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Board on Research Data and Information National Academy of Sciences 30 November 2010

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. Deluge!!!. Data!!. Scientists.

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Board on Research Data and Information National Academy of Sciences 30 November 2010

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  1. Research Data:Who will share what, with whom, when, and why?Christine L. BorgmanProfessor & 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 Humanists Librarians Funding agencies Policy makers http://www.guzer.com/pictures/suprise_suprise.jpg

  3. 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. 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.

  4. 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. Some purposes of data-driven research Hypothesis-driven Synoptic survey Describe phenomena Model system Experimental Theoretical Short term Long term

  6. Some methods of data-driven research Individual investigator Collaborative teams Hand-collect samples Machine-collect samples Hand markup Machine markup Local control of data Community repositories

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

  8. 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 Image source: www.buildingsrus.co.uk/.../ target1.htm

  9. 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 toposytropos.com.ar projects.kmi.open.ac.uk athensacademy.net

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

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

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

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

  14. 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 http://pzwart.wdka.hro.nl/mdr/research/lliang/mdr/mdr_images/opencontent.jpg/

  15. Implications for scholarship - 1 • 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”? http://serc.carleton.edu/cismi/researchonlearning/

  16. Implications for scholarship - 2 • 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? http://serc.carleton.edu/cismi/researchonlearning/

  17. Implications for regulation • 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 Lessig, Free Culture, 2004, p125

  18. 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. 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

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