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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.

Courses
In addition to research, we teach classes and run workshops. 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.

University of Washington Courses

 * [Winter 2020] COM482: Interpersonal Media: Online Communities — A course on online communities and computer mediated communication with an emphasis on learning from research in social psychology, sociology, and behavioral economics taught by Benjamin Mako Hill.
 * [Winter 2020] COM528: Internet Research Methods — A MA/PhD class offering a survey of several Internet research methods taught by Benjamin Mako Hill.

Northwestern Courses

 * [Winter 2020] History and Theory of Information — We live in an information age, with computers of unprecedented power in our pockets. This course seeks to understand how information shapes our lives today, and how it has in the past. It does so via an interdisciplinary inquiry into four technological infrastructures of information and communication—print, wires, airwaves, and bits. Co-taught by Aaron Shaw and Daniel Immerwahr.

Public Data Science Workshops
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 six times in Seattle between 2014 and 2020. 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.

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

Recent posts from the blog include:

https://blog.communitydata.science/feed/atom/

About This Wiki
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  tags like this: $$\frac{\sigma}{\sqrt{n}}$$