Autoethnographic Methods for Studying Data-Driven Knowledge Production

2017 Annual Meeting of the Society for the Social Studies of Science (4S)


August 31, 2017
2:00pm to 3:00pm
Boston, MA

An overview of how to study data science ethnographically by personally engaging in various practices of data science.

This presentation is based on a collaborative, multisided ethnography of data science, in which we have been embedded in aligned institutes dedicated to data science. In this paper, I focus on autoethnographic methods, which can be powerful and generative ways to conduct empirical investigations into data science practices across many theoretical issues. This paper reviews several different exercises, initiatives, and activities that we have conducted in our fieldwork, reflecting on how they help us better understand different aspects of what it means to do data science.


R. Stuart Geiger

BIDS Alum – Ethnographer

Former BIDS Ethnographer Stuart Geiger is now a faculty member at the University of California, San Diego, jointly appointed in the Department of Communication and the Halıcıoğlu Data Science Institute. At BIDS, as an ethnographer of science and technology, he studied the infrastructures and institutions that support the production of knowledge. He launched the Best Practices in Data Science discussion group in 2019, having been one of the original members of the MSDSE Data Science Studies Working Group. Previously, his work on Wikipedia focused on the community of volunteer editors who produce and maintain an open encyclopedia. He also studied distributed scientific research networks and projects, including the Long-Term Ecological Research Network and the Open Science Grid. In Wikipedia and scientific research, he studied topics including newcomer socialization, community governance, specialization and professionalization, quality control and verification, cooperation and conflict, the roles of support staff and technicians, and diversity and inclusion. And, as these communities are made possible through software systems, he studied how the design of software tools and systems intersect with all of these issues.  He received an undergraduate degree at UT Austin, and an MA in Communication, Culture, and Technology at Georgetown University, where he began empirically studying communities using qualitative and ethnographic methods.  As part of receiving his PhD from the UC Berkeley School of Information, he worked with anthropologists, sociologists, psychologists, historians, organizational and management scholars, designers, and computer scientists.