Repositories for Research Data : Citing Data

Citing data is under discussion worldwide. Topics include; the format to cite the data, listing data with references, working with publishers and researchers to include data citations in publications, and using PIDs (ARKs and DOIs) to identify and link data.

A Joint Declaration of Data Citation Principles has just been issued by CODATA. For background information see the blog.

Principles

The Data Citation Principles cover purpose, function and attributes of citations.  These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable.  

These citation principles are not comprehensive recommendations for data stewardship.  And, as practices vary across communities and technologies will evolve over time, we do not include recommendations for specific implementations, but encourage communities to develop practices and tools that embody these principles.

The principles are grouped so as to facilitate understanding, rather than according to any perceived criteria of importance.

1. Importance

   Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications.

2. Credit and Attribution

   Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data.

3. Evidence

   In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited.

4. Unique Identification

   A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community.

5. Access

   Data citations should facilitate access to the data themselves and to such associated metadata, documentation, code, and other materials, as are necessary for both humans and machines to make informed use of the referenced data.

6. Persistence

   Unique identifiers, and metadata describing the data, and its disposition, should persist --  even beyond the lifespan of the data they describe[6].

7. Specificity and Verifiability 

   Data citations should facilitate identification of, access to, and verfication of the specific data that support a claim.  Citations or citation metadata should include information about provenance and fixity sufficient to facilitate verfiying that the specific timeslice, version and/or granular portion of data retrieved subsequently is the same as was originally cited.

8. Interoperability and flexibility

   Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities.

Also check out DataCite for further information.