Last year OpenAIRE ran three workshops1 around services to support FAIR data, designed to explore, discuss and propose recommendations on how existing data infrastructures can evolve and collaborate to provide services that support the implementation of FAIR data Principles, in particular in the context of building the European Open Science Cloud (EOSC).  Based on these workshops a report was released  putting forward recommendations for data and infrastructure services providers to support Findable, Accessible, Interoperable, and Reusable (FAIR) research data within the broader context of the scholarly ecosystem. While the results are a work-in-progress, the challenges and priorities outlined in this report provide a detailed and unique overview of current issues seen as crucial by the community, with the potential to drive results through collaboration and incremental change. The three workshops were jointly organised by the projects FAIRsFAIR, RDA Europe, OpenAIRE, EOSC-hub and FREYAThe first two workshops’ aim was to examine common challenges for services in the scholarly and research data ecosystem to help make data FAIR. Discussions among the break-out groups on what should be expected from a data service in the FAIR ecosystem and resulted in a set of initial recommendations on how existing data infrastructures can help develop a FAIR framework.  The third workshop seeked further feedback from a broader audience aiming for a set of practical guidelines to make services support FAIR. Priorities were set based on the list of recommendations by different stakeholder groups. Four recommendations stand out as being assigned at least medium priority by all, and top priority by two different groups. 

These recommendations were the following:

  • PID services for a wide range of objects, such as publications, researchers, datasets and organisations. Emerging PID types (e.g. for instruments) should be monitored and used when they are mature.
  •  If applicable, metadata that complies with appropriate (domain) standards should be generated and captured automatically (for e.g by instruments).
  • Consider FAIR alignment and data sharing as part of research assessment, among other Criteria.
  • Foster global collaboration on FAIR implementation challenges and emerging solutions through organisations such as the Research Data Alliance.

Based on differences between stakeholder groups, the outcomes of the prioritization-exercise varied, signaling the many simultaneous challenges of including FAIR in research data services and infrastructure. 

The prioritizing exercise was followed by a discussion about which actions should be taken to implement the recommendations. The selected priorities and subsequent actions discussed by the breakout groups formed the basis for a discussion on which stakeholders could take on the responsibilities in the services ecosystem for FAIR data for the various actions. 

Figure 1: Approach to prioritising recommendations

Figure 1: Approach to prioritising recommendations

It has proven to be oftentimes challenging to associate more high-level recommendations with pointed, concrete actions and well-defined owners. This workshop series and report has endeavoured to make a start, while it is hoped that the results presented here will help direct the discussion and spur action, it will no doubt be part of a longer journey with further iterations on the formulation of these recommendations, priorities and actions.

 Read the full report here.

1. Workshop details:

workshop, FAIR

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