principles for data citation micah altman, institute for quantitative social science, harvard...

Principles for Data Citation

Micah Altman, Institute for Quantitative Social Science, Harvard University

Prepared for DataCite's Summer Meeting: Data and the Scholarly Record, the

Changing LandscapeAugust 23-24, 2011

Collaborators*

Principles for Data Citation

Leonid Andreev, Ed Bachman, Adam Buchbinder, Ken Bollen, Bryan Beecher, Steve Burling, Kevin Condon, Jonathan Crabtree, Merce Crosas, Gary King, Patrick King, Tom Lipkis, Freeman Lo, Jared Lyle, Marc Maynard, Nancy McGovern, Lois Timms-Ferrarra, Akio Sone, Bob Treacy

Research SupportThanks to the Library of Congress (PA#NDP03-1), the

National Science Foundation (DMS-0835500, SES 0112072), IMLS (LG-05-09-0041-09), the Harvard University Library, the Institute for Quantitative Social Science, the Harvard-MIT Data Center, and the Murray Research Archive.

* And co-conspirators

Related Work

Principles for Data Citation

M. Altman,2008, "A Fingerprint Method for Verification of Scientific Data" in, Advances in Systems, Computing Sciences and Software Engineering, (Proceedings of the International Conference on Systems, Computing Sciences and Software Engineering 2007) , Springer Verlag.

M. Altman and G. King. 2007. “A Proposed Standard for the Scholarly Citation of Quantitative Data”, D-Lib, 13, 3/4 (March/April).

G. King, 2007, " An Introduction to the Dataverse Network as an Infrastructure for Data Sharing", Sociological Methods and Research, Vol. 32, No. 2, pp. 173-199

Principles for Data Citation

(19 Ways of Looking at Data)

^Citations

AKA

Principles for Data Citation

Com

mon

Prin

cipl

es

Thanks to 37 Participants

Principles for Data Citation

Motivations Elements

Citing Data Virtual Archives

Principles for Data Citation

What are we talking about?

Workflow

Workflow

Workflow

Principles for Data Citation

- Separatescientific principles, use cases, requirements-Distinguish syntax from presentation-Design for ecosystem & lifecycle-Incremental value for incremental effort

Design Principles

Principles for Data Citation

Theory

Principles for Data Citation

Theory +

Data citations should be first class objects for publication -- appear with citation; should be as easy to cite as other works

At minimum, all data necessary to understand assess extend conclusions in scholarly work should be cited

Citations should persist and enable access to fixed version of data at least as long as citing work

Data citation should support unambiguous attribution of credit to all contributors, possibly through the citation ecosystem

Theory + Practice

Principles for Data Citation

Linking Data to Publications through Citation and Virtual Archives

Use Cases

Principles for Data Citation

Use Cases (details)Operational Constraints?

-Syntax-Interoperability-Technical contexts of use

Principles for Data Citation

Operational Requirements?

Syntax Metadata Interoperability Core technical contexts of use

Actors

Actors

Actors

Actors

Use Cases+ Requirements + Scientific Principles

Data citations should be first class objects for publication -- appear in references; be as easy to reference as other works

All data necessary to assess conclusions in scholarly work should be cited Citations should persist and enable access to fixed version of data,

for as long as the citing works exist Citation should support unambiguous attribution of credit to all contributors,

(possibly through the citation ecosystem of metadata, indices, etc.) Separate presentation and content

• The article is (only) a summary of the research• Science requires reproducibility• Scientific disciplines require a common evidence base

Scientific Principles

Requirements

Attribution – legal & scientific Persistence – persistence of reference; identify responsible curators Access – short & long term; machine & human Discovery – locate instances; discover derivative, parent, and related works Provenance – associate scientific claim and evidence; verify fixity of evidence

Key Use Cases

Principles for Data Citation

Simple P

Principles for Data Citation

- Semantic:Persistent ID, Author, Title, Version (or date)

- Presentation:Any styleGrouped other referencesActionable in context

- PolicyIf its scientific evidence, cite itOffer credit to all contributors

Simple Proposal

Contact Us

Principles for Data Citation

Micah Altman

maltman.hmdc.harvard.edu

The Dataverse Network ®

thedata.org