UW Data Science Studies is a group of cross-disciplinary researchers studying the sociocultural and organizational processes around the emerging practice of data science. As a sub-field of Science and Technology Studies, we utilize a variety of qualitative and computational methods to understand the changing scientific practices, knowledge infrastructures, and cultural values that are shaping the environment for data-intensive scientific research.
This encompasses, but is not limited to research focused around topics such as: the cultural and institutional contexts for open science, open software, and open data, tool building and scientific workflow, new pedagogical models for data science education, how disciplines are adapting to the demands of data-intensive science, new epistemologies and social implications of data-intensive science, human-centered data science, and studies of sociotechnical data science ecosystems.
UW Data Science Studies is part of a collaborative and multi-sited working group supported through the Moore-Sloan Data Science Environments and in partnership with researchers at Berkeley Institute for Data Science and the Center for Data Science in New York University.
We invite participation from anyone who may be interested in these areas of study or in any kind of reflexive research around data science. The working group meeting will be a place to convene once a month to discuss research, read scholarly work related to this subfield, and support future research collaborations.
|3/8/17||“Democratizing Data Science: Perspectives from the Community Data Science Workshop and Software Carpentry”||Benjamin Mako Hill and Ariel Rokem|
|2/18/17||“Critical data literacies”||Sayamindu Dasgupta and Jevin West|
|1/11/17||“Democratization of data science”||Meg Drouhard|
|11/9/16||“Discussion of data science and pedagogy (with a focus on challenges and opportunities related to providing students with a human-centered perspective on data science)”||Bill Howe, Jevin West, Amelia Acker, and Katie Shilton (host Jaime Snyder)|
|10/12/16||“Cross-Sector Collaboration in Data Science”||Nick Bolten, Candace Faber, Mark Hallenbeck, Janice Hellman, Clifford Snow, Graham Thompson, and Matthew Wiedner|
|6/8/16||“Transparency, seamful design, and data science”||Jeff Heers and Sarah Fox|
|4/13/16||“The emergence of data science in academia: Opportunities and tensions across disciplines”||Brittany Fiore-Gartland and Alex Franks|