenIntro §1:''' 1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
==== September 24: Numerical and categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). <br />
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)].<br />
* Complete '''exercises from OpenIntro §2:''' 2.12, 2.13, 2.16, 2.20, 2.23, 2.30 (and remember that solutions to odd-numbered problems are in the book!)<br />
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.<br />
<br />
=== Week 3 (9/29, 10/1) ===<br />
<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]<br />
<br />
==== September 29: R fundamentals: Import, transform, tidy, and describe data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w03-R_tutorial.html Week 3 R tutorial] (note that you can access .rmd or .pdf versions by replacing the suffix of the URL accordingly).<br />
* Additional material from any of the recommended R learning resources suggested last week or elsewhere in the syllabus. In particular, you may find the ModernDive, RYouWithMe, Healy, and/or Wickham and Grolemund resources valuable.<br />
<!---<br />
'''Resources'''<br />
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper]<br />
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project]<br />
---><br />
<br />
==== October 1: Probability ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&v=rG-SLQ2uF8U Probability introduction] and [https://www.youtube.com/watch?v=HxEz4ZHUY5Y&list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&index=2 Probability trees] OpenIntro lectures (just videos 1 and 2 in the playlist).<br />
* Complete '''exercises from OpenIntro §3:''' 3.12, 3.15, 3.22, 3.28, 3.34, 3.38<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)]<br />
<br />
=== Week 4 (10/6, 10/8) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]<br />
<br />
==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====<br />
'''Required'''<br />
* Read the paper below as well as the attendant [https://www.pnas.org/content/111/29/10779.1 "Expression of editorial concern"] and [https://www.pnas.org/content/111/29/10779.2 "Correction"] that were subsequently appended to it.<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w04-R_tutorial.html Week 4 R tutorial] (as usual, also available as .rmd or .pdf)<br />
<br />
==== October 8: Distributions ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 normal and binomial distributions] OpenIntro lectures (videos 1-3 in the playlist).<br />
* Complete '''exercises from OpenIntro §4:''' 4.4, 4.6, 4.15, 4.22<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)]<br />
<br />
==== October 9: [[#Research project plan and dataset identification|Research project plan and dataset identification]] due by 5pm CT ====<br />
*'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' (due by 5pm CT)<br />
<br />
=== Week 5 (10/13, 10/15) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]<br />
==== October 13: Descriptive analysis and visualization of data ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)<br />
<br />
'''Recommended'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05-R_tutorial.html Week 5 R tutorial] and [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05a-R_tutorial.html Week 5 R tutorial supplement] (both, as usual, also available as .rmd or .pdf).<br />
<br />
==== October 15: Foundations for (frequentist) inference ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). <br />
* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete [https://www.openintro.org/book/stat/why05/ Why .05?] OpenIntro video/exercise.<br />
* Complete '''exercises from OpenIntro §5:''' 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36<br />
<br />
'''Resources'''<br />
* Kelly M., [https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2013.00693.x Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level?] ''Significance'' 10:5. 2013.<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter4 Seeing Theory §4 (Frequentist Inference)]<br />
<br />
=== Week 6 (10/20, 10/22) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]<br />
==== October 20: Reinforced foundations for inference ====<br />
'''Required'''<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|problem set #4]] <br />
* Read Reinhart, §1.<br />
* Revisit the Kramer et al. (2014) paper we read a few weeks ago:<br />
:Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open access]] <br />
<br />
==== October 22: Inference for categorical data ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures.<br />
* Complete '''exercises from OpenIntro §6:''' 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious)<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo].<br />
<br />
=== Week 7 (10/27, 10/29) ===<br />
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]<br />
==== October 27: Applied inference for categorical data ====<br />
'''Required'''<br />
* Read Reinhart, §4 and §5 (both are quite short).<br />
* Skim the following (all are referenced in the problem set)<br />
** Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. PLoS ONE 13(1): e0189693. [https://doi.org/10.1371/journal.pone.0189693 https://doi.org/10.1371/journal.pone.0189693]<br />
** Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]]<br />
** Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. ''American Behavioral Scientist''. 56(4): 459-487. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]]<br />
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|problem set #5]]<br />
'''Resources'''<br />
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w06-R_tutorial.html Week 06 R tutorial] (it's very short!)<br />
<br />
==== October 29: Inference for numerical data (part 1) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-4 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.12, 7.24, 7.26<br />
<br />
'''Resources'''<br />
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo].<br />
<br />
==== October 30: [[#Research project planning document|Research project planning document]] due 5pm CT====<br />
* Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas] (due by 5pm CT)<br />
<br />
=== Week 8 (11/3, 11/5) ===<br />
==== November 3: U.S. election day (no class meeting) ====<br />
<br />
==== November 4: Interactive self-assessment due ====<br />
* Please submit results [https://canvas.northwestern.edu/courses/122522/assignments/799630 (via Canvas)] from the [https://communitydata.science/~ads/teaching/2020/stats/assessment/interactive_assessment.rmd interactive self-assessment] by 5pm CT.<br />
<br />
==== November 5: Inference for numerical data (part 2) ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons). <br />
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 4-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).<br />
* Complete '''exercises from OpenIntro §7:''' 7.42, 7.44, 7.46<br />
<br />
'''Resources'''<br />
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (useful if you think you'll be doing more ANOVAs).<br />
<br />
=== Week 9 (11/10, 11/12) ===<br />
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====<br />
'''Required'''<br />
* Complete problem set #6<br />
<br />
'''Resources'''<br />
<br />
==== November 12: Linear regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).<br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §8:'''<br />
* Complete '''exercises from OpenIntro supplement:'''<br />
<br />
'''Resources'''<br />
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)]<br />
<br />
=== Week 10 (11/17, 11/19) ===<br />
==== November 17: <Topic> ====<br />
'''Required'''<br />
* Complete Problem set #7<br />
<br />
'''Resources'''<br />
<br />
==== November 19: Multiple and logistic regression ====<br />
'''Required'''<br />
* Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4) <br />
** '''Disclaimer:''' Aaron doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that. <br />
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures.<br />
* Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement).<br />
* Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement).<br />
* Complete '''exercises from OpenIntro §9:''''<br />
* Complete '''exercises from OpenIntro supplements:''''<br />
<br />
'''Resources'''<br />
<br />
=== Week 11 (11/24) ===<br />
==== November 24: <Topic> and assessment ====<br />
'''Required'''<br />
* Complete Problem set #8<br />
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment of statistical concepts] (access code TBA VIA email). '''Submission deadline: December 1, 11:00pm Chicago time'''<br />
'''Resources'''<br />
* Mako Hill created an example of [https://communitydata.science/~mako/2017-COM521/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]<br />
<br />
=== Week 12+ ===<br />
==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====<br />
<br />
==== December 10: [[#Research project paper|Research project paper]] due by 5pm CT ====<br />
<br />
== Credit and Notes ==<br />
<br />
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.</div>Nickmvincent