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Call for Participation

Thursday April 21 2022, 9AM-1PM CDT (optional pre-workshop meet and greet 8AM CDT; optional post-workshop networking at 1PM CDT). Note CDT = GMT-6. 
Online 

A growing body of literature examines the work that goes into creating, collecting, managing, curating, analyzing, interpreting, and communicating data. These investigations detail the practices and processes involved in making data useful and meaningful so that aims of becoming ‘data-driven’ or ‘data-informed’ can become real. Drawing on these rich contributions, the time is now ripe for comparing and framing data work across domains, occupations, and locales. This workshop will initiate such comparisons in order to identify core themes and issues around data work and to lay out a research framework for investigating it. By building a broad understanding of the practices, occupations, tools, and concerns that characterize data work across domains, we will help to shape a toolbox for the study of data work, a broader agenda for future research in data work, and movement towards an integrated framework for the study of data work.

To apply, submit a position paper or case study from your research of up to 4 pages in length in the ACM Submission template. Papers should be submitted to Katie Pine (khpine@asu.edu) with the subject header “Data Work Workshop Submission [author(s) last names].” Papers will be reviewed by workshop co-organizers and participants will be selected based on relevance to the workshop theme on the basis of the position papers. Accepted papers will be made public via the workshop website only with permission of the authors (this is optional).

Format: The workshop will be held virtually via a videoconferencing platform such as Zoom. Instructions for virtual attendance will be sent in advance of the workshop.
Important dates:
Submission Deadline: February 21st
Notification: March 7th
Contact: Katie Pine khpine@asu.edu

CHI 2022 Workshop

Investigating Data Work Across Domains: New Perspectives on the Work of Creating Data

Extended Abstract for the Workshop

Workshop Organizers

Kathleen H. Pine
Arizona State University College of Health Solutions, USA, khpine@asu

Claus Bossen
Aaarhus University School of Communication and Culture, Denmark, clausbossen@cc.au.dk

Naja Holten Møller
University of Copenhagen Department of Computer Science, Denmark, naja@di.ku.dk

Milagros Miceli
Technische Universität Berlin, Weizenbaum Institute, Germany, m.miceli@tu-berlin.de

Alex Jiahong Lu
University of Michigan School of Information, USA, alexjlu@umich.edu

Yunan Chen
University of California, Irvine Department of Informatics, USA, yunanc@ics.uci.edu

Leah Horgan
University of California, Irvine Department of Informatics, USA, horganl@uci.edu

Zhaoyuan Su
University of California, Irvine Department of Informatics, USA, nick.su@uci.edu

Gina Neff
University of Cambridge Minderoo Centre for Technology and Democracy, UK, gsn23@cam.ac.uk

Melissa Mazmanian
University of California, Irvine Department of Informatics, USA, mmazmani@uci.edu

 

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