Methods: Qualitative
Fields: Communication, Ethnography

Collaborators: Brittany Fiore-Gartland, Anissa Tanweer, Katie Kuksenok, Cecilia Aragon UW, Human-Centered Data Science Lab

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

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

The emerging practice of data science is surrounded by cultural and organizational processes that we don’t yet fully understand. Data-intensive research requires new organizational configurations that bring together a range of disciplines and expertise.  These cross-disciplinary and multi-stakeholder data science collaborations represent a new model for scientific discovery. This new model challenges social and institutional norms in academia and presents opportunities to trace emerging scientific practice. Our work is grounded in the people, experiences and everyday practices that make up data science in academia and we explore a set of research questions to this end. How do new expectations for doing data-intensive science and using data science tools transform the way people work and collaborate?  How are disciplines and stakeholder communities adapting to new demands of data-intensive research? What scientific and data-intensive communication practices support the success of these collaborations? What values and affect are at stake across data science collaboration and how how might these values and affect influence research and career decisions? Our team of ethnographers utilizes a variety of qualitative methods including interviews and close observations of data science practices in order to explore these questions in a range of contexts, from urban science to astronomy.


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