Editing HCDS (Fall 2017)/Schedule

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=== Week 1: September 28 ===
[[HCDS_(Fall_2017)/Day_1_plan|Day 1 plan]]

;Assignments due
* fill out the pre-course survey

;Agenda
{{:HCDS (Fall 2017)/Day 1 plan}}

;Readings assigned
* Watch:  [https://www.hcde.washington.edu/seminar-series/aragon ''Why Humans Should Care About Data Science''] (Cecilia Aragon, 2016 HCDE Seminar Series)
* Read: Aragon, C. et al. (2016). [https://cscw2016hcds.files.wordpress.com/2015/10/cscw_2016_human-centered-data-science_workshop.pdf ''Developing a Research Agenda for Human-Centered Data Science.''] Human Centered Data Science workshop, CSCW 2016.
* Read: Provost, Foster, and Tom Fawcett. [http://online.liebertpub.com/doi/pdf/10.1089/big.2013.1508 ''Data science and its relationship to big data and data-driven decision making.''] Big Data 1.1 (2013): 51-59. 
* Read: Kling, Rob and Star, Susan Leigh. [https://scholarworks.iu.edu/dspace/bitstream/handle/2022/1798/wp97-04B.html ''Human Centered Systems in the Perspective of Organizational and Social Informatics.''] 1997.

;Homework assigned
* Reading reflection

;Resources
* Ideo.org [http://www.designkit.org/ ''The Field Guide to Human-Centered Design.''] 2015.
* Faraway, Julian. [https://farawaystatistics.wordpress.com/2015/11/30/the-decline-and-fall-of-statistics/ ''The Decline and Fall of Statistics.''] Faraway Statistics, 2015.
* Press, Gil. [https://www.forbes.com/sites/gilpress/2013/08/19/data-science-whats-the-half-life-of-a-buzzword/#387161dd7bfd Data Science: What's The Half-Life Of A Buzzword?''] Forbes, 2013.
* Bloor, Robin. [http://insideanalysis.com/2013/08/a-data-science-rant/ ''A Data Science Rant.''] Inside Analysis, 2013.
* Various authors. [https://cscw2016hcds.wordpress.com/papers/ Position papers from 2016 CSCW Human Centered Data Science Workshop.] 2016.
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=== Week 2: October 5 ===
[[HCDS_(Fall_2017)/Day_2_plan|Day 2 plan]]

Ethical considerations in Data Science: privacy, informed consent and user treatment


;Assignments due
*Week 1 reading reflection

;Agenda
{{:HCDS (Fall 2017)/Day 2 plan}}


;Readings assigned
* Read: Markham, Annette and Buchanan, Elizabeth. [https://aoir.org/reports/ethics2.pdf ''Ethical Decision-Making and Internet Researchers.''] Association for Internet Research, 2012.
* Read: Barocas, Solan and Nissenbaum, Helen. ''Big Data's End Run around Anonymity and Consent.'' In ''Privacy, Big Data, and the Public Good''. 2014. (PDF on Canvas)

