The 8 Data Citation Principles

Data citation principle

The data citation principles are the eight normative rules — importance, credit and attribution, evidence, unique identification, access, persistence, specificity and verifiability, and interoperability — set out in FORCE11’s 2014 Joint Declaration of Data Citation Principles, defining what it means to cite a dataset properly rather than merely mentioning it in prose.

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· Last updated 22 Aug 2026

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The data citation principles are eight normative statements about what it means to cite a dataset properly. They were set out in FORCE11’s 2014 Joint Declaration of Data Citation Principles — this term covers the content of the eight principles themselves; see that linked term for the document’s own history and governance.

The eight principles

(1) Importance — data citations should be accorded the same importance in the scholarly record as citations to published literature. (2) Credit and attribution — citations should facilitate credit and legal attribution to all contributors to the data, recognising that a single style may not apply to all data. (3) Evidence — in publications relying on data, a citation should be provided for every dataset used, even if the data is not deposited elsewhere. (4) Unique identification — a citation should include a method for identifying the dataset that is machine-resolvable, globally unique, and widely used by a community (typically a DataCite DOI). (5) Access — citations should facilitate access to the data itself and its metadata, documentation, and other materials necessary for its interpretation. (6) Persistence — identifiers and metadata should persist, even beyond the life of the dataset they describe. (7) Specificity and verifiability — citations should identify precisely the version and subset of data used, to support verification and reuse. (8) Interoperability and flexibility — citation methods should be as machine-interoperable as possible across systems, and accommodate community and technology variation.

Why this matters in practice

Applying these principles is what distinguishes a proper data citation from an informal data mention: a proper citation lets a reader locate the exact dataset version used, verify a study’s results, and gives the data’s creators the same scholarly credit an author receives for a cited paper. The Declaration has been endorsed by over a hundred publisher, funder, and infrastructure organisations, and its principles underpin later, more implementation-focused work such as the FAIR Principles and journal data-availability requirements.

References

Data Citation Synthesis Group, ‘Joint Declaration of Data Citation Principles’, FORCE11, San Diego, 2014.

Use in your systems

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Schema.org DefinedTerm (JSON-LD)

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