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If you follow the European policy space, you must have noticed that data is a top-level priority for Europe. Just last month the European Commission released its Data Strategy and a white paper on artificial intelligence. At EUA, we have worked on data for several years, often through the lens of Open Science – as it builds largely on the use of digital technologies and infrastructures. For instance, EUA contributed to the EU’s Open Data Directive and analysed institutional policies for research data management (RDM). Here, the idea of Findable, Accessible, Interoperable and Re-usable (FAIR) research data is gaining ground. This means treating and curating research data in a way that makes them, at the core, more easily reusable for other researchers and machines.
EUA joined the project FAIRsFAIR, which develops recommendations and standards and seeks to build capacity across research organisations to follow these principles. Through a survey and two focus groups we gathered as many insights as possible on skills and competencies to work in a data-driven environment. Building on EUA’s fruitful Open Access surveys, we also inquired about institutional RDM policies and support services. The results are now public and shed some light on the status quo.
One clear take-away message is that universities take digital competencies seriously. A clear majority of respondents indicated that they want their graduates to be versed in data analytics and data management, from bachelor students to doctoral candidates. Moreover, awareness of legal issues like data protection is considered highly important. Perhaps unsurprisingly, computation-heavy domains are seen as ahead of the pack. Through FAIRsFAIR, EUA will take such useful cases and support the sharing of good practices and resources that aid the uptake of these elements elsewhere.
When it comes to how universities do research data management, the picture is still more patchy. While most universities that responded to our survey said they have a policy, their contents are not all equal. The FAIR principles for instance are not yet commonplace, even though many funders increasingly require data to “be FAIR”, most prominently through Horizon Europe, the Open Data Directive and many national programmes. But we also found that more than half of the responding universities mandate data management planning and an additional 40 percent recommends it. Funders move to do the same, so alignment in this respect will be crucial.
In terms of supporting researchers, training is a key component that is happening at 80 percent of responding institutions. Support to publish data in repositories and looking at legal and ethics requirements is also done at 60 percent. This is important to ensure that researchers have the kind of support to comply with the requirements of funders, even if an institution does not have a codified research data policy yet.
However, support is mostly done by a central unit. Universities will need to ask themselves if they want to install so-called “embedded” data stewards within individual departments and faculties, which is a model that brings assistance closer to the researchers. Data competence centres are another complementary model to provide support that is gaining ground. But for most institutions any of those would entail a high upfront investment – here it is up to funders to jump in and provide support, at least until the possible efficiency gains from FAIR data stewardship can be realised.
Finally, a few questions asked universities about their views on the European Open Science Cloud (EOSC). Again, awareness is seen as very low, which means that the communications of this high-level initiative needs to be improved. Respondents also noted that they need better examples of the benefits of EOSC and that they fear a lack of capacity to link their own resources and services with the EOSC infrastructure. However, if realised, EOSC is seen as an opportunity that indeed facilitates research collaboration and may increase an institution’s visibility – a clear value proposition that needs to be picked up by EOSC.
All the above, teaching digital competencies, building infrastructure and support for RDM, and coming to grips with the EOSC are elements for institutional approaches to the digital transformation of science and higher education. What we do with data will be an issue for the years to come, so we had better get ready for it.