01
Where it fits and where it does not
Use these four checks before committing implementation time.
- Use it when
- Use as the evidence rubric that turns FAIR from an aspiration into a repeatable release and improvement assessment.
- Limits
- It is not a certification, and locally adapted scoring or weighting means totals from different assessment tools are not automatically comparable.
- Best for
- Cross-cutting teams working across Plan → Acquire → Harmonize → Exchange → Learn + reuse.
- Maturity
- EstablishedSuitable for production assessment. Pin the exact release and any implementation profile.
02
See it in the workflow
This view shows the input, the change the standard introduces, and the resulting output.
- InputWhat starts
Cross-cutting source data, metadata, and local mappings
- FAIR DMMWhat changes
Use FAIR DMM as a pinned framework across Plan → Acquire → Harmonize → Exchange → Learn + reuse
- OutputWhat becomes possible
A handoff the next system or team can validate against the same release
03
A concrete example
A release gate evaluates data and metadata separately against the essential, important, and useful indicators, recording evidence, exceptions, and changes between versions.
Why it matters: Tests machine-actionability around identifiers, access, knowledge representation, licensing, provenance, and community standards, but not representativeness, label accuracy, privacy, or predictive fitness.
04
What it fits with
Operationalizes FAIR; DQV can publish resulting measurements, while SHACL, repository checks, and domain validators supply evidence.
- FrameworkFAIR
Both support Cross-cutting work and meet around Plan, Acquire, Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata vocabularyDPV
Both support Cross-cutting work and meet around Plan, Acquire, Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - StandardISO/IEC 5259
Both support Cross-cutting work and meet around Plan, Acquire, Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - FrameworkData Cards
Both support Cross-cutting work and meet around Plan, Acquire, Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship
05
Implementation starter
Start with one bounded handoff. Pin, test, and review it before scaling.
Define one handoff, its accountable owner, and the decision FAIR DMM must support.
Pin the exact version and companion artifacts: 1.0 · Endorsed RDA Recommendation · 2020.
Map one representative input to the required framework artifacts.
Test the result against the canonical source and record every exception.
Preserve the source data, mappings, and review evidence before scaling.
06
Test the main limitation
It is not a certification, and locally adapted scoring or weighting means totals from different assessment tools are not automatically comparable.
Run one representative end-to-end pilot and record exactly where FAIR DMM loses context, needs an extension, or depends on another standard.
Machine-readable output may still be unfit for analysis or ML.
Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Tests machine-actionability around identifiers, access, knowledge representation, licensing, provenance, and community standards, but not representativeness, label accuracy, privacy, or predictive fitness.
07
Official resources
Specifications, diagrams, examples, and guides from the organizations that maintain them.
RDA FAIR Data Maturity Model Recommendation
Official publisher or steward guidance for this framework profile.
- Publisher
- Research Data Alliance FAIR Data Maturity Model Working Group