A Generic Workflow for the Data FAIRification Process

Annika Jacobsen; Rajaram Kaliyaperumal; Luiz Olavo Bonino da Silva Santos; Barend Mons; Erik Schultes; Marco Roos; Mark Thompson

doi:10.1162/dint_a_00028

A Generic Workflow for the Data FAIRification Process

Annika Jacobsen^*, Rajaram Kaliyaperumal, Luiz Olavo Bonino da Silva Santos, Barend Mons, Erik Schultes, Marco Roos, Mark Thompson

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

43 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	56-65
Journal	Data Intelligence
Volume	2
Issue number	1-2
DOIs	https://doi.org/10.1162/dint_a_00028
Publication status	Published - 31 Jan 2020
Externally published	Yes

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Jacobsen-2020-A-generic-workflow-for-the-data-faiFinal published version, 1.13 MBLicence: CC BY

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title = "A Generic Workflow for the Data FAIRification Process",

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.",

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AU - Roos, Marco

AU - Thompson, Mark

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A Generic Workflow for the Data FAIRification Process

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