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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

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

Board on research data and information national academy of sciences 30 november 2010




Social Scientists



Funding agencies

Policy makers

Dissemination and sharing of research results
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.

What are data
What are data?

Categories of data*

  • Observational

  • Computational

  • Experimental

  • Records

    What data to keep?

  • Why?

  • Who cares?

*Long-Lived Data, NSF, 2005

Some purposes of data driven research
Some purposes of data-driven research


Synoptic survey

Describe phenomena

Model system



Short term

Long term

Some methods of data driven research
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

Researchers incentives to share data
Researchers’ incentives to share data

  • Open science, scholarship

  • Recognition

  • Collaboration

  • Reciprocity

  • Coercion

Image source: images/sharing%20orangs.jpg

Researchers incentives not to share
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: target1.htm

Arguments for sharing research data
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

Public good user arguments
Public good / user arguments

  • Public monies should serve the public good

  • With data, anyone can be a scientist



Scientific good producers arguments
Scientific good / producers arguments

  • Data curation advances science

  • Www

  • With data, results can be reproduced

WISE image

Worldwide Telescope

Motivations and interests in sharing data
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

Enabling virtual conversations
Enabling Virtual Conversations

Collaboration- Centric View



Slide courtesy of Catherine van Ingen, Microsoft Research

Why openness matters
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

Implications for scholarship 1
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”?

Implications for scholarship 2
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?

Implications for regulation
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


  • 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


  • 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