Toward a FAIR Reproducible Research

Bontemps, Christophe; Orozco, Valérie

doi:10.1007/978-3-030-73249-3_30

Cited by 2 publications

(2 citation statements)

References 64 publications

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“…Recent initiatives include developing tools to assist with achieving FAIRness of research data and adapting the FAIR Principles to other digital research objects such as software, workflows, training material, and tools that assess the degree of FAIRness achieved. There are also discussions around expanding the FAIR Principles, or their interpretation, to include features of digital objects beyond reusability, including popularity 2 , data quality 3 , or reproducibility 4 . Funders may require researchers to make underlying research data "as open as possible and as closed as necessary" and follow the FAIR Principles.…”

Section: Introductionmentioning

confidence: 99%

Community-driven governance of FAIRness assessment: an open issue, an open discussion

Wilkinson,

Sansone,

Méndez

et al. 2023

Open Res Europe

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Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. Various FAIR assessment metrics and tools have been designed to measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation in an open Google Group fair-assessment-governance@googlegroups.com

show abstract

Section: Introductionmentioning

confidence: 99%

Community-driven governance of FAIRness assessment: an open issue, an open discussion

Wilkinson,

Sansone,

Méndez

et al. 2023

Open Res Europe

View full text Add to dashboard Cite

show abstract

“…Recent initiatives include increasing the development of tools to assist with achieving FAIRness of research data and adapting the FAIR Principles to other digital research objects such as software, workflows, training material, and tools that assess the degree of FAIRness achieved. There are also discussions around expanding the FAIR Principles, or their interpretation, to include features of digital objects beyond reusability, including popularity [2], data quality [3], or reproducibility [4]. Different research funders in other countries require researchers to make underlying research data "as open as possible and as closed as necessary" and follow the FAIR Principles.…”

Section: Introductionmentioning

confidence: 99%

Community-driven governance of FAIRness assessment: an open issue, an open discussion

Wilkinson¹,

Sansone²,

Méndez³

et al. 2022

Open Res Europe

View full text Add to dashboard Cite

Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. A range of FAIR assessment metrics and tools have been designed that measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation in an open Google Group fair-assessment-governance@googlegroups.com

show abstract

Toward a FAIR Reproducible Research

Cited by 2 publications

References 64 publications

Community-driven governance of FAIRness assessment: an open issue, an open discussion

Community-driven governance of FAIRness assessment: an open issue, an open discussion

Community-driven governance of FAIRness assessment: an open issue, an open discussion

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