;Homework assigned
* Reading reflection

;Resources
* Wittkower, D.E. [http://firstmonday.org/article/view/6948/5628 Lurkers, creepers, and virtuous interactivity: From property rights to consent and care as a conceptual basis for privacy concerns and information ethics]
* National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. [https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html ''The Belmont Report.''] U.S. Department of Health and Human Services, 1979.
* Hill, Kashmir. [https://www.forbes.com/sites/kashmirhill/2014/06/28/facebook-manipulated-689003-users-emotions-for-science/#6a01653e197c ''Facebook Manipulated 689,003 Users' Emotions For Science.''] Forbes, 2014.
* Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. [http://www.pnas.org/content/111/24/8788.full ''Hancock Experimental evidence of massive-scale emotional contagion through social networks.''] PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
* Barbaro, Michael and Zeller, Tom. [http://query.nytimes.com/gst/abstract.html?res=9E0CE3DD1F3FF93AA3575BC0A9609C8B63&legacy=true ''A Face Is Exposed for AOL Searcher No. 4417749.''] New York Times, 2008.
* Zetter, Kim. [https://www.wired.com/2012/06/wmw-arvind-narayanan/ ''Arvind Narayanan Isn’t Anonymous, and Neither Are You.''] WIRED, 2012.
* Gray, Mary. [http://culturedigitally.org/2014/07/when-science-customer-service-and-human-subjects-research-collide-now-what/ ''When Science, Customer Service, and Human Subjects Research Collide. Now What?''] Culture Digitally, 2014.
* Tene, Omer and Polonetsky, Jules. [https://www.stanfordlawreview.org/online/privacy-paradox-privacy-and-big-data/ ''Privacy in the Age of Big Data.''] Stanford Law Review, 2012.
* Dwork, Cynthia. [https://www.microsoft.com/en-us/research/wp-content/uploads/2008/04/dwork_tamc.pdf ''Differential Privacy: A survey of results'']. Theory and Applications of Models of Computation , 2008.
* Green, Matthew. [https://blog.cryptographyengineering.com/2016/06/15/what-is-differential-privacy/ ''What is Differential Privacy?''] A Few Thoughts on Cryptographic Engineering, 2016.
* Hsu, Danny. [http://blog.datasift.com/2015/04/09/techniques-to-anonymize-human-data/ ''Techniques to Anonymize Human Data.''] Data Sift, 2015.
* Metcalf, Jacob. [http://ethicalresolve.com/twelve-principles-of-data-ethics/ ''Twelve principles of data ethics'']. Ethical Resolve, 2016.
* Poor, Nathaniel and Davidson, Roei. [https://cscw2016hcds.files.wordpress.com/2015/10/poor_hcds20161.pdf ''When The Data You Want Comes From Hackers, Or, Looking A Gift Horse In The Mouth'']. CSCW Human Centered Data Science Workshop, 2016.
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=== Week 3: October 12 ===
[[HCDS_(Fall_2017)/Day_3_plan|Day 3 plan]]

;Data provenance, preparation, and reproducibility: ''data curation, preservation, documentation, and archiving; best practices for open scientific research''

;Assignments due
* Week 2 reading reflection

;Agenda
{{:HCDS (Fall 2017)/Day 3 plan}}

;Readings assigned 
*TBD

;Homework assigned
* Reading reflection
* [[HCDS_(Fall_2017)/Assignments#A1:_Data_curation|A1: Data curation]]


;Resources
* Press, Gil. [https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2608257f6f63 ''Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says.''] Forbes, 2016.
* (''sections TBD''): Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
* Chapman et al. [ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf ''Cross Industry Standard Process for Data Mining'']. IBM, 2000.


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=== Week 4: October 19 ===
[[HCDS_(Fall_2017)/Day_4_plan|Day 4 plan]]

;Study design: ''understanding your data; framing research questions; planning your study''


;Assignments due
* Reading reflection
* A1: Data curation

;Agenda
{{:HCDS (Fall 2017)/Day 4 plan}}

;Readings assigned

;Homework assigned
* Reading reflection
* A2: Bias in data


;Resources
* Aschwanden, Christie. [https://fivethirtyeight.com/features/science-isnt-broken/ ''Science Isn't Broken''] FiveThirtyEight, 2015.
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=== Week 5: October 26 ===
[[HCDS_(Fall_2017)/Day_5_plan|Day 5 plan]]

;Machine learning: ''ethical AI, algorithmic transparency, societal implications of machine learning''

;Assignments due
* Reading reflection
* A2: Bias in data

;Agenda
{{:HCDS (Fall 2017)/Day 5 plan}}

;Readings assigned

;Homework assigned
* Reading reflection
* A3: Final project plan


;Resources

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<hr/>
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=== Week 6: November 2 ===
[[HCDS_(Fall_2017)/Day_6_plan|Day 6 plan]]

;Mixed-methods research: ''Big data vs thick data; qualitative research in data science ''


