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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.

CDSC members at the CDSC group retreat in March 2021 (pandemic virtual edition #3). Left to right by row, starting at top: Connor (special guest!), Regina, Nick, Floor, Aaron, Nate, Sohyeon, Carl, Stef, Emilia, Salt, Jeremy, Charlie, Kaylea, Sneha, Mako. Check out our other group photos!


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

Purdue University

  • [Fall 2021] Communication and Social Networks (COM 411, Fall 2021) – This class focuses on understanding how the structure of relationships between people influence communication patterns and behavior. This perspective can help us to understand a broad set of phenomena, from online communities to friendships to businesses. The course will also introduce students to using network visualizations to gain and share insights about network phenomena. Taught by Jeremy Foote.
  • [Fall 2021] Intro to Programming and Data Science (COM 674, Fall 2021) – This course is intended to give students an introduction to programming principles, the Python programming language, and data science tools and approaches. Taught by Jeremy Foote.

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:

Measuring Wikipedia Article Quality in One Continuous Dimension
Accurately estimating the quality of Wikipedia articles is important task for both researchers and Wikipedia community members. In a forthcoming paper in the Proceedings of the OpenSym 2021, I describe a new method for estimating article quality in Wikipedia in one dimension that builds on the widely used ORES quality model and that improves on …
— Nate TeBlunthuis 2021-09-14
Why do people participate in small online communities?
When it comes to online communities, we often assume that bigger is better. Large communities can create robust interactions, have access to broad and extensive body of experiences, and provide many opportunities for connections. As a result, small communities are often thought as failed attempts to build big ones. In reality, most online communities are …
— Community Data Science Collective https://communitydata.cc/ 2021-09-07
Apply to Join the Community Data Science Collective as a PhD student!
It’s Ph.D. application season and the Community Data Science Collective is recruiting! As always, we are looking for talented people to join our research group. Applying to one of the Ph.D. programs that the CDSC faculty members are affiliated with is a great way to do that. This post provides a very brief run-down on …
— Community Data Science Collective https://communitydata.cc/ 2021-09-04
Community Data Science Collective Research at DebConf 2021
Debian is one of the oldest, largest, and most influential peer production communities and has produced an operating system used by millions for over the last three decades. DebConf is that community’s annual meeting. This year, the Community Data Science Collective was out in force at Debian’s virtual conference to present several Debian-focused research projects …
— Community Data Science Collective https://communitydata.cc/ 2021-08-31

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 <math> tags like this: <math>\frac{\sigma}{\sqrt{n}}</math> and it will write .