By Paula Martinez-Lavanchy
On the 19th of November I joined the meeting of the EUA-FAIRsFAIR focus “Teaching (FAIR) data management and stewardship” at the University of Amsterdam. In this post I summarized my key reflections of what happened during the meeting.
For those who are not yet familiar with FAIRsFAIR, it is an European project that started in March 2019 with the aim “to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. Emphasis is on fostering FAIR data culture and the uptake of good practices in making data FAIR.” The project has four main areas of work: ‘Data Practices’, ‘Data Policy’, ‘Certification’ (repositories) and ‘Training, Education and Support’. The meeting in Amsterdam was part of the activities of this last area, and specifically part of Work Package 7 of the project “FAIR Data Science and Professionalisation”. The main organizer of the event was the European University Association (EUA).
FAIRsFAIR project aims to be deeply connected with the European Open Science Cloud (EOSC) through a dedicated Synchronisation Force, which will offer coordination and interaction opportunities between various stakeholders, including the EOSC. It was not clear to me how exactly will the input of the project be used/adopted by EOSC in practice. However, the EOSCpilot work on skills was part of the presentations we saw, which suggest that the deliverables of FAIRsFAIR project are meant to become a building block of the EOSC, and not yet another layer of the cake of FAIR.
The various initiatives related to RDM training and FAIR data skills
The meeting started with five presentations that introduced the audience to different initiatives regarding or related to Research Data Management (RDM) training and/or FAIR data skills. Since we already talked about layers, I would divide the presentations in two: Framework initiatives and Implementation initiatives.
Framework initiatives: where the goal is to define the skills/competences that data scientists, data stewards and researchers should acquire around data management and to build up training curricula. There was a dedicated presentation about the EDISON project (Yuri Demchenko – University of Amsterdam) and FAIR4S (Angus Whyte – Digital Curation Centre – DCC). However, many other initiatives related to RDM skills and competences were mentioned: RDA Education & Training in Data Handling IG, Skills Framework for Information Age (SFIA), Competency Matrix for Data Management Skills (Sapp Nelson, M – Purdue), Open Science Careers Assessment Matrix, Towards FAIR Data Steward as a profession for the Lifesciences”. Kind of impressive and overwhelming to see the amount of groups working in the RDM training field.
Implementation initiatives: I call them implementation initiatives because these are initiatives already providing training or they are in the planning of creating an education program.
It was very interesting to hear about the work done by ELIXIR (Celia van Gelder – DTL/ELIXIR-NL), which is running training events for researchers, developers, infrastructure operators and trainers in the Life Sciences. ELIXIR also have a consolidated train-the-trainer- programme that provides training skills and have developed a really nice platform (TESS) where they announce training, make training materials available, but also provide guidance on how to build training.
We also had the opportunity to hear about the “National Coordination of Data Steward Education in Denmark” (Michael Svendsen – Danish Royal Library). They used a survey approach to investigate the landscape of expected skills that Data Stewards should have (results to be published soon). Based on this, the Danish Royal Library together with the University of Copenhagen, are planning to design a Data steward Education curriculum (launch 2021) and drafting a specific training module for the study program of librarians.
In summary, the terms ‘training’ and ‘education’ were used in the different presentations, but also many target groups and many types of skills with a different degree of relevance depending on the project or the initiative working on it. While this diversity was impressive, it felt somewhat difficult to understand the rationale for all these parallel projects and approaches, and how will they all lead to a coherent, agreed, pan-European framework for RDM skills and competences.
Advantages, disadvantages, challenges and opportunities
In the afternoon session we had break out discussions where 4 topics were proposed:
- Teaching RDM/FAIR at Bachelor/Master level
- Addressing RDM/FAIR at Doctoral/Early-career researcher level
- Generic Data Stewardship and FAIR data competences
- Disciplinary/Domain-specific Data Stewardship and FAIR data competences
We had two sessions of discussion, so each of us had the opportunity to join two different topics. For each topic we discussed advantages/disadvantages, good practices, missed opportunities, challenges, target audiences, possible synergies, etc. I joined topic 1 (Teaching RDM/FAIR at Bachelor/Master level) and 4 (Disciplinary/Domain-specific Data Stewardship and FAIR data competences). In both breakout groups we had rather broad discussions and exchange of knowledge, with more or less structure, but I found them very interesting and valuable. The organizers promised to report on the discussion results, so I will not duplicate their efforts. There will be a following post for sharing my own overall reflections about education and training on RDM. So to be continued.
What are the next steps for the FAIRsFAIR project with regards to skills and competences? The organizers intend to use the results of this meeting and the results collected in the “Consultation on EUA-FAIRsFAIR survey on research data and FAIR data principles”, a survey that they recently run, in order to define the activities of the project in the track of training and education. So hopefully more on this soon.