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Recommendations for Services in a FAIR data ecosystem
21 July 2020
Results from a collaboration between RDA Europe, FAIRsFAIR, OpenAIRE, FREYA and EOSC-hub have just been published in Patterns in an article entitled Recommendations for Services in a FAIR Data Ecosystem.
The publication is an outcome of three workshops held in 2019 that gathered and prioritised community feedback on the current challenges and priorities for services to support FAIR data. The recommendations propose how data infrastructures and related stakeholders can align and collaborate to provide services that support the implementation of the FAIR data principles, in particular in the context of building the European Open Science Cloud (EOSC).
This article puts forward recommendations for data and infrastructure service providers to support findable, accessible, interoperable, and reusable (FAIR) research data within the scholarly ecosystem. Formulating such recommendations is important to coordinate progress in realizing a FAIR data ecosystem in which research data can be easily shared and optimally reused, with the aim of driving down inefficiencies in the current academic system and enabling new forms of data-driven discovery. Key recommendations—ranked by their perceived urgency—resulting from an extensive community consultation process include that (1) funders and institutions should consider FAIR alignment and data sharing as part of research assessment, among other criteria; (2) services should support domain-specific ontologies by identifying disciplines that lack ontologies and enriching existing registries of ontologies; (3) repositories should support FAIR data by developing tools, such as APIs, sharing best practices, and undergoing FAIR-aligned certification; and (4) institutions should support FAIR awareness and implementation by establishing data stewardship programs providing simple and intuitive training for researchers. The recommendations outlined in this article are meant to help guide the way forward to putting into practice the FAIR guiding principles for data management.