Data Science Ethnography

Conway Venn Diagram

“The Data Science Venn Diagram” by Drew Conway is licensed under CC BY-NC.

We study how researchers work with large and complex datasets, and the institutions, programs, and communities that support data-intensive research. Using an ethnographic approach, we examine how people use and learn data science methods, how different communities make sense of and value data, and how institutions and organizations support data-intensive practices and collaborations.

HDS Lab members are part of an interdisciplinary Data Science Studies team at UW, with counterparts at NYU and UC Berkeley. Our research group works within the data science environment to understand the cultural changes that are reshaping how data-intensive work is accomplished, and the institutional structures supporting this work. We generate insights into the opportunities and challenges data scientists face that inform the design of tools, educational programs, and institutional initiatives for data-intensive research. Our work is funded in large part by the Data Science Environment grant awarded by the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation.


Cecilia Aragon, University of Washington, Human Centered Design & Engineering, Associate Professor

Brittany Fiore-Gartland, UW, HCDE, Moore/Sloan Data Science Postdoctoral Fellow, Washington Research Foundation Innovation Postdoctoral Fellow

Anissa Tanweer, UW, Communication, PhD Student

Michael Brooks, UW, HCDE, PhD Candidate

Katerina Kuksenok, UW, Computer Science and Engineering, PhD Student


Cabasse Mazel, C., Fiore-Gartland, B., and Noren, L.  Building data science: Translating imagined collaborations into place.  Society for Social Studies of Science (4S), Denver, CO (2015).

Fiore-Gartland, B. & Tanweer, A. Community-level data science and its spheres of influence: beyond novelty. eScience Institute (2015).

Fiore-Silfvast, B. Hacked ethnographic fieldnotes. Astro Hack Week (2014, Oct. 1).

Fiore-Gartland, B.; Tanweer, A. & Drouhard, M. Data walking for social good. (2017).

Rokem, A., Aragon, C., Arendt, A., Fiore-Gartland, B., Hazelton, B., Hellerstein, J.,  Herman, B., Howe, B.,  Lazowska, E.,  Parker, M.,  Staneva, V., Stone, S., Tanweer, A., Vanderplas, J. Building an Urban Data Science Summer Program at the University of Washington eScience Institute. Bloomberg Data for Good Exchange Conference, New York, NY (2015). PDF 

Tanweer, A., Fiore-Gartland, B., and Aragon, C. The role of breakdown in imagining big data: Impediment to insight to innovation. Association of Internet Researchers, Phoenix, AZ (2015).

Tanweer, A., Fiore-Gartland, B., Aragon, C. Impediment to insight to innovation: understanding data assemblages through the breakdown-repair process. Information, Communication & Society (2016). DOI: 10.1080/1369118X.2016.1153125 PDF

Tanweer, A., Fiore-Gartland, B., Neff, G., Aragon, C. Data empathy: a call for human subjectivity in data science. Human-centered Data Science Workshop at Computer Supported Cooperative Work 2016 (CSCW ‘16), San Francisco, CA (2016). PDF

Tanweer, A. & Fiore-Gartland, B. Cross-sector collaboration in data science for social good: Opportunities, challenges, and open questions raised by working with academic researchers. Data Science for Social Good Conference 2017. Chicago, IL. PDF

Tanweer, A., Drouhard, M., Fiore-Gartland, B., Bolten, N., Hamilton, J., Tan, K., Caspi, A. Mapping for accessibility: A case study of ethics in data science for social good. Bloomberg Data for Good Exchange 2017 (D4GX ’17). New York, NY. PDF

Tanweer, A. & Fiore-Gartland, B. Data for good: Harbinger of social sector change? Society for the Social Studies of Science and Technology 2017 (4S ’17). Boston, MA.