Archives des mots-clés : FAIR Principles

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

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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

Hors norme ? Une approche normative des données de la recherche

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Auteur : Joachim Schöpfel

Nous proposons une réflexion sur le rôle des normes et standards dans la gestion des données de la recherche, dans l’environnement de la politique de la science ouverte.

A partir d’une définition générale des données de la recherche, nous analysons la place et la fonction des normes et standards dans les différentes dimensions du concept des données. En particulier, nous nous intéressons à trois aspects faisant le lien entre le processus scientifique, l’environnement réglementaire et les données de la recherche : les protocoles éthiques, les systèmes d’information recherche et les plans de gestion des données.

A l’échelle internationale, nous décrivons l’effet normatif des principes FAIR qui, par la mobilisation d’autres normes et standards, créent une sorte de « cascade de standards » autour des plateformes et entrepôts, avec un impact direct sur les pratiques scientifiques.

URL : https://revue-cossi.info/numeros/n-5-2018-processus-normalisation-durabilite-information/730-5-2018-schopfel

Are the FAIR Data Principles Fair?

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Authors : Alastair Dunning, Madeleine de Smaele, Jasmin Böhmer

This practice paper describes an ongoing research project to test the effectiveness and relevance of the FAIR Data Principles. Simultaneously, it will analyse how easy it is for data archives to adhere to the principles. The research took place from November 2016 to January 2017, and will be underpinned with feedback from the repositories.

The FAIR Data Principles feature 15 facets corresponding to the four letters of FAIR – Findable, Accessible, Interoperable, Reusable. These principles have already gained traction within the research world.

The European Commission has recently expanded its demand for research to produce open data. The relevant guidelines1are explicitly written in the context of the FAIR Data Principles. Given an increasing number of researchers will have exposure to the guidelines, understanding their viability and suggesting where there may be room for modification and adjustment is of vital importance.

This practice paper is connected to a dataset (Dunning et al.,2017) containing the original overview of the sample group statistics and graphs, in an Excel spreadsheet. Over the course of two months, the web-interfaces, help-pages and metadata-records of over 40 data repositories have been examined, to score the individual data repository against the FAIR principles and facets.

The traffic-light rating system enables colour-coding according to compliance and vagueness. The statistical analysis provides overall, categorised, on the principles focussing, and on the facet focussing results.

The analysis includes the statistical and descriptive evaluation, followed by elaborations on Elements of the FAIR Data Principles, the subject specific or repository specific differences, and subsequently what repositories can do to improve their information architecture.

URL : Are the FAIR Data Principles Fair?

DOI: https://doi.org/10.2218/ijdc.v12i2.567

 

The FAIR Guiding Principles for scientific data management and stewardship

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There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles.

The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.

This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

URL : The FAIR Guiding Principles for scientific data management and stewardship

Alternative location : http://www.nature.com/articles/sdata201618