Joint Declaration of Data Citation Principles - FINAL

LIST OF ENDORSEMENTS

Principles created by the Data Citation Synthesis Group 

Preamble

Sound, reproducible scholarship rests upon a foundation of robust, accessible data.  For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record.  In other words, data should be considered legitimate, citable products of research.  Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.

In support of this assertion, and to encourage good practice, we offer a set of guiding principles for data within scholarly literature, another dataset, or any other research object.  

These principles are the synthesis of work by a number of groups.  As we move into the next phase, we welcome your participation and endorsement of these principles.
 

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[1].

  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[2].

  3. Evidence

    In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited[3].

  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[4].

  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[5].

  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[7].

  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[8].

    For further information glossary, examples and references