The webinar is offered by FAIRsFAIR Work Package FAIR Practices: Semantics, Interoperability, and Services, and explores two facets of the FAIRification of services: the FAIR assessment framework for data services, and the features of FAIR repositories.
The FAIR principles are explicitly targeted at both metadata and data, with data here being regarded as any digital resource, asset or object (e.g., APIs, workflows, ontologies, models, and others). However, digital objects can not be made FAIR without supporting infrastructure services that are FAIR themselves.
One approach is a systematic evaluation of the FAIRness of services. In this regard, FAIRsFAIR is developing a FAIR assessment framework for data services alongside a similar framework for research software. The interim “Assessment report on ‘FAIRness of services" is the topic of the first part of the webinar. The webinar will briefly review FAIR assessment frameworks for data and other digital objects and motivate the need for a similar framework for services.
In the second part, the focus is on repositories, which not only give access (with needed restrictions) to research data and metadata, but are searchable and offer persistent identifiers which offer or enable search functionality. Deliverable 2.3 Set of FAIR data repositories features presents a list of the features of repositories which allows repositories not only to host FAIR digital objects but also support the FAIR principles even better.
After the presentations there will be time for Q&A and discussion; all feedback on the underlying reports will be welcome at the webinar and can also be offered online at the links provided above.
The FAIR principles (Wilkinson et al., 2016) provide direction and guidance for research data management and stewardship and are relevant to all stakeholders in the current digital ecosystem. They aim at improving findability, accessibility, interoperability and reusability of data, and define expected behaviours for metadata, data and supporting infrastructural elements. However, the principles are, by design, formulated as high-level guidelines that require further interpretation and definition by the community.
While the FAIR principles are explicitly targeted at research data and associated metadata, a truly FAIR ecosystem also includes other digital research objects (e.g. software, workflows, ontologies, digital models, and others). – as well as the services and supporting infrastructure that operates on them to enable efficient, equitable and frictionless sharing, analysis and re-use. It is therefore imperative to critically consider how such data services can ‘be’ or, perhaps better, ‘enable’ FAIR.