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[[File:CDSC at Pok Pok (2017-03).jpg|250px|thumb|right|[[People|CDSC members]] at Pok Pok in March 2017. Clockwise from top left: Sneha, Mako, Aaron, Emilia, Nate, Jeremy, Sayamindu, Salt.]]
[[File:CDSC at Pok Pok (2017-03).jpg|250px|thumb|right|[[People|CDSC members]] at Pok Pok in March 2017. Clockwise from top left: Sneha, Mako, Aaron, Emilia, Nate, Jeremy, Sayamindu, Salt.]]

Revision as of 14:37, 11 April 2018


CDSC members at Pok Pok in March 2017. Clockwise from top left: Sneha, Mako, Aaron, Emilia, Nate, Jeremy, Sayamindu, Salt.

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 and the Northwestern University Department of Communication Studies.

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

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

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

  • [Fall 2017] DATA512: Human Centered Data Science — Fundamental principles of data science and its human implications. Data ethics; data privacy; differential privacy; algorithmic bias; legal frameworks and intellectual property; provenance and reproducibility; data curation and preservation; user experience design and usability testing for big data; ethics of crowdwork; data communication; and societal impacts of data science.

Northwestern Courses & Workshop

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:

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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 <nowiki> tags like this: <math>\frac{\sigma}{\sqrt{n}}</math>