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CDSC members plus affiliates and guests at Northwestern University September 2019. Back row, from left to right: Aaron, Nate, Jeremy, Mako, Jim, Charlie, Regina, Salt. Front row, f.l.t.r.: Sohyeon, Kaylea, Nick, Sejal, Floor, Jackie.

The Community Data Science Collective is an interdisciplinary research group made of up of faculty and students at the University of Washington Department of Communication, the Northwestern University Department of Communication Studies, the University of North Carolina School of Information and Library Science, the Carleton College Computer Science Department, and the Purdue University School of Communication.

We are social scientists applying a range of quantitative and qualitative methods to the study of online communities. We seek to understand both how and why some attempts at collaborative production — like Wikipedia and Linux — build large volunteer communities and high quality work products.

Our research is particularly focused on how the design of communication and information technologies shape fundamental social outcomes with broad theoretical and practical implications — like an individual’s decision to join a community, contribute to a public good, or a group’s ability to make decisions democratically.

Our research is deeply interdisciplinary, most frequently consists of “big data” quantitative analyses, and lies at the intersection of communication, sociology, and human-computer interaction.

Workshops and Courses[edit]

In addition to research, we run workshops and teach classes. Some of that work is coordinated on this wiki. A more detailed lists of workshops and teaching material on this wikis is on our Workshops and Classes page. In this page, we only list ongoing classes and workshops.

Public Data Science Workshops[edit]

Community Data Science Workshops — The Community Data Science Workshops (CDSW) are a series of workshops designed to introduce some of the basic tools of programming and analysis of data from online communities to absolute beginners. The CDSW have been held roughly twice a year since beginning in Seattle in 2014. So far, more than 100 people have volunteered their weekends to teach more than 500 people to program in Python, to build datasets from Web APIs, and to ask and answer questions using these data.

University of Washington Courses[edit]

Northwestern Courses & Workshop[edit]

  • [Spring 2019] MTS 525: Statistics and Statistical Programming — A quarter-long quantitative methods course that builds a first-quarter introduction to quantitative methodology and that focuses on both the more mathematical elements of statistics as well as the nuts-and-bolts of statistical programming in the GNU R programming language. Taught by Aaron Shaw.
  • [Spring 2019] MTS 503: The Practice of Scholarship — A workshop-style course dedicated to the submission of an original (lead or sole authored) piece of academic research for publication by the end of the quarter. The course and assignments require weekly writing and feedback from all participants (required of all second year Ph.D. students in the MTS and TSB Ph.D. programs). Taught by Aaron Shaw

Research Resources[edit]

If you are a member of the collective, perhaps you're looking for CommunityData:Resources which includes details on email, TeX templates, documentation on our computing resources, etc.

Research News[edit]

Follow us as @comdatasci on Twitter and subscribe to the Community Data Science Collective blog.

Recent posts from the blog include:

Community Data Science Collective at CSCW 2019
A good chunk of the Community Data Science Collective is in Austin, Texas this week for the 2019 ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW). The conference marks the official publication of four papers by collective students and faculty. All four papers were published in the journal Proceedings of the ACM on …
— Benjamin Mako Hill http://mako.cc 2019-11-14
Teaching introduction to statistics and statistical computing
I taught a graduate-level introduction to applied statistics and statistical computing this past Spring. The course design iterated on a class Mako developed in 2017. Very nearly all of the course materials are available open access through the Community Data Science Collective wiki and I wanted to make sure to share them more widely with …
— Aaron Shaw http://aaronshaw.org 2019-10-01
How Online Communities Adapt to New Platforms with Public APIs
Introducing new technology into a work place is often disruptive, but what if your work was also completely mediated by technology? This is exactly the case for the teams of volunteer moderators who work to regulate content and protect online communities from harm. What happens when the social media platforms these communities rely on change …
— Charlie Kiene 2019-09-09
New Grant for Studying “Underproduction” in Software Infrastructure
Earlier this year, a team led by Kaylea Champion were announced as recipients of a generous grant from the Ford and Sloan Foundations to support research into into peer produced software infrastructure. Now that the project is moving forward in earnest, we’re thrilled to tell you about it. The project is motivated by the fact …
— Community Data Science Collective https://communitydata.cc/ 2019-06-06

About This Wiki[edit]

This is open to the public and hackable by all but mostly contains information that will be useful to collective members, their collaborators, people enrolled in their projects, or people interested in building off of their work. If you're interested in making a change or creating content here, generally feel empowered to Be Bold. If things don't fit, somebody who watches this wiki will be in touch.

This is mostly a normal MediaWiki although there are a few things to know:

  • There's a CAPTCHA enabled. If you create an account and then contact any collective member with the username (on or off wiki), they can turn the CAPTCHA off for you.
  • Extension:Math is installed so you can write math here. Basically you just add math by putting TeX inside <nowiki> tags like this: <math>\frac{\sigma}{\sqrt{n}}</math>