;Assignments due
* Reading reflection


;Agenda
{{:HCDS (Fall 2017)/Day 6 plan}}


;Readings assigned

;Homework assigned
* Reading reflection


;Resources

<br/>
<hr/>
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=== Week 7: November 9 ===
[[HCDS_(Fall_2017)/Day_7_plan|Day 7 plan]]

;Human computation: ''ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation''


;Assignments due
* Reading reflection
* A3: Final project plan


;Agenda
{{:HCDS (Fall 2017)/Day 7 plan}}

;Readings assigned

;Homework assigned
* Reading reflection
* A4: Crowdwork self-ethnography


;Resources
*''go here''

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<hr/>
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=== Week 8: November 16 ===
[[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]]

;User experience and big data: ''prototyping and user testing; benchmarking and iterative evaluation; UI design for data science''


;Assignments due
* Reading reflection

;Agenda
{{:HCDS (Fall 2017)/Day 8 plan}}

;Readings assigned

;Homework assigned
* Reading reflection


;Resources

<br/>
<hr/>
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=== Week 9: November 23 ===
[[HCDS_(Fall_2017)/Day_8_plan|Day 9 plan]]

;Human-centered data science in the wild: ''community data science; data science for social good''

;Assignments due
* Reading reflection
* A4: Crowdwork self-ethnography

;Agenda
{{:HCDS (Fall 2017)/Day 9 plan}}

;Readings assigned

;Homework assigned
* Reading reflection

;Resources

<br/>
<hr/>
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=== Week 10: November 30 ===
[[HCDS_(Fall_2017)/Day_10_plan|Day 10 plan]]

;Communicating methods, results, and implications: translating for non-data scientists ''


;Assignments due
* Reading reflection


;Agenda
{{:HCDS (Fall 2017)/Day 10 plan}}

;Readings assigned

;Homework assigned
* Reading reflection
* A5: Final presentation

;Resources
* ''one''

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<hr/>
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=== Week 11: December 7 ===
[[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]]

;Future of human centered data science: ''case studies from research, industry, and policy; final presentations''


;Assignments due
* Reading reflection
* A5: Final presentation


;Agenda
{{:HCDS (Fall 2017)/Day 11 plan}}

;Readings assigned
* none!

;Homework assigned
* none!

;Resources
* ''one''

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<hr/>
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=== Week 12: Finals Week ===
* NO CLASS
* A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 10
* LATE PROJECT SUBMISSIONS NOT ACCEPTED.


