One-day workshop to be held Sunday, February 28th, 2016 in San Francisco, CA, USA (in conjunction with CSCW 2016). Invited attendees, please register via the CSCW registration page – you should receive an email with the invited registration code.
*UPDATE: extended deadline for submissions
11th 18th December 2015: Submission of Position Papers (more info)
– 18th January 2016: Notification of acceptance
– 19th February 2016: Camera-ready version
– 28th February 2016: Workshop at CSCW 2016
View a copy of the extended abstract for the workshop [.pdf].
The study and analysis of large and complex data sets offer a wealth of insights in a variety of applications. Computational approaches provide researchers access to broad assemblages of data, but the insights extracted may lack the rich detail that qualitative approaches have brought to the understanding of sociotechnical phenomena. How do we preserve the richness associated with traditional qualitative methods while utilizing the power of large data sets? How do we uncover social nuances or consider ethics and values in data use?
These and other questions are explored by human-centered data science, an emerging field at the intersection of human-computer interaction (HCI), computer-supported cooperative work (CSCW), human computation, and the statistical and computational techniques of data science. This workshop, the first of its kind at CSCW, seeks to bring together researchers interested in human-centered approaches to data science to collaborate, define a research agenda, and form a community.
This workshop has the following goals:
- Build and connect an international community of researchers in human-centered data science.
- Define and develop the terms and techniques of human-centered data science.
- Identify, encourage, and facilitate opportunities for research cooperation, especially multi-institute, and interdisciplinary collaboration.
- Develop a research agenda for human-centered data science.
- Produce a co-authored paper or edited journal issue on the research agenda for human-centered data science.