Examples
Worked examples
- Is an instance
A national research-infrastructure consortium creates a FIP declaring, for each relevant FAIR sub-principle, a specific choice — for example: metadata standard = DataCite Metadata Schema; identifier scheme = DOI via DataCite; repository = a named certified repository; access protocol = OAI-PMH. Other communities can then inspect that FIP and reuse or interoperate with the same technology stack instead of making the same decisions from scratch.
- Is an instance
The Data Management Plan (DMP) community's machine-actionable DMP work treats a FIP as a companion artifact to the DMP itself — the DMP describes what data will be produced and why, while the FIP records the concrete technical infrastructure choices behind the FAIR claims the DMP makes.
Counter-examples
Looks similar, but isn't
- Not an instance
A general statement that "our data will be FAIR" in a grant application or DMP is not a FIP. A FIP has to name specific, concrete technology choices — a named repository, a named metadata schema, a named identifier scheme — for each relevant FAIR sub-principle, not an aspiration or a restatement of the FAIR principles themselves.
Editorial commentary
A FAIR Implementation Profile (FIP) is a structured, machine-actionable declaration of the specific technology choices a research community makes to implement the FAIR Data Principles — naming, for each FAIR sub-principle, a concrete repository, metadata standard, identifier scheme, vocabulary, or access protocol rather than a general statement of intent. Each declared choice is called a FAIR Enabling Resource (FER).
Where a FIP comes from
FIPs originate with the GO FAIR Foundation, which developed a structured question-and-answer methodology for capturing them — traditionally run as facilitated “FIP Wizard” workshops with community stakeholders, and increasingly available as a self-service tool (FIP Wizard 4.0, built on the same technology stack as the Data Stewardship Wizard). The output is published openly, in both machine-actionable form (compatible with nanopublications, so a FIP can itself be queried and reused programmatically) and human-readable exports (PDF, Word, Excel, CSV).
Why communities produce them
The FAIR Principles deliberately don’t mandate specific technologies — they describe outcomes (data should be findable, accessible, interoperable, reusable) and leave the technical implementation to each community. That flexibility is useful but also means two research communities can both claim to be “FAIR” while making completely incompatible technology choices underneath. A FIP makes those choices explicit and comparable: once published, a FIP is itself a reusable resource that other communities can inspect, adopt in part, or use as a starting point, rather than repeating the same technology-selection exercise independently.
How a FIP relates to a Data Management Plan
A Data Management Plan (DMP) describes what data a specific project will produce, how, and under what conditions it will be shared. A FIP operates at a different level: it’s typically produced once by a community or infrastructure (not per-project) and records the concrete technical stack — which repository, which metadata schema, which identifier scheme — that projects following that community’s practice will actually use to make their FAIR claims real. RDA’s DMP Common Standard work treats FIPs as a companion, more granular artifact that machine-actionable DMP tooling can reference rather than duplicate.
What a FIP is not
A FIP is not a substitute for assessing whether a given dataset is actually FAIR — that’s the role of assessment frameworks such as the RDA FAIR Data Maturity Model and self-assessment tools such as FAIR-Aware. A FIP records what technology choices a community has committed to; a maturity-model assessment checks whether a specific dataset actually meets FAIR indicators in practice. The two are complementary: a well-designed FIP makes it easier for datasets produced under it to score well on a maturity-model assessment, but doesn’t guarantee it.
Related terms
Machine-readable encodings
Use in your systems
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