[[Category:HCDS (Fall 2017)]]
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 === Week 1: September 28 ===
 [[HCDS_(Fall_2017)/Day_1_plan|Day 1 plan]]
-[[:File:HCDS Week 1 slides.pdf|Day 1 slides]]
-;Course overview: ''What is data science? What is human centered? What is human centered data science?''
 ;Assignments due
@@ Line 56: / Line 50: @@
 [[HCDS_(Fall_2017)/Day_2_plan|Day 2 plan]]
-[[:File:HCDS Week 2 slides.pdf|Day 2 slides]]
+Ethical considerations in Data Science: privacy, informed consent and user treatment
-;Ethical considerations in Data Science: ''privacy, informed consent and user treatment''
@@ Line 95: / Line 87: @@
 === Week 3: October 12 ===
 [[HCDS_(Fall_2017)/Day_3_plan|Day 3 plan]]
-[[:File:HCDS Week 3 slides.pdf|Day 3 slides]]
 ;Data provenance, preparation, and reproducibility: ''data curation, preservation, documentation, and archiving; best practices for open scientific research''
@@ Line 107: / Line 97: @@
 ;Readings assigned
-*Read: Chapter 2 [https://www.practicereproducibleresearch.org/core-chapters/2-assessment.html "Assessing Reproducibility"] and Chapter 3 [https://www.practicereproducibleresearch.org/core-chapters/3-basic.html "The Basic Reproducible Workflow Template"] from ''The Practice of Reproducible Research'' University of California Press, 2018.
+*TBD
-* Read: Hickey, Walt. [https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/ ''The Dollars and Cents Case Against Hollywood's Exclusion of Women.''] FiveThirtyEight, 2014. '''AND''' Keegan, Brian. [https://github.com/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb ''The Need for Openness in Data Journalism.''] 2014.
 ;Homework assigned
@@ Line 114: / Line 103: @@
 * [[HCDS_(Fall_2017)/Assignments#A1:_Data_curation|A1: Data curation]]
-;Examples of well-documented open research projects
-* Keegan, Brian. [https://github.com/brianckeegan/WeatherCrime ''WeatherCrime'']. GitHub, 2014.
-* Geiger, Stuart R. and Halfaker, Aaron. [https://github.com/halfak/are-the-bots-really-fighting ''Operationalizing conflict and cooperation between automated software agents in Wikipedia: A replication and expansion of "Even Good Bots Fight"'']. GitHub, 2017.
-* Thain, Nithum; Dixon, Lucas; and Wulczyn, Ellery. [https://figshare.com/articles/Wikipedia_Talk_Labels_Toxicity/4563973 ''Wikipedia Talk Labels: Toxicity'']. Figshare, 2017.
-* Narayan, Sneha et al. [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6HPRIG ''Replication Data for: The Wikipedia Adventure: Field Evaluation of an Interactive Tutorial for New Users'']. Harvard Dataverse, 2017.
-;Examples of not-so-well documented open research projects
+;Resources
-* Eclarke. [https://github.com/eclarke/swga_paper SWGA paper]. GitHub, 2016.
-* David Lefevre. [https://figshare.com/articles/Lefevre_and_Cox_Delayed_instructional_feedback_may_be_more_effective_but_is_this_contrary_to_learners_preferences_/2061303 ''Lefevre and Cox: Delayed instructional feedback may be more effective, but is this contrary to learners’ preferences?''] Figshare, 2016.
-* Alneberg. [https://github.com/BinPro/paper-data ''CONCOCT Paper Data'']. GitHub, 2014.
-;Other resources
 * Press, Gil. [https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2608257f6f63 ''Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says.''] Forbes, 2016.
-* Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
+* (''sections TBD''): Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
-* Hickey, Walt. [https://fivethirtyeight.com/features/the-bechdel-test-checking-our-work/ ''The Bechdel Test: Checking Our Work'']. FiveThirtyEight, 2014.
 * Chapman et al. [ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf ''Cross Industry Standard Process for Data Mining'']. IBM, 2000.
@@ Line 138: / Line 116: @@
 === Week 4: October 19 ===
 [[HCDS_(Fall_2017)/Day_4_plan|Day 4 plan]]
-[[:File:HCDS Week 4 slides.pdf|Day 4 slides]]
