FAIR Convergence Matrix: Optimizing the Reuse of Existing                     FAIR-Related Resources

Sustkova, Hana Pergl; Hettne, Kristina; Wittenburg, Peter; Jacobsen, Annika; Kuhn, Tobias; Pergl, Robert; Slifka, Jan; McQuilton, Peter; Magagna, Barbara; Sansone, Susanna-Assunta; Stocker, Markus; Imming, Melanie; Lannom, Larry; Musen, Mark A.; Schultes, Erik

doi:10.1162/dint_a_00038

Cited by 14 publications

(11 citation statements)

References 11 publications

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“…Next, convergence needs to be technologically enabled, such as by a community governed platform e.g. the GO FAIR Convergence Matrix [15].…”

Section: Discussionmentioning

confidence: 99%

“…Guided by these implementation considerations, a stakeholder community may choose to reuse a solution from among existing implementations, or if none of these appear suitable, will have a clear roadmap describing the challenge in creating a de novo solution for the identified gap. A platform where stakeholder communities can declare their FAIR choices and challenges -the FAIR Convergence Matrix -is described in a separate paper [15].…”

Section: Introductionmentioning

confidence: 99%

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FAIR Principles: Interpretations and Implementation Considerations

et al. 2020

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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.

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“…Next, convergence needs to be technologically enabled, such as by a community governed platform e.g. the GO FAIR Convergence Matrix [15].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FAIR Principles: Interpretations and Implementation Considerations

et al. 2020

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“…across disciplines is the real challenge. Working with communities under GO-FAIR, as the StrRePo IN, FAIRsharing has become a key element to build the matrix of resources that enable FAIRness [10] curating and linking metadata on each repository, knowledge-base, standard and data policy. This metadata can be used to assess the FAIRness of a resource and also to make distributed data analytics possible by improving the AI readiness of the data [11].…”

Section: Helping the Consumers And Producers Of Standards Repositories And Policies To Enable Fair Datamentioning

confidence: 99%

Helping the Consumers and Producers of Standards, Repositories and Policies to Enable FAIR Data

McQuilton

Batista²,

Beyan

et al. 2020

Data Intellegence

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Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases, across all disciplines. These resources are necessary to meet government, funder and publisher expectations of greater transparency and access to and preservation of data related to research publications. This obligates researchers to ensure their data is FAIR, share their data using the appropriate standards, store their data in sustainable and community-adopted repositories, and to conform to funder and publisher data policies. FAIR data sharing also plays a key role in enabling researchers to evaluate, re-analyse and reproduce each other's work. We can map the landscape of relationships between community-adopted standards and repositories, and the journal publisher and funder data policies that recommend their use. In this paper, we show how the work of the GO-FAIR FAIR Standards, Repositories and Policies (StRePo) Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards, repositories and data policies in general. Pivotal to this effort, the FAIRsharing, an endorsed flagship resource of the Research Data Alliance that maps the landscape of relationships between community-adopted standards and repositories, and the journal publisher and funder data policies that recommend their use. Lastly, we highlight a number of activities around FAIR tools, services and educational efforts to raise awareness and encourage participation.

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“…At the end of 2018, The CDS began its active involvement in two international activities to develop leading practices for implementation solutions for FAIR data: the FAIR funders pilot programme (FFPP) (Wittenburg et al 2019) and the FAIR Implementation Matrix (Sustkova et al 2020). Both activities were initiated and led collectively by GO FAIR and RDA.…”

Section: Leading Practicesmentioning

confidence: 99%

From FAIR Leading Practices to FAIR Implementation and Back: An Inclusive Approach to FAIR at Leiden University Libraries

Hettne

Verhaar

Schultes³

et al. 2020

Data Science Journal

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Leiden University (LU) adopted a data management regulation in 2016. The regulation embraces the Findable, Accessible, Interoperable and Reusable (FAIR) principles. To implement the regulation a programme was established. The focus of the programme was initially to raise awareness and to set up services to make data Findable and Accessible and to train researchers on data management planning. In 2019, the programme entered its second phase, with an increased focus on Interoperable and Reusable data, and on implementing the machine-actionable aspects of FAIR data. This step is non-trivial, however, because of the fast-developing FAIR research data international research field that requires fast adoption of leading practices by support professionals with the adequate skills. In this paper we describe how LU aims to close the feedback loop between international bottom-up organisations such as GOFAIR, the Research Data Alliance and CODATA on the one hand and university staff engaged in developing and implementing emerging FAIR leading practices on the other. During processes such as these, it is of crucial importance to focus primarily on the needs of researchers. We describe how LU builds up its support for FAIR data before, during and after research through its involvement in leading practices, training and consultancy and end with recommendations for other universities wanting to implement the FAIR principles.

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FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

Cited by 14 publications

References 11 publications

FAIR Principles: Interpretations and Implementation Considerations

FAIR Principles: Interpretations and Implementation Considerations

Helping the Consumers and Producers of Standards, Repositories and Policies to Enable FAIR Data

From FAIR Leading Practices to FAIR Implementation and Back: An Inclusive Approach to FAIR at Leiden University Libraries

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