FAIR is a standard for metadata first published in 2016 in the Scientific Data journal. It laid out a framework proposed by scientists and organizations for making digital assets more usable and shareable within research communities. Specifically, FAIR Data Principles refer to “good” research data as being –
- (F)indable – data should have sufficient descriptive metadata to allow for discovery and searching by data consumers.
- (A)ccessible – both data and metadata, when appropriate, should be stored in a reliable, centralized repository.*
- (I)nteroperable – metadata should be standardized (e.g. controlled vocabularies) with a shared, well understood language.
- (R)eusable – data collections should have appropriate licensing for repeated usage. The original or provenance of the data should be known and well documented.
*It should be noted that making research data accessible does not assume that ALL data should be made accessible to consumers. FAIR is different than the Open Science movement which states that all data is made available.
RESOURCES
GO FAIR metadata principles
https://www.go-fair.org/fair-principles/
How to FAIR [online course]
https://howtofair.dk/how-to-fair/
Luiz Olavo Bonino da Silva Santos, Kees Burger, Rajaram Kaliyaperumal, Mark D. Wilkinson; FAIR Data Point: A FAIR-Oriented Approach for Metadata Publication. Data Intelligence 2023; 5 (1): 163–183. doi: https://doi.org/10.1162/dint_a_00160
Putu Hadi Purnama Jati, Yi Lin, Sara Nodehi, Dwy Bagus Cahyono, Mirjam van Reisen; FAIR Versus Open Data: A Comparison of Objectives and Principles. Data Intelligence 2022; 4 (4): 867–881. doi: https://doi.org/10.1162/dint_a_00176
What is the difference between “FAIR data” and “Open data” if there is one?
https://www.go-fair.org/resources/faq/ask-question-difference-fair-data-open-data/
HEATH Information 