Examples
Worked examples
- Is an instance
A repository manager checks a dataset against the RDA model's indicators and finds it meets the Findable indicators (it has a persistent identifier and rich metadata) but only partially meets Interoperable (its metadata uses a structured schema, but not one built from FAIR-compliant vocabularies) — giving concrete, specific next steps rather than a single opaque score.
- Is an instance
An automated tool such as F-UJI implements a machine-testable subset of the RDA model's indicators, so a dataset's FAIRness can be scored programmatically from its resolvable identifier rather than requiring a manual review against every indicator.
Counter-examples
Looks similar, but isn't
- Not an instance
A generic statement in a data management plan that "we follow FAIR data principles" is not an RDA FAIR Data Maturity Model assessment — the model requires evaluating specific, named indicators against an actual dataset, not stating an intention to comply with FAIR in general terms.
Editorial commentary
The RDA FAIR Data Maturity Model is a common, community-endorsed set of indicators for assessing how well a specific dataset implements the FAIR Data Principles, published by the Research Data Alliance (RDA)‘s FAIR Data Maturity Model Working Group.
Why it exists
By the late 2010s, a proliferation of different, mutually incompatible approaches to “assessing FAIRness” had emerged across projects and infrastructures, making it hard to compare results or trust that “FAIR” meant the same thing from one assessment to the next. The RDA Working Group built the FAIR Data Maturity Model specifically to harmonise these divergent approaches into one shared, reusable indicator set that other tools and projects could adopt rather than reinventing.
How the model is structured
Instead of a single pass/fail judgment, the model breaks each of the four FAIR principles — Findable, Accessible, Interoperable, Reusable — into specific, individually checkable indicators. Each indicator can be evaluated against a given dataset as fully met, partially met, or not met, producing a structured maturity picture that shows exactly which aspects of FAIRness a dataset achieves and which still need work, rather than a single opaque score.
Adoption
The FAIRsFAIR EU project formally adopted the RDA model’s specification and guidelines as the basis for its own FAIR-assessment work, and DANS’s FAIR-Aware self-assessment questionnaire draws its metrics substantially from these same RDA indicators — so the RDA model functions as a shared reference point behind more than one downstream assessment tool, rather than a single standalone product.
Manual vs. automated assessment
Because the RDA model’s indicators are described at a conceptual level, applying them can be done manually (a repository manager or data steward reviewing a dataset indicator by indicator) or, for the subset of indicators that are machine-testable, through an automated tool such as F-UJI, which programmatically checks a dataset’s resolvable identifier against a testable subset of the model’s indicators.
Related terms
Machine-readable encodings
Use in your systems
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