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1 UCLA Graduate School of Education and Information Studies UCLA Peer Reviewed Title: Slides for When use cases are not useful: Data practices, astronomy, and digital libraries Author: Wynholds, Laura, University of California - Los Angeles Fearon, David, University of California - Los Angeles Borgman, Christine L, University of California, Los Angeles Traweek, Sharon, University of California, Los Angeles Publication Date: Series: Working Papers Permalink: Keywords: Data practices, astronomy, and digital libraries Abstract: As science becomes more dependent upon digital data, the need for data curation and for data digital libraries becomes more urgent. Questions remain about what researchers consider to be their data, their criteria for selecting and trusting data, and their orientation to data challenges. This paper reports findings from the first 18 months of research on astronomy data practices from the Data Conservancy. Initial findings suggest that issues for data production, use, preservation, and sharing revolve around factors that rarely are accommodated in use cases for digital library system design including trust in data, funding structures, communication channels, and perceptions of scientific value. Copyright Information: All rights reserved unless otherwise indicated. Contact the author or original publisher for any necessary permissions. escholarship is not the copyright owner for deposited works. Learn more at escholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide.

2 WHEN USE CASES ARE NOT USEFUL: DATA PRACTICES, ASTRONOMY, AND DIGITAL LIBRARIES Laura Wynholds David S. Fearon Jr Christine L. Borgman Sharon Traweek University of California, Los Angeles

3 Ethnographic Project Analysis Repository Design Methods and Scope Sloan Digital Sky Survey (SDSS) Pan-STARRS Large Synoptic Survey Telescope (LSST) data practices history archives circulation of people, knowledge transfer, development history curation plans Interviews Oral Histories Project Archives Websites Requirements Use Cases Wynholds, Fearon, Borgman & Traweek JCDL

4 1. Data practices What are the data management, curation, and sharing practices? Research Questions 2. Social networks Who uses what data when, with whom, and why? 3. Curation What data are most important to curate, how, and for whom? Wynholds, Fearon, Borgman & Traweek JCDL

5 Astronomy Data Practices Astronomy data Heterogeneous Highly distributed data collections many important datasets have no home Data-intensive terabyte and petabyte scale projects favor large national and international collaborations Long tail of smaller-scale investigations with more modest data footprint Wynholds, Fearon, Borgman & Traweek JCDL

6 Complex Data Transformations Even defining what is the most basic thing, like what's the flux coming from a galaxy?...there are tens or hundreds of ways to do that. And if you don't know what you're looking at, you cannot do precision work. Observations are handled with a constant awareness of the constraints on their evidential value. Use requires expertise, knowledge and judgment. Astronomers rely on personal negotiations with data experts. NASA data archives provide experts in addition to data Astronomers reported contacting other researchers and being contacted by others regarding their data Wynholds, Fearon, Borgman & Traweek JCDL

7 Known Digital Library Design Challenges Heterogeneity of data as a concept of data uses and repurposing of identity and identifiers of approaches to data in scholarly publishing Use impacted by tacit knowledge origin audience trust reliability validity dependencies description documentation Wynholds, Fearon, Borgman & Traweek JCDL

8 Top 5 Non-Technical Challenges Focus on use cases was failing to represent the predominance of social and sociotechnical challenges: I. Trust II. III. IV. Documentation Value Funding and curation environments V. Data Integration and Interoperability Wynholds, Fearon, Borgman & Traweek JCDL

9 I. Trust in Sources the further you are from that expert, the less trust. Extensively tested and vetted data products are generally considered well documented and trustworthy e.g. SDSS, NASA satellite missions canonical vs secondary data Trust is related to: expertise & reputation of the data producers adequacy of documentation fears of misinterpretation, how the data were processed Wynholds, Fearon, Borgman & Traweek JCDL

10 II. Documentation Whenever you have not generated the data, then the documentation must be incredibly detailed and incredibly well thought through. Because if I produced the data, then I know exactly what's happening, but if somebody else did, it's so easy to miss essential details or misinterpret things. Lack of clear provenance makes it easier to rerun secondary data manipulations than to figure out what was done. Astronomers were hesitant to share their own secondary data products citing cost of documentation and concerns regarding misinterpretation Wynholds, Fearon, Borgman & Traweek JCDL

11 III. Funding and Curation Environments curation impacted by methods & approaches funding sources, project size type of instrument size of collaboration space based vs. ground based parent institution type domain Radio Optical Infrared Ultraviolet and X-Ray Theoretical Wynholds, Fearon, Borgman & Traweek JCDL

12 IV. Assessing Value Show some major new results that came about as a result of data mining, and then explain those tools in the language of astronomers, and then we will start to see a sea change. Astronomers evaluate data based on anticipated use & quality of results Future use is difficult to anticipate. Contested within and between subdomains Wynholds, Fearon, Borgman & Traweek JCDL

13 V. Data Integration and Interoperability Astronomers regularly draw observations from multiple data archives and projects Poor interoperability among archives and a steep learning curve has resulted in low adoption rates of tools in the community Perception of a lack of significant discoveries associated with data reuse There s some off the shelf tools, they're not terribly scalable, most people don't even know how to use them, not even such as they are. The learning curves are very steep, their penetration in communities is very low." you risk having people grab data from these distributed archives and not really understand the data. I think there actually are papers that are having that problem now. Wynholds, Fearon, Borgman & Traweek JCDL

14 Conclusions Trusted data products are central to astronomy research, especially when well calibrated, vetted, and publicly accessible. Secondary data products are currently socially marginal Considerable resources, especially in the form of human expertise are required to curate canonical datasets Data digital libraries need to address issues of trust, documentation, interoperability, and appraising value as a part of design. Wynholds, Fearon, Borgman & Traweek JCDL

15 Acknowledgements Comments & Feedback UCLA CENS Data Research Group: Matthew Mayernik, Katie Shilton, Jillian Wallis. Research funding National Science Foundation Data Conservancy: OCI , Sayeed Choudhury, PI, Johns Hopkins University. Microsoft External Research Wynholds, Fearon, Borgman & Traweek JCDL