 ;Study design: ''understanding your data; framing research questions; planning your study''
@@ Line 152: / Line 128: @@
 ;Readings assigned
-* Shyong (Tony) K. Lam, Anuradha Uduwage, Zhenhua Dong, Shilad Sen, David R. Musicant, Loren Terveen, and John Riedl. 2011. ''[http://files.grouplens.org/papers/wp-gender-wikisym2011.pdf WP:clubhouse?: an exploration of Wikipedia's gender imbalance.]'' In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym '11). ACM, New York, NY, USA, 1-10. DOI=http://dx.doi.org/10.1145/2038558.2038560
 ;Homework assigned
@@ Line 162: / Line 136: @@
 ;Resources
 * Aschwanden, Christie. [https://fivethirtyeight.com/features/science-isnt-broken/ ''Science Isn't Broken''] FiveThirtyEight, 2015.
-* Halfaker, Aaron et al. ''[https://www-users.cs.umn.edu/~halfak/publications/The_Rise_and_Decline/ The Rise and Decline of an Open Collaboration Community: How Wikipedia's reaction to sudden popularity is causing its decline].'' American Behavioral Scientist, 2012.
-* Warnke-Wang, Morten. ''[https://meta.wikimedia.org/wiki/Research:Autoconfirmed_article_creation_trial Autoconfirmed article creation trial].'' Wikimedia, 2017.
-* ''[https://www.forbes.com/sites/hbsworkingknowledge/2015/01/20/wikipedia-or-encyclopaedia-britannica-which-has-more-bias/#1a68e6337d4a Wikipedia Or Encyclopædia Britannica: Which Has More Bias?]''. Forbes, 2015. Based on Greenstein, Shane, and Feng Zhu.''[http://www.hbs.edu/faculty/Publication%20Files/15-023_e044cf50-f621-4759-a827-e9a3bf8920c0.pdf Do Experts or Collective Intelligence Write with More Bias? Evidence from Encyclopædia Britannica and Wikipedia]''. Harvard Business School working paper.
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 <hr/>
@@ Line 171: / Line 142: @@
 === Week 5: October 26 ===
 [[HCDS_(Fall_2017)/Day_5_plan|Day 5 plan]]
-[[:File:HCDS Week 5 slides.pdf|Day 5 slides]]
 ;Machine learning: ''ethical AI, algorithmic transparency, societal implications of machine learning''
@@ Line 178: / Line 147: @@
 ;Assignments due
 * Reading reflection
+* A2: Bias in data
 ;Agenda
@@ Line 183: / Line 153: @@
 ;Readings assigned
-* Christian Sandvig, Kevin Hamilton, Karrie Karahalios, Cedric Langbort (2014/05/22) ''[http://www-personal.umich.edu/~csandvig/research/Auditing%20Algorithms%20--%20Sandvig%20--%20ICA%202014%20Data%20and%20Discrimination%20Preconference.pdf Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms].'' Paper presented to "Data and Discrimination: Converting Critical Concerns into Productive Inquiry," a preconference at the 64th Annual Meeting of the International Communication Association. May 22, 2014; Seattle, WA, USA.
 ;Homework assigned
@@ Line 191: / Line 160: @@
 ;Resources
-* Bamman, David ''[https://cscw2016hcds.files.wordpress.com/2015/10/bamman_hcds.pdf Interpretability in Human-Centered Data Science].'' 2016 CSCW workshop on Human-Centered Data Science.
-* Anderson, Carl. ''[https://medium.com/@leapingllamas/the-role-of-model-interpretability-in-data-science-703918f64330 The role of model interpretability in data science].'' Medium, 2016.
-* Hill, Kashmir. ''[https://gizmodo.com/facebook-figured-out-my-family-secrets-and-it-wont-tel-1797696163 Facebook figured out my family secrets, and it won't tell me how].'' Engadget, 2017.
-* Blue, Violet. ''[https://www.engadget.com/2017/09/01/google-perspective-comment-ranking-system/ Google’s comment-ranking system will be a hit with the alt-right].'' Engadget, 2017.
-* Ingold, David and Soper, Spencer. ''[https://www.bloomberg.com/graphics/2016-amazon-same-day/ Amazon Doesn’t Consider the Race of Its Customers. Should It?].'' Bloomberg, 2016.
-* Mars, Roman. ''[https://99percentinvisible.org/episode/the-age-of-the-algorithm/ The Age of the Algorithm].'' 99% Invisible Podcast, 2017.
-* [https://www.perspectiveapi.com/#/ Google's Perspective API]
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@@ Line 205: / Line 167: @@
 === Week 6: November 2 ===
