A Generic Workflow for the Data FAIRification Process

• Published in: Data intelligence Vol. 2; no. 1-2; pp. 56 - 65
• Main Authors: Jacobsen, Annika, Kaliyaperumal, Rajaram, da Silva Santos, Luiz Olavo Bonino, Mons, Barend, Schultes, Erik, Roos, Marco, Thompson, Mark
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.01.2020; MIT Press Journals, The
• Subjects: FAIR data; FAIR data stewardship; FAIR dissemination; FAIRification workflow; Hands-on FAIRification; Metadata; Principles; Workflow
• ISSN: 2641-435X; 2641-435X
• DOI: 10.1162/dint_a_00028

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.