FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

Abstract

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.

Kakila database: Towards a FAIR community approved database of cetacean presence in the waters of the Guadeloupe Archipelago, based on citizen science

“…We also followed the "as open as possible, as closed as necessary" principle of the H2020 Programme Guidelines on FAIR Data ( Landi et al 2020 ), by deleting, from this shared version, the observer names to avoid the dissemination of personal data. As a consequence, the chosen strategy for the FAIRification process mainly used the recommendations of the Sharing Rewards and Credit (SHARC) IG (Interest Group of the Research Data Alliance), particularly the FAIR assessment decision-tree criteria and lessons learned for the gradual implementation of FAIR criteria ( David et al 2020 ).…”

“…By their very heterogeneous nature, citizen science data are challenging to analyse ( Van Strien et al 2013 ). That is why it makes sense to integrate them into a database complying with the FAIR principles ( Wilkinson et al 2016 ) using a step-by-step community approach ( David et al 2020 ) and a pragmatic method taking into account the constraints of the stakeholders ( Jacob et al 2020 ). All of this is with the aim of promoting their sharing and dissemination within the scientific community interested in marine mammals and marine spatial planning.…”

Making experimental data tables in the life sciences more FAIR: a pragmatic approach

“…The Fair Data Maturity Model (FDMM) document ( https://www.rd-alliance.org/groups/fair-data-maturity-model-wg ) describes a maturity model for the FAIR assessment with indicators, priorities, and assessment methods, which are useful for standardizing assessment approaches to allow comparison of their results. In contrast, the FAIR SHARC (SHAring Rewards and Credit) [ 9 ] document allows the fairness of projects and associated human processes to be assessed, either by external evaluators or by the researchers themselves. Therefore, these grids cannot be compared with each other, but rather complement each other.…”

“…Unfortunately, researchers who devote time and expertise to activities like data curation are not currently rewarded by traditional career progression metrics. We believe that this should change in the future, and crediting and rewarding mechanisms are the subject of the Research Data Alliance SHAring Rewards and Credit Interest Group [ 9 ].…”

Open Science als Beitrag zur Qualität in der Bildungsforschung

“…Eine tiefergehende Diskussion zur Weiter- und Wiederverwendung von Daten ist in den FAIR Richtlinien (David et al. 2020 ; Wilkinson et al 2016 ) zu finden.…”