Together with EOSC-hub, RDA Europe and FAIRsFAIR, OpenAIRE recently organised two workshops titled “Services to support FAIR data; tyring to explore how existing infrastructures can work together and understand how to deliver services that support the creation of FAIR research outputs. These are part of 3-workshop series: the first two already took place in April, and a proposal for the third one has been submitted to the call for workshops of the Open Science Fair Conference in Porto in September 2019.

Goal: "Services to support FAIR data: from theory to implementation" took place on April 12th, 2019 during the EOSC-hub week. The half-day event, addressing service providers and research infrastructures and e-infrastructures representatives, examined the scholarly and research ecosystem, what exists, what could be modified, and will give insights into optimum service provisioning and represented a good chance to engage experts and audiences on how to turn FAIR data into reality.
You can see the full programme here

The  introduction set the context of FAIR in the EOSC and immediately two key current developments were presented: the goals and mode of operation of the EOSC WG on FAIR by Sarah Jones (DCC) and the RDA WG FAIR Maturity model goal and interim results from Kostas Repanas (DG RTD).

Three (3) implementation stories following services within the Research Data Lifecycle illustrated what aspects of FAIR and how services supported them, focussing mainly on interoperability challenges as key to successfully implement FAIR data and FAIR services:

  • CoreTrustSeal, how repsository certification can add to FAIRness of data (Mari Kleemola from Finnish Data Archive and CESSDA)
  • EOSC-hub services, PIDs in action for interoperable services (Baptise Grenier & Enol Fernandez from EGI)
  • PIDs for FAIR data, A PID Graph for allowing discovery of research (Maaike de Jong, FREYA)

and explored the issues of preserving data and make long-term preservation and explicit need, along with the necessity to validate the approach of structuring services with real research communities, to conclude with addressing the gaps in PIDs availability.

The unconference second part of the workshop allowed for interaction and discussion (four groups) around key topics related to services: perceived costs and time investment in making data FAIR, the main challenge(s) to address, the cultural changes required, competing standards, required organisational structures or policies, and needed skills or training to effectively produce, curate, preserve and disseminate research data. The participants were also asked to comment on the implementation priorities, the areas of intervention, any expectations from the EOSC Governance and data infrastructures, and to conclude, the identification of support mechanism to implement services for FAIR data.

The discussion in the different groups explored existing issues around FAIR, such as the too ambitious definition of FAIR and the missing statement on why researchers should be FAIR or adhere to the FAIR principles.

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All groups discussed the different approaches to FAIR among disciplines, where in some cases researchers don’t feel the need or the push to share research data outside their community as none in there has ever been tasked with looking at the “outside” the community of practice; in other cases, the uptake of the FAIR principles is easier because it’s in the nature of the community itself. In general terms, the need for specific services, support, skills development and training varies tremendously from one discipline to another, and change remains domain-specific. Nonetheless, increasing the amount of cross- and inter-disciplinary research is a strong driver for greater “harmonisation” of FAIR-like approachers between disciplines. When discussing communities’ approaches, a certain resistance to change emerges, that has to be addressed with an approach demonstrating the results and the benefits.

Key conclusions from the discussion were:
Some work is still needed around the availability of better tools and the communication of their existence, starting from services that can support the data management lifecycle from its beginning; again, attention needs to paid to the way data is made available: sometimes, in fact, they have technical barriers that hinder access instead of facilitating it.
The infrastructure devoted to hosting the research output has to exist before the specific mandate (e.g. to deposit data in a certain repository) comes in place.
Data policies and mandates support the change of mind and behaviour of researchers, but the compliance aspect is still challenging.

while key points for next steps include the following:

Implementation priorities for services
  • Follow a domain-specific approach taking into consideration targeted requirements
  • Ensure long-term usability and findability of datasets
  • Sustainable funding model
  • Take into account additional costs for FAIR
  • Adopt standards to make datasets findable, reusable and interoperable
  • Have machine-readable licenses for datasets
Areas of intervention
  • Data Management Plans should be required early when applying for funding and must have organisational relevance
  • Data generated should have an automatic generation of metadata that already complies with standards
  • There should be a data selection policy that rules what has long term value, pre-deposit, and has effect immediately after generation
  • Legal aspects must be taken into account from the start in the DMP
Expectations from the EOSC Governance and data infrastructures
  • Clarification of the cost models behind the use of the (EOSC) services as well as the granularity around how services can and will be procured
  • Definition of the direction of the EOSC in 5-10 years time
  • Infrastructures as custodians of standards
Identification of support mechanisms to implement services for FAIR data
  • Creation of practical guidelines on how to enable FAIR in repositories
  • Presence of skilled legal advisers in institutions would help in preparing robust DMPs
  • Establishment of an institutional data stewardship programme providing simple and intuitive training for researchers, and enable data stewards to work with researchers who support applications of FAIR
  • Set up of a cross-institution and cross-countries community of practice in FAIR
  • Support is needed when determining the cost of data management as this is typically underestimated or unknown
  • Researchers’ assessment should consider compliance with the FAIR principles among the other criteria.

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