1. Welcome to C4: Data Sharing Across the Disciplines Terrence Bennett, The College of New Jersey Joel Herndon, Duke University Shawn Nicholson, Michigan State University Robert O’Reilly, Emory University IASSIST: Wednesday, May 27, 2009 3:45pm - 5:15pm

2. Scholarly Primitive

3. Clickstream

4. Acuity

5. Decisiveness

6. Seizes

7. Scavenger

8. Opportunistic; seldom attacking

9. Data Sharing Across the DisciplineData sharing behavior Terrence Bennett, The College of New Jersey IASSIST: May 27, 2009 An empirical study

10. Data sharing behavior  Why do researchers share?  Advance scholarship and inquiry  Comply with ethical imperatives  Support open access  Why might researchers be reluctant to share?  Need for confidentiality  Competitive advantage of secrecy  Lack of infrastructure that supports sharing  Too much trouble IASSIST: May 27, 2009

11. Study: Data sharing in life sciences*  Surveyed trainees in life sciences (and compared with computer science and chemical engineering)  Results were disturbing  23% were denied access to published data;  21% were denied access to unpublished data  8% had denied requests from others for access to data  51% reported that withholding of data had a negative effect on research progress IASSIST: May 27, 2009 * Vogeli, C. et al. (2006). Data withholding and the next generation of scientists: Results of a national survey. Academic Medicine 81(2), p. 128-136.

12. These results raise new questions  Are dissertators sharing?  Do dissertators in the life sciences share better than their counterparts in the social sciences? IASSIST: May 27, 2009

13. Methodology  Searched PQDT database  Restricted to PhD dissertations  Limited to most recent five years  Used PQDT controlled subject index (5 disciplines):  Political Science  Cell Biology  Psychology  Biochemistry  Genetics IASSIST: May 27, 2009

14. Methodology (continued)  Random sort of results from each discipline  Selected 12 from each discipline  N = 60 (not a multinational sample)  Coded for 9 variables related to presence of data and availability of data for sharing IASSIST: May 27, 2009

15. Research questions  Do abstracts and tables of contents accurately indicate the presence of data?  What is the nature of the data collected?  Origin  Functional category  Is data scarce? Valuable?  Is data automated?  Are there disciplinary differences regarding dataset use, reuse, and availability? IASSIST: May 27, 2009

16. Findings: abstracts and TOCs  Great variation in the percentage of author-supplied abstracts that indicate the use or availability of data collections IASSIST: May 27, 2009 For detailed findings, be sure to visit us during the poster session!

17. Findings: data category*  Datasets are predominantly dissertation-specific IASSIST: May 27, 2009 *National Science Foundation (2005), The elements of the digital data collections universe. Ch. 2 (p. 17-23) in Long-lived digital data collections enabling research and education in the 21 st Century).

18. Findings: data automation IASSIST: May 27, 2009

19. Findings: data availability IASSIST: May 27, 2009

20. Conclusions  Dissertators in the life sciences may be slightly better than their social sciences counterparts in depositing data in repositories.  Dissertation datasets tend to be configured to serve only the immediate need of the dissertation; this leads to interesting questions for archiving and preservation. IASSIST: May 27, 2009

21. Conclusions  Very few dissertators are embracing the open data movement.  Highly automated data collecting does not lead to increased data sharing, despite strong theoretical support for this result. IASSIST: May 27, 2009

22. Further questions / next steps IASSIST: May 27, 2009  Need stronger empirical data – larger sample; more disciplines; not limited to dissertations  Implications for saving/preserving/disseminating research data  Are disciplinary differences in data sharing behavior inevitable?  What is the role of librarians in promoting data sharing across the disciplines?