Guest post by Alastair Dunning¹, Susanna-Assunta Sansone², Marta Teperek¹

(authors listed alphabetically by surname)

¹ Delft University of Technology, the Netherlands; ² Oxford e-Research Centre, Department of Engineering Science, University of Oxford, UK

Science is living in the era of data – the reuse of other people’s data can drive new research questions and products, and inspire new scientific discoveries. This was the motivation behind the FAIR Principles (published in 2016), which provide researchers with a framework to improve the quality of their research: making their data more Findable, Accessible, Interoperable and Reusable. To turn these aspirational principles into reality, however, we need to provide researchers with FAIR-enabling tools and services that make frictionless the (complex) technical machinery of (meta)data standards and identifiers that underpins FAIR. Service providers, librarians, journal publishers and funders, among others, are actively working to deliver the next generation framework for FAIR data, which is collaborative, interdisciplinary and sustainable. FAIR has also become the (core) mission of a growing number of initiatives – especially in Europe, USA and Australia – encompassing R&D projects and programmes, institutional, national and global service provision, alliances and societies, training and educational efforts. In particular, numerous policy makers from around the world have articulated a vision of global open science and embraced FAIR as the driving principles. In Europe, for example, the vision is being realised through the ambitious European Open Science Cloud (EOSC) programme, across all disciplines.

So where are we now? Is the road to FAIRness understandably or unnecessarily congested? Do we have sufficient bottom-up (discipline-specific) initiatives rooted in real data scenarios, and do these interact with top-down (cross-discipline) efforts? Have these initiatives reached the researchers, whose work FAIR is supposed to enhance? Do we have the necessary technological and social infrastructure? Have we done enough to foster cultural and policy changes to motivate, reward and credit researchers for disseminating and publishing high-quality, machine-readable data? The answer is simple: it is a work in progress. The 2018 State of Open Data Report found that just 15% of researchers were “familiar with FAIR principles”.

What is clear, however, is that there is no lack of coordination initiatives: funded and unfunded, via existing authorities or grass-roots born. In recent years there seems to be a growing number of calls for greater coordination between various research projects and initiatives, all in order to avoid duplication of efforts and wasted resources. Funding schema also seem to favour the coordination approach, sending the message that the fundamental building blocks to enable FAIR data already exist, and that we only need to bring them together. Noble initiatives and who would disagree with the rationale? However, how much effort and resource is now put into coordination, and is it effort well spent? Are these efforts appropriately funded to make the necessary technical and social penetration and have a real impact in terms of depth and breath? In the following sections we provide our opinion on the status quo, which we call the layer cake of FAIR coordination.

Coordination as a challenge

Long-standing, global and cross-disciplinary events, such as those organized by the Research Data Alliance (RDA) and CODATA (which also co-organizes every two years International Data Week), and more recently the Open Science FAIR 2019 conference, are great venues to learn about the wealth of coordinating initiatives. If, like us, you work to make open science and FAIR data a reality, participating in these events is almost essential, because they serve as a huge shop window of initiatives. Often the take home message is the desperate need for harmonisation: a menu to better understand the variety of cakes in the making; the working groups, the position papers, the synchronisation projects, the coordination groups. Interestingly, these projects are often funded by the same funding body. Is there a need to coordinate the coordinators?

Isn’t coordination necessary to avoid duplication of efforts and resource waste? In principle, surely. However, most funded projects already have their specific work plans and deliverables agreed when awarded. Typically, once approved by funders, projects are expected to deliver in accordance with the approved timetable.

So even if ‘coordinators’ identify duplication of efforts between the ‘coordinated’ projects, is it realistic to expect that these funded projects change their agreed schedules or content and format of deliverables? Is it realistic to believe that suddenly the ‘coordinated’ projects (which sometimes are themselves already ‘coordinating’ various other initiatives) voluntarily drop their agreed plans and embark on lengthy discussions and negotiations with their project partners and funders, to justify amendments to their agreed deliverables and to agree on new timelines? What’s their incentive to do this? Coordination is social engineering, and takes time and effort. How realistically can differences in views and approaches be discussed and easily resolved in the life span of a project?

Plus, coordination costs money. That’s not only people’s salaries, but also the costs involved in the administration of these projects, communication efforts, travel to various meetings, and, most importantly, time needed to participate in multiple conflicting telecons, face to face meetings, reviewing and commenting on work packages, deliverables, participating in coordination workshops, etc. All valuable (and expensive!) time which could have been otherwise spent on actual working.

