Participants: Katie Shilton (U. Maryland iSchool), Cathryn Carson (UC Berkeley DSI), Stuart Geiger (UC Berkeley DSI), Anna Hoffmann (UC Berkeley DSI), Charlotte Cabasse (UC Berkeley DSI), Brooke Singer (SUNY Purchase New Media), Joyce Bell (U. Minnesota), Bonnie Mak (U. Illinois iSchool)
External Prompts: Joe Dumit (UC Davis), Donna Haraway (UC Santa Cruz), Karen Levy (Cornell)
The primary objective of this NSF workshop is to bring academic researchers and students together around the topic of data science. The workshop approaches data science as an important and growing profession that operates at the intersection of the STEM fields and the liberal and creative arts. Advocates describe data science as producing novel kinds of knowledge and insight; this is partly the result of how visualization, communication, and storytelling practices figure within data science work as well as the scale and pace of knowledge production in data science research communities. But data science has also imported tools, theories, and work cultures from pre-existing disciplines (e.g., physics and statistics) and brought them to bear on problems that have long been pursued in other domains of inquiry (e.g., social science and humanities research). In order to interrogate the potential for conflict and collaboration, this workshop will focus on data science’s values, communication patterns, analytical habits, standards, tools, infrastructures, and ethical codes.
Rather than offering a series of formal research paper presentations, the workshop will be run as an interactive meeting focused on unpacking data science research cultures and producing a “Next Steps” document for the National Science Foundation (NSF). The document will identify key research gaps in social studies of data science, suggest concrete research milestones, and highlight agenda-setting research in this important area. The workshop will also be designed to promote dialogue between scholars from different disciplines and from different career stages who may hold competing ideas about what constitutes data science and about what itineraries the data science profession should follow.
A particular focus of the workshop, as the title suggests, will be the idea of ‘social facets’. This language was provided by the NSF and thus offers an important theme to probe and query. Why social facets? Social facets can mean data ethics, social friction within the profession of data science, the cultural impacts of data science, the layers of interpersonal behavior among working data scientists, and more. As such, a key aim of the workshop will be to outline some of the most salient ‘social facets’ of data science and to discuss research techniques that can make data science’s ‘social facets’ observable.
Additional topics to be covered: the historical contexts from which data science emerged; the relationship between data science and the infrastructures on which it depends; data science and the public sphere; ethnography of data science; data science curriculum; and links between data science, markets, and the state.
The workshop is being co-organized and facilitated by a group of scholars from California Polytechnic State University (Cal Poly), Cornell University, North Carolina State University (NC State), University of Alabama, and the University of Texas at Austin. Workshop participants include scholars from some of the country’s leading research and teaching universities, along with students in data science and the interdisciplinary field of science, technology and society (STS). The National Science Award number for the workshop is 1632499