Date/Time
Thursday - Friday
10 Nov - 11 Nov 2016
Location
Robert E. Kennedy Library, Room 111H
Event Type
Non-CSTMS Event
Organizers: Amelia Acker (UT Austin), Brian Beaton (Cal Poly), Lauren Di Monte (NC State), Shiv Setlur (Cornell), Tonia Sutherland (U. Alabama)
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. 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.