 [[HCDS_(Fall_2017)/Day_6_plan|Day 6 plan]]
-[[:File:HCDS Week 6 slides.pdf|Day 6 slides]]
 ;Mixed-methods research: ''Big data vs thick data; qualitative research in data science ''
@@ Line 213: / Line 173: @@
 ;Assignments due
 * Reading reflection
-* A2: Bias in data
@@ Line 221: / Line 180: @@
 ;Readings assigned
-* R. Stuart Geiger and Aaron Halfaker. 2017. ''[https://commons.wikimedia.org/wiki/File:conflict-bots-wp-cscw.pdf Operationalizing conflict and cooperation between automated software agents in Wikipedia: A replication and expansion of Even Good Bots Fight]''. Proceedings of the ACM on Human-Computer Interaction (Nov 2017 issue, CSCW 2018 Online First) 1, 2, Article 49. DOI: https://doi.org/10.1145/3134684
 ;Homework assigned
@@ Line 228: / Line 186: @@
 ;Resources
-* Maximillian Klein. ''[http://whgi.wmflabs.org/gender-by-language.html Gender by Wikipedia Language]''. Wikidata Human Gender Indicators (WHGI), 2017.
-* Benjamin Collier and Julia Bear. ''[https://static1.squarespace.com/static/521c8817e4b0dca2590b4591/t/523745abe4b05150ff027a6e/1379354027662/2012+-+Collier%2C+Bear+-+Conflict%2C+confidence%2C+or+criticism+an+empirical+examination+of+the+gender+gap+in+Wikipedia.pdf Conflict, criticism, or confidence: an empirical examination of the gender gap in wikipedia contributions]''. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12). DOI: https://doi.org/10.1145/2145204.2145265
-* Christina Shane-Simpson, Kristen Gillespie-Lynch, Examining potential mechanisms underlying the Wikipedia gender gap through a collaborative editing task, In Computers in Human Behavior, Volume 66, 2017, https://doi.org/10.1016/j.chb.2016.09.043. (PDF on Canvas)
-* Amanda Menking and Ingrid Erickson. 2015. ''[https://upload.wikimedia.org/wikipedia/commons/7/77/The_Heart_Work_of_Wikipedia_Gendered,_Emotional_Labor_in_the_World%27s_Largest_Online_Encyclopedia.pdf The Heart Work of Wikipedia: Gendered, Emotional Labor in the World's Largest Online Encyclopedia]''. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). https://doi.org/10.1145/2702123.2702514
-* Andrea Forte, Nazanin Andalibi, and Rachel Greenstadt. ''[http://andreaforte.net/ForteCSCW17-Anonymity.pdf Privacy, Anonymity, and Perceived Risk in Open Collaboration: A Study of Tor Users and Wikipedians]''. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17). DOI: https://doi.org/10.1145/2998181.2998273
 <br/>
@@ Line 252: / Line 205: @@
 {{:HCDS (Fall 2017)/Day 7 plan}}
-;Readings assigned (read both, reflect on one)
+;Readings assigned
-* Lilly C. Irani and M. Six Silberman. 2013. ''[https://escholarship.org/content/qt10c125z3/qt10c125z3.pdf Turkopticon: interrupting worker invisibility in amazon mechanical turk]''. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). DOI: https://doi.org/10.1145/2470654.2470742
-* Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao (Ken) Wang, and Brent Hecht. 2015. ''[http://www-users.cs.umn.edu/~bhecht/publications/goldstandards_CSCW2015.pdf Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards]''. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '15). DOI: http://dx.doi.org/10.1145/2675133.2675285
 ;Homework assigned
 * Reading reflection
-* A4: Crowdwork ethnography
+* A4: Crowdwork self-ethnography
 ;Resources
-* WeArDynamo contributors. ''[http://wiki.wearedynamo.org/index.php?title=Basics_of_how_to_be_a_good_requester How to be a good requester]'' and ''[http://wiki.wearedynamo.org/index.php?title=Guidelines_for_Academic_Requesters Guidelines for Academic Requesters]''. Wearedynamo.org
+*''go here''
-* Wang, Tricia. ''[https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7 Why Big Data Needs Thick Data]''. Ethnography Matters, 2016.
-<!-- * Wanda J. Orlikowski. 1992. ''[https://dspace.mit.edu/bitstream/handle/1721.1/2412/SWP-3428-27000158-CCSTR-134.pdf%3Bjsessionid%3D89CCB8F0923C0235DB2902AA40C25E28?sequence%3D1 Learning from Notes: organizational issues in groupware implementation]''. In Proceedings of the 1992 ACM conference on Computer-supported cooperative work (CSCW '92). DOI=http://dx.doi.org/10.1145/143457.143549 -->
