FAIR Principles: Interpretations and Implementation Considerations

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.

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data

“…Here, we want to specifically highlight that even if it looks like a technical problem that can be addressed by the FAIR principles with further work [ 154 ], we are convinced that it cannot be solved by technology alone. Therefore, significant sections of the paper have focussed on the human factor, i.e., the need to build understanding and commitment of all players involved in the data lifecycle to improve the status quo, and the need to reach community-wide consensus on levels of metadata completeness making the data fit for reuse in broader settings.…”

Statisticians roll up your sleeves! There’s a crisis to be solved.

“…Open data is useless when it is only available from a hidden website or in a proprietary format, therefore the applied statisticians of tomorrow make data, not only open, but also FAIR (findable, accessible, interoperable and reusable, see https://www.go-fair.org/fair-principles ). Even in cases where raw data cannot be made openly available, e.g., due to privacy concerns, it is still useful to FAIRify the data as much as possible (Jacobsen et al 2020). For example, by providing the metadata on a data platform (potentially with the option to apply for data access).…”

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

“…In this respect, many recent collaborative works have started to propose ways to implement, adapt and evaluate FAIR principles in several communities (e.g., Herschel et al 2017, Doorn & Timmermann, 2018, Federer et al 2018, Mons et al, 2017, Stall et al, 2018, de Miranda Azevedo & Dumontier, 2019, Erdmann et al, 2019. However, we are also reaching a moment where the FAIR principles now need cross-community convergence and consensus (EC DGRI, 2016;EC DGRI, 2018;Jacobsen et al, 2019;Sustkova et al, 2019;Thompson et al, 2019;Wilkinson et al, 2019). The work on FAIR data standards, repositories and policies is already ongoing as very well illustrated by the FAIRsharing.…”