The EOSC Symposium week (19-22 October) included lots of interesting presentations on various EOSC related activities and I’m fairly sure there was something in it for all of us. As a member of two EOSC projects on FAIR, FAIRsFAIR and EOSC-Nordic, I attended the sessions that were of particular interest to those. These were a session on FAIR practice by Rob Hooft (DTLS, ELIXIR-NL and representative of the Task Force “FAIR practices”) and Marta Teperek (TU Delft) and a session on EOSC skills and training by Natalia Manola and Vinciane Gaillard (OpenAIRE). One of the themes that emerged from these sessions were the subsequent measures of FAIR research required now that the technical bits and pieces of data management are falling into place.
Firstly, the evolving demands on open and FAIR research requires a new set of skills and new professions to emerge to fully support FAIR and open research. These demands concerns both support staff, but also scientists themselves. To meet this end, there needs to be an appropriate amount of discipline specific data stewards and data curators in place within a short reach to the scientists, with deep understanding of the FAIR principles, community standards and open science requirements. This would finally enable data stewards and data curators to get the recognition they deserve! The scientist on the other hand should have a general knowledge of the FAIR principles and the ways in which these are conveyed into practical measures. Another type of support structure increasingly emerging is in the form of data champions, who are data experts in their scientific fields and are able to share their real-life stories of best practices and are thus perfect ambassadors for FAIR and open science. An example of this is the European Group of FAIR Champions set up by the FAIRsFAIR project.
Secondly, there is also a need of a coherent incentive and recognition system facilitating the change towards a FAIRer and open research system. There were several good suggestions on how we could achieve this, e.g. through development of a FAIR/open science profile, by creating an academic credit system for open data/open research, by modernising the academic recognition and evaluation processes, by diversifying what is recognisable as an achievement, and through FAIR branding - certifications for FAIR-enabling repositories and badges for researchers.
Regarding developing and maintaining community standards, a topic addressed in the Six Recommendations for Implementation of FAIR Practice, the trend of recognising the development of a new standard is higher than recognising the adoption an existing one isn’t really helpful when it comes to *standards*, according to Rob Hooft. In the live poll performed, the audience considered this recommendation highest priority. Another recommendation is to map the FAIR principles to other digital objects, such as software and workflows. In its introduction the report positions itself as complementary to “Recommendations on practice to support FAIR data principles” (Molloy, L., Whyte, A., Davidson, J., Asmi, A., Grootveld, M., Herterich, P., Martin, I., Méndez, E., Nordling, J., Principe, P., van Horik, R., Vieira, A., (2020) D3.4 Recommendations on practice to support FAIR data principles, Zenodo: https://doi.org/10.5281/zenodo.3924132)
Does not a research environment like this sound pretty fantastic? Unfortunately it is easier said than done and requires a lot of time and effort from a range of stakeholders to put all this into practice. But little by little we will get there, of that I’m sure.
Blogpost written by Josefine Nordling (CSC) and Marjan Grootveld (DANS-KNAW)