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@@ Line 273: / Line 222: @@
 [[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]]
-[[:File:HCDS Week 8 slides.pdf|Day 8 slides]]
+;User experience and big data: ''prototyping and user testing; benchmarking and iterative evaluation; UI design for data science''
-;User experience and big data: ''user-centered design and evaluation of recommender systems; UI design for data science, collaborative visual analytics''
@@ Line 285: / Line 232: @@
 ;Readings assigned
-*Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. ''[https://md.ekstrandom.net/research/pubs/listcmp/listcmp.pdf User perception of differences in recommender algorithms].'' In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14). ACM, New York, NY, USA, 161-168. DOI: https://doi.org/10.1145/2645710.2645737
-* Chen, N., Brooks, M., Kocielnik, R.,  Hong, R.,  Smith, J.,  Lin, S., Qu, Z., Aragon, C. ''[https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1254&context=hicss-50 Lariat: A visual analytics tool for social media researchers to explore Twitter datasets].'' Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS), Data Analytics and Data Mining for Social Media Minitrack (2017)
 ;Homework assigned
@@ Line 293: / Line 238: @@
 ;Resources
-* Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. ''[http://files.grouplens.org/papers/mcnee-chi06-hri.pdf Making recommendations better: an analytic model for human-recommender interaction].'' In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06). ACM, New York, NY, USA, 1103-1108. DOI=http://dx.doi.org/10.1145/1125451.1125660
-* Kevin Crowston and the Gravity Spy Team. 2017. ''[https://crowston.syr.edu/sites/crowston.syr.edu/files/cpa137-crowstonA.pdf Gravity Spy: Humans, Machines and The Future of Citizen Science].'' In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17 Companion). ACM, New York, NY, USA, 163-166. DOI: https://doi.org/10.1145/3022198.3026329
-* Michael D. Ekstrand and Martijn C. Willemsen. 2016. ''[https://md.ekstrandom.net/research/pubs/behaviorism/BehaviorismIsNotEnough.pdf Behaviorism is Not Enough: Better Recommendations through Listening to Users].'' In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 221-224. DOI: https://doi.org/10.1145/2959100.2959179
-* Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
-* Xavier Amatriain and Justin Basilico. ''[https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 Netflix Recommendations: Beyond the 5 stars].'' Netflix Tech Blog, 2012.
-*Fabien Girardin. ''[https://medium.com/@girardin/experience-design-in-the-machine-learning-era-e16c87f4f2e2 Experience design in the machine learning era].'' Medium, 2016.
-* Brian Whitman. ''[https://notes.variogr.am/2012/12/11/how-music-recommendation-works-and-doesnt-work/ How music recommendation works - and doesn't work].'' Variogram, 2012.
-* Paul Lamere. ''[https://musicmachinery.com/2011/05/14/how-good-is-googles-instant-mix/ How good is Google's Instant Mix?].'' Music Machinery, 2011.
-* Snyder, Jaime. ''[https://cscw2016hcds.files.wordpress.com/2015/10/snyder_hcds20162.pdf Values in the Design of Visualizations].'' 2016 CSCW workshop on Human-Centered Data Science.
 <br/>
@@ Line 314: / Line 250: @@
 ;Assignments due
 * Reading reflection
-* A4: Crowdwork ethnography
+* A4: Crowdwork self-ethnography
 ;Agenda
@@ Line 320: / Line 256: @@
 ;Readings assigned
-* Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). Democratizing Data Science: The Community Data Science Workshops and Classes. In N. Jullien, S. A. Matei, & S. P. Goggins (Eds.), ''Big Data Factories: Scientific Collaborative approaches for virtual community data collection, repurposing, recombining, and dissemination''. New York, New York: Springer Nature. [[https://mako.cc/academic/hill_etal-cdsw_chapter-DRAFT.pdf Preprint/Draft PDF]]
