The document summarizes a joint statement from a data citation synthesis group on the principles of linkability and citability of research data. The group was made up of 36 members from around 20 organizations. They compared existing data citation principles and merged them into a consensus set of 8 principles. The principles state that data should be considered citable research products, attribution should be given to all data contributors, data citations should be provided when claims rely on data, citations should include persistent unique identifiers, citations should facilitate access to data and metadata, metadata and identifiers should persist beyond data lifespan, citations should include version/subset details, and citation methods should be flexible enough within communities but not differ so much as to compromise interoperability.

1. Citing Data
Ed Pentz
Executive
Director

3. Joint Statement on Linkability
and Citability of Research
Data:

4. Joint Statement on Linkability
and Citability of Research
Data:

5. Lots of activity

6. Data Citation Synthesis Group
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36 members, ~ 20 organizations
Weekly meetings for 3 months, led by Force 11
Comparison of 4 sets of principles:
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Amsterdam Manifesto
CoData Data Citation Principles
DataCite Data Citation Principles
Digital Data Center Data Citation Principles
Merge them to create a synthesis set of
principles - high level and simple
http://www.force11.org/node/4381

7. The CONSENSUS: 8
principles

8. 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.

9. 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.

10. 3. EVIDENCE
• Where a specific claim rests upon
data, the corresponding data citation
should be provided.

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

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

13. 6. Persistence
• Metadata describing the data, and
unique identifiers should persist,
even beyond the lifespan of the data
they describe.

14. 7. VERSIONING AND
GRANULARITY
• Data citations should facilitate
identification and access to different
versions and/or subsets of data.
Citations should include sufficient
detail to verifiably link the citing work
to the portion and version of data
cited.

15. 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.

20. Text-mining can be used to extend structured data citation, and could
be a basis for the development of services to help authors or editors
to add structured content at the beginning of the publication process,
rather than after the fact.
Kafkas Ş, Kim J-H, McEntyre JR (2013) Database Citation in Full Text
Biomedical Articles. PLoS ONE.
Available: http://dx.doi.org/10.1371/journal.pone.0063184

22. Summary