Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》
Jacobsen, Annika
Azevedo, Ricardo De Miranda
Juty, Nick
Batista, Dominique
Coles, Simon
Cornet, Ronald
Courtot, Melanie
Crosas, Merce
Dumontier, Michel
Evelo, Chris T.
Goble, Carole
Guizzardi, Giancarlo
Hansen, Karsten Kryger
Hasnain, Ali
Hettne, Kristina
Heringa, Jaap
Hooft, Rob W. W.
Imming, Melanie
Jeffery, Keith G.
Kaliyaperumal, Rajaram
Abstract：
The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.
Subjects: Computer Science >> Computer Application Technology submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》
Abstract： “FAIR enough”?... A question asked on a daily basis in the rapidly evolving field of open science and the underpinning data stewardship profession. After the publication of the FAIR principles in 2016, they have sparked theoretical debates, but some communities have already begun to implement FAIR-guided data and services. No-one really argues against the idea that data, as well as the accompanying workflows and services should be findable, accessible under well-defined conditions, interoperable without data munging, and thus optimally reusable. Being FAIR is not a goal in itself; FAIR Data and Services are needed to enable data intensive research and innovation and (thus) have to be “AI-ready” (= future proof for machines to optimally assist us). However, the fact that science and innovation becomes increasingly “machine-assisted” and hence the central role of machines, is still overlooked in some cases when people claim to implement FAIR
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》
Sustkova, Hana Pergl
Hettne, Kristina Maria
Wittenburg, Peter
Jacobsen, Annika
Kuhn, Tobias
Pergl, Robert
Slifka, Jan
McQuilton, Peter
Magagna, Barbara
Sansone, Susanna-Assunta
Stocker, Markus
Imming, Melanie
Lannom, Larry
Musen, Mark
Schultes, Erik
Abstract： The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》
Jacobsen, Annika
Kaliyaperumal, Rajaram
Santos, Luiz Olavo Bonino da Silva
Mons, Barend
Schultes, Erik
Roos, Marco
Thompson, Mark
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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