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How data services can support a FAIR data culture: insights from IDCC 2020

This year I was delighted to attend and present a poster at IDCC 2020, which put together a truly thought-provoking line-up of speakers and topics, as well as a number of wonderful opportunities to sample some of Dublin’s cultural attractions. Even more than the delights of the “fair city”, I was especially interested in one important theme of the conference which explored supporting a FAIR data culture. Inspired by the many valuable contributions, this post outlines some of the key insights presented on this topic.

An excellent hook around which to frame this review is, I think, offered by the figure below capturing results from the FAIRsFAIR open consultation on Policy and Practice, which was one focus of Joy Davidson’s illuminating overview of developments in this area. The top factor influencing researchers to make data FAIR, when we take both positive points on the scale together, is the level of support provided.

IDCC_Blog_Figure1_V2.png
Source: FAIRsFAIR report

So, let’s take a closer look at some of the key developments and opportunities for data services to enhance support for FAIR culture, bearing in mind of course that, when it comes to shaping service developments, local solutions must be informed by local contexts taking into account factors such as research strategy, available resources and service demand.

Enhancing the FAIR Support Infrastructure

That making data FAIR is an endeavour shared by researchers and data services was neatly illustrated by Sarah Jones. Her conclusion that equal, if not more, responsibility lies with data services gives cause to reflect on where and how we may need to raise our capabilities.

Let’s look here at three areas of opportunity for developing our support mechanisms around data stewardship, institutional repositories, and training.

Professionalising Data Stewardship

In 2016, Barend Mons predicted that 500,000 data stewards would need to be trained in Europe over the following decade to ensure effective research data management. Given this sort of estimate, it’s clear that our ability to build and scale data stewardship capability will be critical if we agree that data stewardship and data management skills are key enablers for research. Two particularly interesting developments in this area were presented.

Mijke Jetten outlined one project that examined the data steward function in terms of tasks and responsibilities, and the competencies required to deliver on these. The objective is a common job description, which then offers a foundation from which to develop a customised training and development pathway – informed of course by FAIR data principles, since alignment with FAIR is seen as a central tenet of good data stewardship. Although the project focused on life sciences in the Netherlands, its insights are highly transferable to other research domains.

Equally transferable is the pilot project highlighted by the Conference’s “best paper” from Virginia Tech, which described an innovative approach to addressing the challenge of effectively resourcing support across the data lifecycle in the context of ever-growing demand for services. Driven by the University Libraries, the DataBridge programme trains and mentors students in key data science skills to work across disciplines on real-world research data challenges. This approach not only provides valuable and scalable support for the research process, but serves also to develop data champions of the future, skilled and invested in FAIR data principles.

Leveraging Institutional Data Repositories

As a key part of the research data infrastructure, it’s clear that institutional data repositories (IRs) have an important role to play in promoting FAIR. Of course, researcher engagement and expertise are crucial to this end – as we rely on them to create good metadata and documentation that will facilitate discovery and re-use of their data.

In terms of fostering engagement, inspiring trust in an IR would seem to be an important fundamental, and formal certification is one way to build researchers’ confidence that their data will be well-cared for in the longer term by their repository. Ilona von Stein outlined one such certification framework, the CoreTrustSeal, which seems particularly useful since there’s a strong overlap between its requirements and FAIR principles. In terms of enhancing a repository’s reputation, one important post-Conference development worth noting is the recent publication of the TRUST Principles for digital repositories which offers a framework for guiding best practice and demonstrating IR trustworthiness.

Ilona also pointed to ongoing developments in terms of tools to support pre- and post-deposit assessment of data FAIRness. SATIFYD, for example, is an online questionnaire that helps researchers evaluate, at pre-deposit stage, how FAIR their dataset is and offers tips to make it more so. Developed by DANS, a prototype of this manual self-assessment tool is currently available with plans in the offing to enable customisation for local contexts and training. One to watch out for too is the development of a post-publication, automated evaluation tool to assess datasets for their level of FAIRness over time and create a scoring system to indicate how a given dataset performs against a set of endorsed FAIR metrics.

Another fundamental to think about is how skilled our researchers may or may not be when it comes to metadata creation as well as their level of tolerance for this task. Joao Castro made the point that researchers typically regard spending more than 20 minutes on this activity as time-consuming.

This observation came out of a project at the University of Porto to engage researchers in the RDM process and underlines the need to think creatively about how we, as data professionals, can enhance the support we offer. Joao described how the provision of a consultancy-type service had been explored to support researchers in using domain-specific metadata to describe their data. Underpinned by DENDRO, an open-source collaborative RDM platform, this service was well received by researchers across a range of disciplines and served to develop their knowledge / skills in metadata production, as well as raising FAIR awareness more broadly.

Maximising Training Impact

Of course, beyond raising awareness it’s clear that the upskilling of researchers through curriculum development and training is an essential step on the road to FAIR – a key question, however, is how do we make the most of our training efforts?

Daniel Bangert helpfully summarised findings from a landscape analysis of FAIR in higher education institutions and recommended focusing FAIR training initiatives on early career researchers (ECRs). This would seem to be a particularly powerful approach for affecting ‘ground up’ culture change, since ECRs are typically involved in operational aspects of research and will become the influential researchers of tomorrow.

This same report suggests that training and communication regarding FAIR should be couched within the wider framework of research integrity and open research. Framing data management training initiatives in this way provides important context and pre-empts the risk that it will be seen purely as a compliance issue.

As an interesting aside, an extensive research integrity landscape study, commissioned by UK Research and Innovation and published post-Conference, identified ‘open data management’ as the overall most popular topic for future training – a useful channel perhaps then through which to deliver and extend reach in the UK context at least.

Both Daniel and Elizabeth Newbold highlighted the need to draw on and share best practices and existing materials, where available. Subsequent workshop discussions strongly agreed with this sentiment but noted the challenges in finding and/or repurposing existing FAIR training, guidance and resources e.g. for a specific audience or level of knowledge. Indeed, it would seem sensible that FAIR principles should be applied to FAIR training materials!

In this regard, a helpful starting point might perhaps be this recent PLOS article – Ten simple rules for making training materials FAIR. Going forward, the development of a FAIR Competence Centre, with a key focus on supporting training delivery, will be one to look out for.

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Poster presentation at IDCC 2020. (Photo: Rosie Higman)

In Conclusion

So, hopefully plenty of food for thought and ideas for practical next steps here to adapt for your local context, wherever you are on the road to FAIR. While the challenges to creating a FAIR data culture are many, broad and complex, we can take heart not only from the many examples of sterling work underway, but also from the highly collaborative spirit across the data services community. In the context of increasing demands on tight resources, this will serve us well as we drive the FAIR agenda.

By Chris Gibson

Research Services Librarian (Data)
The University of Manchester Library

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