-* Bivens, R. and Haimson, O.L. 2016. ''[http://journals.sagepub.com/doi/pdf/10.1177/2056305116672486 Baking Gender Into Social Media Design: How Platforms Shape Categories for Users and Advertisers]''. Social Media + Society. 2, 4 (2016), 205630511667248. DOI:https://doi.org/10.1177/2056305116672486.
-* Schlesinger, A. et al. 2017. ''[http://arischlesinger.com/wp-content/uploads/2017/03/chi2017-schlesinger-intersectionality.pdf Intersectional HCI: Engaging Identity through Gender, Race, and Class].'' Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17. (2017), 5412–5427. DOI:https://doi.org/10.1145/3025453.3025766.
 ;Homework assigned
@@ Line 328: / Line 261: @@
 ;Resources
-* Berney, Rachel, Bernease Herman, Gundula Proksch, Hillary Dawkins, Jacob Kovacs, Yahui Ma, Jacob Rich, and Amanda Tan. ''[https://dssg.uchicago.edu/wp-content/uploads/2017/09/berney.pdf Visualizing Equity: A Data Science for Social Good Tool and Model for Seattle].'' Data Science for Social Good Conference, September 2017, Chicago, Illinois USA (2017).
-* Sayamindu Dasgupta and Benjamin Mako Hill. ''[https://cscw2016hcds.files.wordpress.com/2015/10/dasgupta_hcds2016.pdf Learning With Data: Designing for Community Introspection and Exploration].'' Position paper for Developing a Research Agenda for Human-Centered Data Science (a CSCW 2016 workshop).
 <br/>
@@ Line 337: / Line 268: @@
 === Week 10: November 30 ===
 [[HCDS_(Fall_2017)/Day_10_plan|Day 10 plan]]
-[[:File:HCDS Week 10 slides.pdf|Day 10 slides]]
 ;Communicating methods, results, and implications: translating for non-data scientists ''
@@ Line 351: / Line 280: @@
 ;Readings assigned
-* Megan Risdal, ''[http://blog.kaggle.com/2016/06/29/communicating-data-science-a-guide-to-presenting-your-work/ Communicating data science: a guide to presenting your work].'' Kaggle blog, 2016.
-* Marilynn Larkin, ''[https://www.elsevier.com/connect/how-to-give-a-dynamic-scientific-presentation How to give a dynamic scientific presentation].'' Elsevier Connect, 2015.
 ;Homework assigned
@@ Line 360: / Line 286: @@
 ;Resources
-* Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. ''[https://pure.tue.nl/ws/files/3484177/724656348730405.pdf Explaining the user experience of recommender systems].'' User Modeling and User-Adapted Interaction 22, 4-5 (October 2012), 441-504. DOI=http://dx.doi.org/10.1007/s11257-011-9118-4
+* ''one''
-* Sean M. McNee, Nishikant Kapoor, and Joseph A. Konstan. 2006. ''[http://files.grouplens.org/papers/p171-mcnee.pdf Don't look stupid: avoiding pitfalls when recommending research papers].'' In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work (CSCW '06). ACM, New York, NY, USA, 171-180. DOI=http://dx.doi.org/10.1145/1180875.1180903
-* Megan Risdal, ''[http://blog.kaggle.com/2016/08/10/communicating-data-science-why-and-some-of-the-how-to-visualize-information/ Communicating data science: Why and how to visualize information].'' Kaggle blog, 2016.
-* Megan Risdal, ''[http://blog.kaggle.com/2016/06/13/communicating-data-science-an-interview-with-a-storytelling-expert-tyler-byers/ Communicating data science: an interview with a storytelling expert].'' Kaggle blog, 2016.
-* Richard Garber, ''[https://joyfulpublicspeaking.blogspot.com/2010/08/power-of-brief-speeches-world-war-i-and.html Power of brief speeches: World War I and the Four Minute Men].'' Joyful Public Speaking, 2010.
-* Brent Dykes, ''[https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/ Data Storytelling: The Essential Data Science Skill Everyone Needs].'' Forbes, 2016.
 <br/>
@@ Line 374: / Line 295: @@
 [[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]]
-;Future of human centered data science: course wrap up, final presentations''
+;Future of human centered data science: ''case studies from research, industry, and policy; final presentations''