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 >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》
Abstract：
Since their publication in 2016 we have seen a rapid adoption of the FAIR principles in many scientific disciplines where the inherent value of research data and, therefore, the importance of good data management and data stewardship, is recognized. This has led to many communities asking “What is FAIR?” and “How FAIR are we currently?”, questions which were addressed respectively by a publication revisiting the principles and the emergence of FAIR metrics. However, early adopters of the FAIR principles have already run into the next question: “How can we become (more) FAIR?” This question is more difficult to answer, as the principles do not prescribe any specific standard or implementation. Moreover, there does not yet exist a mature ecosystem of tools, platforms and standards to support human and machine agents to manage, produce, publish and consume FAIR data in a user-friendly and efficient (i.e., “easy”) way. In this paper we will show, however, that there are already many emerging examples of FAIR tools under development. This paper puts forward the position that we are likely already in a creolization phase where FAIR tools and technologies are merging and combining, before converging in a subsequent phase to solutions that make FAIR feasible in daily practice.
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.
Hits
Hits
Downloads
Comment