An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

Authors : Romain David, Christian Ohmann, Jan‑Willem Boiten, Mónica Cano Abadía, Florence Bietrix, Steve Canham, Maria Luisa Chiusano, Walter Dastrù, Arnaud Laroquette, Dario Longo, Michaela Th. Mayrhofer, Maria Panagiotopoulou, Audrey S. Richard, Sergey Goryanin, Pablo Emilio Verde

For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed.

The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics).

109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system.

Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

URL : An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

DOI : https://doi.org/10.1038/s41598-022-25278-z

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

Authors : Romain David, Laurence Mabile, Alison Specht, Sarah Stryeck, Mogens Thomsen, Mohamed Yahia, Clement Jonquet, Laurent Dollé, Daniel Jacob, Daniele Bailo, Elena Bravo, Sophie Gachet, Hannah Gunderman, Jean-Eudes Hollebecq, Vassilios Ioannidis, Yvan Le Bras, Emilie Lerigoleur, Anne Cambon-Thomsen, The Research Data Alliance – SHAring Reward and Credit (SHARC) Interest Group

The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing.

This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation.

It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria – i.e., promoting FAIRness literacy – and not only the machine-readability of the criteria, are critical steps in the data sharing process.

Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding.

These are essential milestones in developing adapted support and credit back mechanisms not yet in place.

URL : FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

DOI : http://doi.org/10.5334/dsj-2020-032