There also seems to be a growing demand to have as many partners as possible involved in big international projects. It is not uncommon, especially in European projects, to see consortia that have received several million Euro funding, which may sound like a lot, but it is not when the funds are split between a large number of partners. Generally, this large participation is necessary to bring different expertise together and to avoid reinventing existing resources. Nevertheless, how much effort (in terms of people, time, money and other resources) must be devoted to mere coordination between all these different partners? We trust that these projects are motivated by a genuine desire to help researchers do their research more effectively. However, there is a risk that between these layers of coordinators who coordinate the coordinators the underlying motivation of these projects might get lost.

Coordination as a necessity

Fundamentally, coordination is beneficial. There are multiple examples where important achievements have been reached through a reasonable two layers of coordination activities. The first layer encompasses efforts that are directly related to practitioners in a given discipline. Exemplars are the Integrated Research Programme on Wind Energy, ELIXIR in the life sciences and AGU in the earth and space science. These initiatives work with researchers and other stakeholders in their domains to make data FAIR. The second coordination layer offers these efforts the opportunity to engage with neighbouring disciplines as well as cross-disciplines and reach out to more stakeholders groups. But in reality, the two layers of coordination are just the ideal scenario, but not always a clear cut. The RDA, for example, is a successful global forum, which is also a network and a working platform where multiple players from various steps in the research life cycle engage to address research data problems. They subsequently deliver solutions that are first reviewed by relevant communities and then endorsed by RDA. Some initiatives from the first layer also participate in RDA to ensure their work can be re-used and aligned with other related activities, in order to avoid unnecessary duplications. For example, both ELIXIR and AGU have groups and representatives in RDA. However, for some, RDA is the first point of coordination where the community itself defines the problem and decides on specific solutions, e.g. for the Wheat Interoperability group. For others, RDA offers the opportunity to enlarge the stakeholder network, and this is the case of a resource like FAIRsharing (Sansone et al. 2019) that evolves and matures driven by community needs.

In all cases, problems and solutions are defined at research community levels, while coordination provides structure and support to catalyse these efforts.

Coordination is also a necessary evil. To be effective, a coordination effort has to be realistic and targeted at the right level. In addition, coordinators need to hold a position of authority in a given area, and be sufficiently resourced to be impactful. Therefore, funders should have a better oversight of what gets funded (refrain from funding several initiatives planning to do the same) and an overview of all funded proposals and their progress. For coordination-focused projects, less is more. Furthermore, the division between research and infrastructure funding programmes should be less drastic to better connect the need of the researchers with the FAIR-enabling tools and services.

A healthier cake with fewer layers

Better oversight on a funder level would have already helped a lot, but would not solve the problem when similar projects are funded by different funding organisations. This requires some bolder changes and introducing more transparency into the whole process of how funding is awarded. Perhaps project proposals should be published as soon as they are awarded and before the project gets going? In that way, anyone could read the proposals and interested communities could have an opportunity to reach out to each other and offer collaboration. Or perhaps, projects could be funded provisionally. After a provisional decision to grant funding is reached, projects would need to be made available for public consultation (public peer-review) on some sort of grant pre-print platform, and the final award only released after comments are adequately addressed.

Alternatively, funded projects could be encouraged to adopt a more agile approach. Detailed three year plans would no longer be necessary – three-year projects could outline high level goals they want to achieve, and then plan and execute in blocks of six or even three months. At the end of each block they would have time to react and respond to the demands for coordination.

Or to go completely in the other direction: maybe funders need to be more directive. Instead of providing a broad theme and inviting open responses, funders need to be clearer and more decisive about the direction they want a project to follow. Dictating not only the ingredients of a project, but also the recipe to be used. Handled badly, directive funding can risk killing innovation, but if managed with expertise, in particular for strategically-important topics, public funding can make transformative changes that are essential for today’s data-driven science.

Lastly, it is essential that coordination efforts do not become echo chambers. In this layer cake of FAIR coordination, the higher you go, the further away you are from the researchers. So something needs to change. Whatever option is taken, it’s clear that solving the current profusion of FAIR coordination projects simply by adding another layer of coordination might not be the best solution. While an extra layer may seem to offer a sweet way of bringing various ingredients together, the result can be a gooey mess.

This work is licensed under a Creative Commons Attribution 4.0 International License.