Abstract
The FAIR guiding principles aim to enhance the Findability, Accessibility, Interoperability and Reusability of digital resources such as data, for both humans and machines. The process of making data FAIR (“FAIRification”) can be described in multiple steps. In this paper, we describe a generic step-by-step FAIRification workflow to be performed in a multidisciplinary team guided by FAIR data stewards. The FAIRification workflow should be applicable to any type of data and has been developed and used for “Bring Your Own Data” (BYOD) workshops, as well as for the FAIRification of e.g., rare diseases resources. The steps are: 1) identify the FAIRification objective, 2) analyze data, 3) analyze metadata, 4) define semantic model for data (4a) and metadata (4b), 5) make data (5a) and metadata (5b) linkable, 6) host FAIR data, and 7) assess FAIR data. For each step we describe how the data are processed, what expertise is required, which procedures and tools can be used, and which FAIR principles they relate to.
Highlights
The FAIR data principles aim to enable efficient and error-free analysis of data from multiple sources by machines and by humans, through enhancing their Findability, Accessibility, Interoperability and Reusability [1]
Since the initiation of the FAIR principles in 2014, FAIR metrics [2, 3, 4], FAIR infrastructure [5] and FAIR tools [6] have been developed to aid the process of making data FAIR (“FAIRification”)
We have described a generic workflow for the data FAIRification process
Summary
Annika Jacobsen1†, Rajaram Kaliyaperumal, Luiz Olavo Bonino da Silva Santos, Barend Mons, Erik Schultes, Marco Roos1 & Mark Thompson. Kaliyaperumal, L.O. Bonino da Silva Santos, B. A generic workflow for the data FAIRification process.
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