Published Versions 2 Vol 2 (1) : 285–292 2020
Considerations for the Conduction and Interpretation of FAIRness Evaluations
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Abstract & Keywords
Abstract: The FAIR principles were received with broad acceptance in several scientific communities. However, there is still some degree of uncertainty on how they should be implemented. Several self-report questionnaires have been proposed to assess the implementation of the FAIR principles. Moreover, the FAIRmetrics group released 14, general-purpose maturity for representing FAIRness. Initially, these metrics were conducted as open-answer questionnaires. Recently, these metrics have been implemented into software that can automatically harvest metadata from metadata providers and generate a principle-specific FAIRness evaluation. With so many different approaches for FAIRness evaluations, we believe that further clarification on their limitations and advantages, as well as on their interpretation and interplay should be considered.
Keywords: FAIR metrics; FAIR principles; FAIR data; FAIRness; Implementation; Maturity indicators; Scientific community
M. Dumontier was supported by grants from NWO (400.17.605;628.011.011), NIH (3OT3TR002027-01S1; 1OT3OD025467-01; 1OT3OD025464-01), H2020-EU EOSClife (824087), and ELIXIR, the research infrastructure for life-science data. R. de Miranda Azevedo was supported by grants from H2020-EU EOSClife (824087), and ELIXIR, the research infrastructure for life-science data.
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Article and author information
Cite As
R. de Miranda Azevedo & M. Dumontier. Considerations for the conduction and interpretation of FAIRness evaluations. Data Intelligence 2(2020), 285–292. doi: 10.1162/dint_a_00051
Ricardo de Miranda Azevedo Miranda Azevedo ( took the lead in writing themanuscript.
Ricardo de Miranda Azevedo is an independent and pragmatic researcher with a backgroundin psychology and clinical epidemiology who is happy to work in multidisciplinary settings. He isa great multilingual communicator who is enthusiastic about scientific research but also forcomputers and teaching different topics. He is often labeled as a creative mind with a problemsolver attitude.
Michel Dumontier
M. Dumontier ( supervised and reviewed themanuscript.
Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University andco-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. Hisresearch focuses on the development of computational methods for scalable and responsiblediscovery science. Previously at Stanford University, Dr. Dumontier now leads the interfacultyInstitute of Data Science at Maastricht University to develop socio-technological systems foraccelerating scientific discovery, improving human health and well-being, and empoweringcommunities with ethical data-driven decision making.
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