FAIR data
View the content of this page
FAIR data vs. open data
FAIR data are data that are findable (F), accessible (A), interoperable (I) and reusable (R).
Research data that are well managed are essential to qualitative and efficient research. You can go one step further and make your data FAIR, if you apply machine-readable, standardized (meta)data, persistent identifiers, data usage licenses and open formats.
Open data are data that are openly available with an open license.
FAIR data and open data are not the same. On the one hand, FAIR data does not necessarily have to be open, and on the other, open data are not always FAIR, or even properly managed. However, all funders recommend or require that research data are FAIR and as open as possible.
Image from 'Open data, FAIR data and RDM: the ugly duckling', CC BY S. Jones How can you make your data FAIR?
Based on KU Leuven RDM website
FINDABLE
Data are discoverable via search engines and catalogues, have machine-readable metadata and a unique persistent identifier.
ACCESSIBLE
Data does not have to be openly available, but the access protocol should be clear and preferably machine-readable.
- Apply a free and standardized access protocol. (e.g. a download link, an authentication/authorization procedure, a phone number, email address, …)
- The metadata should always remain accessible, even if the data are not (anymore).
INTEROPERABLE
(meta)Data are interoperable when they can be combined and exchanged with other (meta)data.
- Use standardized language in your (meta)data: controlled vocabularies, ontologies and thesauri that are common in your discipline and well documented.
- Use a (meta)data model that is common in your discipline and well documented.
- Use standard, open file formats instead of proprietary ones.
REUSABLE
Data are reusable when they are clearly structured, documented and provided with a data usage license.
- Add documentation and metadata to your data describing the provenance, methodology, procedures, limitations, variables, etc.
- Apply a data usage license stating what other people can or cannot do with your data.
|
|
|---|
Recommended RDM tools for FAIR data:
FAIR knowledge quiz
FAIR-Aware checklist comprises ten questions about the FAIR Principles, assessing your knowledge of the principles and providing guidance to improve your knowledge about FAIR.
FAIRness self-assessment
Self-Assessment Tool to Improve the FAIRness of Your Dataset, SATIFYD, is a questionnaire to assess how FAIR your dataset is and provides tips to improve the FAIRness before submitting your data to a data repository.
Automated FAIRness assessment
F-UJI is a web service that automatically assesses how FAIR your dataset is after submitting your data to a data repository.