BROWN BAG TALK WITH MICAH ALTMAN INTEGRATING OPEN DATA INTO OPEN ACCESS JOURNALS
Principles for Data Citation Micah Altman, Institute for Quantitative Social Science, Harvard...
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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
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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