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 InformationNational Academy of Sciences

30 November 2010

Deluge!!!

Data!!Data!!

ScientistsScientists

Social ScientistsSocial Scientists

Funding agenciesFunding agencies Policy makersPolicy makers

HumanistsHumanists

LibrariansLibrarians

http://www.guzer.com/pictures/suprise_suprise.jpg

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.

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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, 20055

Some purposes of data-driven research

Hypothesis-driven Synoptic survey

Model system

Experimental Theoretical

Long term

Describe phenomena

Short term

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Some methods of data-driven research

Hand-collect samples

Collaborative teamsIndividual investigator

Machine-collect samples

Hand markup Machine markup

Community repositoriesLocal control of data

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Researchers’ incentives to share data

• Open science, scholarship• Recognition• Collaboration • Reciprocity• Coercion

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Image source:www.buffaloworks.us/ images/sharing%20orangs.jpg

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

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

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

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athensacademy.net

toposytropos.com.ar

Public good / user arguments

1. Public monies should serve the public good

2. With data, anyone can be a scientist

11http://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

Scientific good / producers arguments

3. Data curation advances science

4. Www5. With data, results can be reproduced

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http://chemistry.curtin.edu.au/research/index.cfm

http://serc.carleton.edu/cismi/broadaccess/groupwork.html

WISE image Worldwide Telescope

Motivations and interests in sharing data

Interests of data producersInterests of data producers

Science-driven motivationsScience-driven motivations

Interests of data usersInterests of data users

4. Reproducibility

2. Ask new questions2. Ask new questions

1. Public goods1. Public goodsPublic-driven motivationsPublic-driven motivations

3. Advance science

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Enabling Virtual Conversations

Curators Consumers

Science-Ready Data

Products

Screening and

Processingby Data Valets

Initial quarantine

data results

Screening by

Curators

Quarantine data

results

Quarantine Database and Data CubeScience Database and Data Cubes

Authors

Original Data

Archive

Data Valets

PublishersData

Curators ConsumersAuthors

Data Valets

Publishers

Reports, Hosted Excel PivotTables and Charts

Excel and MatLab Interface

Blog, maps, data explanations, protected data download and web parts for each role

Quarantine Data Science Data

Sharepoint Collaboration Web Portal

Collaboration- Centric View

Data-Centric View

Slide courtesy of Catherine van Ingen, Microsoft Research

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

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• 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/

• 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/

• 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

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

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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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