1. Research Data: Who will share what, with whom, when, and why? Christine L. Borgman Professor & Presidential Chair in Information Studies University of California, Los Angeles Board on Research Data and Information National Academy of Sciences 30 November 2010

2. Deluge!!! Data!! Scientists Social Scientists Funding agencies Policy makers Humanists Librarians http://www.guzer.com/pictures/suprise_suprise.jpg

4. Dissemination and Sharing of Research Results NSF Data Sharing Policy Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing. See Award & Administration Guide (AAG) Chapter VI.D.4.Award & Administration Guide (AAG) Chapter VI.D.4 NSF Data Management Plan Requirements Beginning January 18, 2011, proposals submitted to NSF must include a supplementary document of no more than two pages labeled “Data Management Plan”. This supplementary document should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. See Grant Proposal Guide (GPG) Chapter II.C.2.j for full policy implementation.Grant Proposal Guide (GPG) Chapter II.C.2.j 4

5. What are data? Categories of data* Observational Computational Experimental Records What data to keep? Why? Who cares? http://datalib.ed.ac.uk/GRAPHICS/blue_data.gif *Long-Lived Data, NSF, 2005 5

6. Some purposes of data-driven research Hypothesis-drivenSynoptic survey Model system ExperimentalTheoretical Long term Describe phenomena Short term 6

7. Some methods of data-driven research Hand-collect samples Collaborative teamsIndividual investigator Machine-collect samples Hand markupMachine markup Community repositoriesLocal control of data 7

8. Researchers’ incentives to share data Open science, scholarship Recognition Collaboration Reciprocity Coercion 8 Image source: www.buffaloworks.us/ images/sharing%20orangs.jpg

9. Researchers’ incentives not to share Lack of rewards Labor to document data Competition, priority of claims Intellectual property Control over data and sources Access to data and sources 9 Image source: www.buildingsrus.co.uk/.../ target1.htm

10. Arguments for sharing research data Motivations – Means to advance scientific research – Promote the public good Interests served – Producers of scientific data – Users of scientific data 10 projects.kmi.open.ac.uk athensacademy.net toposytropos.com.ar

11. Public good / user arguments 1.Public monies should serve the public good 2.With data, anyone can be a scientist 11 http://digitalassetmanagement.org.uk/2010/02/01/the-winds-of-change-are-blowing-in-the-clouds-favor/ data discovery http://annualreport.ucdavis.edu/2008/images/photos/discovery.jpg

12. Scientific good / producers arguments 3.Data curation advances science 4.Www 2.With data, results can be reproduced 12 http://chemistry.curtin.edu.au/research/index.cfm http://serc.carleton.edu/cismi/broadaccess/groupwork.html WISE image Worldwide Telescope

13. Motivations and interests in sharing data Interests of data producers Science-driven motivations Interests of data users 4. Reproducibility 2. Ask new questions 1. Public goods Public-driven motivations 3. Advance science 13

14. Enabling Virtual Conversations Collaboration- Centric View Data-Centric View Slide courtesy of Catherine van Ingen, Microsoft Research

15. Why openness matters Interoperability trumps all Import and export in open formats Mixup and mashup Add value Avoid lock in Discoverability of related Documents Data Assorted digital objects Usability and reusability For research For learning 15 http://pzwart.wdka.hro.nl/mdr/research/lliang/mdr/mdr_images/opencontent.jpg/

16. Scholarly information infrastructure – Enable and promote new kinds of scholarship – Distributed, collaborative, open access to scholarly work Lack of clear guidelines for sharing data – What are considered to be data? – What is “incremental cost”? – What is “reasonable period of time”? Implications for scholarship - 1 http://serc.carleton.edu/cismi/researchonlearning/

17. Who is responsible for implementation, costs? – PI, grad students, department, university, library? – Curate for duration of grant or to the end of time? How to assign credit for new forms of scholarly contributions? Implications for scholarship - 2 http://serc.carleton.edu/cismi/researchonlearning/

18. Clear guidelines for sharing scholarly products – Based on practices within and between fields – Flexible and innovative – Avoid lowest common denominator Identify stakeholders and costs – Investigators, students, post-docs… – Universities, libraries, research institutes … Develop policy, technology, and practice – Ownership, access, and control of scholarly products – Credit for scholarly contributions – Value chain of scholarly artifacts Implications for regulation Lessig, Free Culture, 2004, p125

19. Conclusions Data sharing scenarios – Release all of the data, all of the time, to anyone – Release none of the data, at any time, to anyone – Release some of the data, under certain conditions, to some of the people Science-driven data curation – Examine policy arguments – Recognize data diversity – Identify stakeholders Motivations Interests – Engage stakeholders http://plus.maths.org/content/text-bytes-and-videotape 19

20. Acknowledgements Paper comments: CENS Data Practices team at UCLA – David Fearon, Matthew Mayernik, Katie Shilton, Jillian Wallis, and Laura Wynholds; Paul Uhlir of the National Academies. Audience comments on prior versions of this talk – China-North America Library Conference, Beijing, September, 2010 – Santa Fe Institute, November, 2010 Research funding: – National Science Foundation CENS: Cooperative Agreement #CCR-0120778, D.L. Estrin, UCLA, PI. CENS Education Infrastructure: #ESI- 0352572, W.A. Sandoval, PI; C.L. Borgman, co-PI. Towards a Virtual Organization for Data Cyberinfrastructure, #OCI-0750529, C.L. Borgman, UCLA, PI; G. Bowker, Santa Clara University, Co-PI; T. Finholt, University of Michigan, Co-PI. Monitoring, Modeling & Memory: Dynamics of Data and Knowledge in Scientific Cyberinfrastructures: #0827322, P.N. Edwards, UM, PI; Co-PIs C.L. Borgman, UCLA; G. Bowker, SCU; T. Finholt, UM; S. Jackson, UM; D. Ribes, Georgetown; S.L. Star, SCU) Data Conservancy: OCI0830976, Sayeed Choudhury, PI, Johns Hopkins University. – Microsoft External Research: Tony Hey, Lee Dirks, Catherine van Ingen, Catherine Marshall 20