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

Abstract

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.

ISA API: An open platform for interoperable life science experimental metadata

“…There are many data models and frameworks for describing entities and artefacts of scientific research.The life sciences have pioneered the development and application of ontologies, data standards, and minimum standards for reporting research results. More than ever, the importance of making scientific data FAIR (Findable, Accessible, Interoperable and Reusable) is at the forefront of discourse in the research community [1]. The ISA Metadata Framework, or simply ISA – so named after its constituent key concepts: ‘Investigation’ (the project context), ‘Study’ (a unit of research) and ‘Assay’ (analytical measurement), was born out of initial efforts to create an ‘exchange network test bed’ for a diverse set of digital resources in ‘omics studies.…”

“…There are many data models and frameworks for describing entities and artefacts of scientific research.The life sciences have pioneered the development and application of ontologies, data standards, and minimum standards for reporting research results. More than ever, the importance of making scientific data FAIR (Findable, Accessible, Interoperable and Reusable) is at the forefront of discourse in the research community [1].…”

FAIR Principles: Interpretations and Implementation Considerations

“…Examples of metadata schemata can be found in FAIRsharing  [18] [19] and include for instance the Data Documentation Initiative (DDI)  , the HCLS Dataset Descriptors  , and many domain-specific "minimal information" models that have been invented.…”

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

“…The work on FAIR data standards, repositories and policies is already ongoing as very well illustrated by the FAIRsharing. org platform which gathered more than 2800 registered standards, databases and policies (McQuilton et al, 2019, by various RDA -WG (e.g., FAIR data maturity model) or by the international GO FAIR initiative (Schultes et al 2018). There is a need for an implementation of the FAIR principles in a progressive and community-oriented way, consolidated within existing practices to ensure that they evolve without interruption and in a way that is acceptable to the various actors.…”