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： The last letter of the FAIR acronym stands for Reusability. Data and metadata should be made available with a clear and accessible usage license. But, what are the choices? How can researchers share data and allow reusability? Are all the licenses available for sharing content suitable for data? Data can be covered by different layers of copyright protection making the relationship between data and copyright particularly complex. Some research data can be considered as a work and therefore covered by full copyright while other data can be in the public domain due to their lack of originality. Moreover, a collection of data can be protected by special rights in Europe to acknowledge the investment in time and money in obtaining, presenting, arranging or verifying the data. The need of using a license when sharing data comes from the fact that, under current copyright laws, when rights exist, the absence of any legal notice must be understood as the default “all rights reserved” regime. Unless an exception applies, the authorisation of right holders is necessary for reuse. Right holders could use any text to state the reusability of data but it is advisable to use some of the existing licenses, and especially the ones that are suitable for data and databases. We hope that with this paper we can bring some clarity in relation to the rights involved when sharing research data
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》
Landi, Annalisa
Thompson, Mark
Giannuzzi, Viviana
Bonifazi, Fedele
Labastida, Ignasi
Santos, Luiz Olavo Bonino da Silva
Roos, Marco
Abstract：
In order to provide responsible access to health data by reconciling benefits of data sharing with privacy rights and ethical and regulatory requirements, Findable, Accessible, Interoperable and Reusable (FAIR) metadata should be developed. According to the H2020 Program Guidelines on FAIR Data, data should be "as open as possible and as closed as necessary", "open" in order to foster the reusability and to accelerate research, but at the same time they should be "closed" to safeguard the privacy of the subjects. Additional provisions on the protection of natural persons with regard to the processing of personal data have been endorsed by the European General Data Protection Regulation (GDPR), Reg (EU) 2016/679, that came into force in May 2018. This work aims to solve accessibility problems related to the protection of personal data in the digital era and to achieve a responsible access to and responsible use of health data. We strongly suggest associating each data set with FAIR metadata describing both the type of data collected and the accessibility conditions by considering data protection obligations and ethical and regulatory requirements. Finally, an existing FAIR infrastructure component has been used as an example to explain how FAIR metadata could facilitate data sharing while ensuring protection of individuals.
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