Directory of external FAIR assessment resources
16 resources shown
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Tools that assess datasets or other digital objects identified by a DOI or a web address, and questionnaires that assess a dataset or the knowledge of the person answering.
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The ARDC FAIR Data Self-Assessment Tool is an online questionnaire of 12 questions in four sections (findable, accessible, interoperable and reusable) about a research dataset. It is designed for researchers, data librarians and IT staff.
Use case: Reviewing a dataset by answering questions about it, without entering an identifier.
F-UJI is a web service that assesses the FAIRness of research data objects at the dataset level, based on the FAIRsFAIR Data Object Assessment Metrics. The web form takes a DOI or URL and can also take a metadata service endpoint (OAI-PMH, OGC CSW or SPARQL).
Use case: Assessing a published dataset that has a DOI or a landing page URL.
FAIR-Aware is an online questionnaire of 10 questions that asks whether the person answering knows the requirements for making a dataset FAIR and how willing the person is to comply with them. It assesses the knowledge of the person, not a dataset.
Use case: Reviewing one's own knowledge of the FAIR data requirements before depositing a dataset in a repository.
FAIR Champion runs FAIR Metric Tests registered in the OSTrails Test Registry, and Benchmark Quality Assessments built from them, against a digital object identified by its GUID (a URL, a DOI or another resolvable identifier). Its README describes it as an evolution of the FAIR Evaluator.
Use case: Assessing a digital object that has a DOI or URL with a community-defined benchmark algorithm.
FAIR-Checker tests the metadata of a web resource, identified by a URL or a DOI, against 12 metrics for the FAIR principles and gives a recommendation for each metric. A separate Inspect page shows the metadata retrieved from the resource and runs metadata quality checks.
Use case: Assessing the metadata that a web resource exposes, one metric at a time or all together.
FAIR enough runs a collection of FAIR Maturity Indicator tests, written in Python, against a resource identified by its URL. DOIs and handles are accepted by most collections.
Use case: Assessing a resource that has a URL against a chosen collection of maturity indicator tests.
FAIR Evaluator is the FAIR Maturity Evaluation Service. Its web front-end runs collections of Maturity Indicator tests against a resource identified by a GUID (a DOI, a handle, an InChI key or a URL), and lets users register tests and collections.
Use case: Assessing the metadata record of a resource that has a DOI or URL against a chosen collection of maturity indicator tests.
FAIRshake is a toolkit for community-defined FAIR metrics and rubrics, paired with manual and automated assessments of digital objects such as datasets, software tools, databases, APIs and workflows. Starting a project requires an account.
Use case: Assessing a registered digital object with a rubric chosen or defined by a community.
Resources that assess ontologies, software or repositories instead of datasets.
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FAIR EVA (Evaluator, Validator & Advisor) is open source software that checks the FAIRness of a digital object held in a repository or data portal, using RDA indicators, through an API and an optional web interface. This entry points to the source code, which each institution deploys itself; an instance named for DIGITAL.CSIC is published at https://fair.csic.es.
Use case: Deploying an evaluator for an institutional repository, with configurable metadata terms and plugins.
FOOPS! (Ontology Pitfall Scanner for the FAIR Principles) assesses whether a vocabulary written in OWL or SKOS conforms to the FAIR principles. Its source code can run as a server or from the command line.
Use case: Assessing a vocabulary that is published with a URI, or running the assessment locally from the source code.
O'FAIRe is a methodology and Java web service that scores an ontology or other semantic resource with 61 questions on the 15 FAIR principles, using the metadata held in an OntoPortal ontology repository. Its README names deployments in AgroPortal, SIFR BioPortal and IndustryPortal.
Use case: Assessing an ontology that is stored in an OntoPortal repository with metadata descriptions.
Metric sets and maturity models that tools implement. They are documents, not runnable tools.
No resources in this section match the selected filters.
The FAIR-IMPACT page on metrics for data. It describes the 17 domain-agnostic metrics refined from the FAIRsFAIR work (version 0.8) and 5 social sciences metrics that can replace their domain-agnostic counterparts.
A specification of 17 minimum viable metrics for measuring how FAIR research data objects are. Version 0.8 (March 2025) is a major revision of the 2020 FAIRsFAIR metrics.
The specification and guidelines of the FAIR Data Maturity Model: a set of indicators, with priorities and maturity levels, for evaluating the FAIR principles. Version 1.0 was published on Zenodo on 2020-06-25.
Other catalogues of FAIR tools and services.
No resources in this section match the selected filters.
A browsable online catalogue of resources (tools, solutions, methods and approaches) for FAIR implementation, part of the FAIR Implementation Framework. Its resources page showed 54 entries on 2026-10-25.
A web application, part of the educational component of FAIRsharing, whose Tools page lists resources for assessing digital objects against the FAIR principles (33 entries on 2026-10-25). Its Registry page offers tabular and faceted views of principles, metrics and benchmarks.
Find the row that matches what you have at hand. The tools listed accept that input.
| Starting data | Tools that accept it |
|---|---|
| DOI The tool resolves the DOI and reads the metadata published for it. | |
| Landing page URL The tool reads the metadata embedded in the page or linked from it. | |
| Metadata file The tool accepts a metadata file or an ontology file supplied by the user. | |
| None The tool starts from a questionnaire and needs no identifier. |