Examples
Worked examples
- Is an instance
A doctoral researcher completes the FAIR-Aware questionnaire before submitting a dataset to a repository as part of a funder's data management plan requirement, and uses the results to identify specific gaps — for example, a missing persistent identifier or an unclear reuse licence — that still need attention before deposit.
- Is an instance
A university research data service links to FAIR-Aware from its DMP guidance as a first, low-friction awareness step, before pointing researchers toward a more rigorous automated assessment for datasets that will be formally certified.
Counter-examples
Looks similar, but isn't
- Not an instance
FAIR-Aware is a manual self-assessment based on the researcher's own answers to a questionnaire, not an automated technical check of a live dataset or repository — it should not be confused with F-UJI , a separate tool that programmatically tests a resolvable dataset URL or DOI against machine-checkable FAIR metrics.
Editorial commentary
FAIR-Aware is a free, self-guided online questionnaire developed by DANS (Data Archiving and Networked Services, the Netherlands) that helps researchers and data stewards assess their own understanding of, and their dataset’s likely alignment with, the FAIR Data Principles before depositing data in a repository.
Origin and how it’s built
FAIR-Aware was developed and refined through the FAIRsFAIR EU project. Its underlying metrics draw substantially on the indicators separately developed by the RDA FAIR Data Maturity Model Working Group, so completing FAIR-Aware gives a rough proxy for how a dataset is likely to score against that more detailed indicator set — useful as an early, low-friction check before a more formal assessment.
Self-assessment, not automated testing
FAIR-Aware works by asking the researcher a structured series of questions about their own dataset and practices, and scoring the answers — it depends on the researcher’s own knowledge and honesty, not on machine-testing a live, resolvable dataset. That distinguishes it from F-UJI, a separate automated tool that takes an actual dataset identifier (a DOI or URL) and programmatically checks it against machine-testable FAIR metrics. A research office might reasonably use both: FAIR-Aware early, as a teaching and awareness tool for researchers who haven’t yet deposited data, and an automated tool such as F-UJI later, once a dataset actually has a resolvable identifier to test.
Typical use
FAIR-Aware is most often pointed to from data management plan guidance, as a first practical step for a researcher trying to work out what “make your data FAIR” concretely requires of them before choosing a repository and metadata standard.
Related terms
Machine-readable encodings
Use in your systems
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