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 adapted from 'Open data, FAIR data and RDM: the ugly duckling' by S. Jones, licensed under CC BY.
Based on KU Leuven RDM website
Data are discoverable via search engines and catalogues, have machine-readable metadata and a unique persistent identifier.
Data does not have to be openly available, but the access protocol should be clear and preferably machine-readable.
(meta)Data are interoperable when they can be combined and exchanged with other (meta)data.
Data are reusable when they are clearly structured, documented and provided with a data usage license.
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.
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.