Reusing Scientific Data: How Earthquake Engineering Researchers Assess the Reusability of Colleagues’ Data
- 784 Downloads
Investments in cyberinfrastructure and e-Science initiatives are motivated by the desire to accelerate scientific discovery. Always viewed as a foundation of science, data sharing is appropriately seen as critical to the success of such initiatives, but new technologies supporting increasingly data-intensive and collaborative science raise significant challenges and opportunities. Overcoming the technical and social challenges to broader data sharing is a common and important research objective, but increasing the supply and accessibility of scientific data is no guarantee data will be applied by scientists. Before reusing data created by others, scientists need to assess the data’s relevance, they seek confidence the data can be understood, and they must trust the data. Using interview data from earthquake engineering researchers affiliated with the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES), we examine how these scientists assess the reusability of colleagues’ experimental data for model validation.
Key wordsdata reuse data sharing data quality trust scientific data collections data repositories e-Science cyberinfrastructure
We want to thank John L. King, Stephanie Teasley, Elizabeth Yakel, and the reviewers and editors at CSCW for their feedback on early versions of this work. We also want to thank Martha Gukeisen for her help during data collection. This research is based on work supported by the National Science Foundation, Award number CMMI-0714116 to the University of Michigan. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
- Baker, K. S., & Yarmey, L. (2008). Data stewardship: Environmental data curation and a web-of-repositories: 4th International Digital Curation Conference, Edinburgh, Scotland, December, 2008.Google Scholar
- Birnholtz, J. P., & Bietz, M. (2003). Data at work: Supporting sharing in science and engineering: ACM Conference on Supporting Group Work, Sanibel Island, FL, 2003, pp. 339–348.Google Scholar
- Borgman, C. L. (2007). Scholarship in the digital age: Information, infrastructure, and the internet. Cambridge: MIT Press.Google Scholar
- Carlson, S., & Anderson, B. (2007). What are data? The many kinds of data and their implications for data re-use. Journal of Computer-Mediated Communication, 12(2). Retrieved from http://jcmc.indiana.edu/issue2/carlson.html.
- Council on Governmental Relations. (2006). Access to and retention of research data: Rights and responsibilities Retrieved July 17, 2009, from http://22.214.171.124/docs/DataRetentionIntroduction.htm.
- Data’s Shameful Neglect [Editorial]. (2009). Nature, 461(7261), p. 145.Google Scholar
- Davidson, S., & Friere, J. (2008). Provenance and scientific workflows: Challenges and opportunities: SIGMOD’08, Vancouver,BC,Canada, June 9–12, 2008, pp. 1–6.Google Scholar
- De Roure, D., Goble, C., Bhagat, J. et al. (2008). myExperiment: Defining the Social Virtual Research Environment: 4th IEEE International Conference on e-Science, Indianapolis, Indiana, December, 2008.Google Scholar
- Faniel, I. M. (2009). Unrealized potential: The socio-technical challenges of a large scale cyberinfrastructure initiative retrieved July 17, 2009, from http://hdl.handle.net/2027.42/61845.
- Freire, J., Silva, C. T., Callahan, S. P. et al. (2006). Managing rapidly-evolving scientific workflows: PAW’06 International Provenance and Annotation Workshop, LNCS 4145, Chicago, Illinois, USA, May 3–5, 2006, 2006.Google Scholar
- Karasti, H., & Baker, K. S. (2008). Digital data practices and the long term ecological research program growing global. The International Journal of Digital Curation, 3(2), 42–58.Google Scholar
- Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.Google Scholar
- Lee, C., & Bietz, M. (2009). Barriers to the Adoption of New Collaboration Technologies for Scientists, CHI 2009, Boston, MA, April 4–9 Retrieved 26 February, 2010, from http://www.matthewbietz.org/blog/wp-content/uploads/chi2009-scientificcollaborationsposition.pdf.
- National Institutes of Health. (2003). NIH Data Sharing Policy and Implementation Guidance. Retrieved June 18, 2009. from http://grants2.nih.gov/grants/policy/data_sharing/data_sharing_guidance.htm.
- National Science Foundation. (July 10, 2008). Data Archiving Policy. Retrieved June 18, 2009. from http://www.nsf.gov/sbe/ses/common/archive.jsp.
- Sandusky, R. J., Tenopir, C., & Casado, M. M. (2008). Figure and table retrieval from scholarly journal articles: User needs for teaching and research. Proceedings of the American Society for Information Science and Technology, 44(1), 1–13.Google Scholar
- Scheidegger, C. E., Vo, H. T., Koop, D., et al. (2008). Querying and ReUsing Workflows with VisTrails: SIGMOD’08, Vancouver, BC, Canada, June 9–12, 2008, pp. 1–4.Google Scholar
- Stewart, L. (1996). User acceptance of electronic journals: Interviews with chemists at Cornell University. College & Research Libraries, 57(4), 339–349.Google Scholar
- Van House, N. A., Butler, M. H., & Schiff, L. R. (1998). Cooperative knowledge work and practices of trust: Sharing environmental planning data sets: The ACM Conference On Computer Supported Cooperative Work, Seattle, Washington, 1998, pp. 335–343.Google Scholar
- Wallis, J. C., Milojevic, S., Borgman, C. L., et al. (2006). The special case of scientific data sharing with education: The American Society for Information Science & Technology, October, 2006, pp. 169–181.Google Scholar
- Wallis, J. C., Borgman, C. L., Mayernik, M. S., et al. (2007). Know thy sensor: Trust, data quality, and data integrity in scientific digital libraries: European Conference on Research and Advanced Technology for Digital Libraries, Budapest, Hungary, 2007.Google Scholar