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RESEARCH PAPER

Agenda Setting on FAIR Guidelines in the European
Union and the Role of Expert Committees

Misha Stocker1,2†, Mia Stokmans3, Mirjam van Reisen3,4,5

1Research Advisors and Experts Europe, 1000

2VODAN-Africa

3Tilburg University, P.O. Box 90153 5000, the Netherlands

4Leiden University, 1310 Leiden, the Netherlands

5Leiden University Medical Centre (LUMC), Leiden University, 1310 Leiden, the Netherlands

Mots clés: FAIR Guidelines; EU expert committees; Policy entrepreneurs; Public agenda setting; European Open

Science Cloud; EOSC

Citation: Stocker, M., Stokmans, M., Van Reisen, M.: Agenda setting on FAIR Guidelines in the European Union and the role of

expert committees. Data Intelligence 4(4), 724–746 (2022). est ce que je: 10.1162/dint_a_00168

Submitted: Mars 10, 2021; Revised: Juin 10, 2022; Accepté: Juillet 15, 2022

ABSTRAIT

The FAIR Guidelines were conceptualised and coined as guidelines for Findable, Accessible, Interoperable
and Reusable (FAIR) data at a conference held at the Lorentz Centre in Leiden in 2014. A relatively short
period of time after this conference, the FAIR Guidelines made it onto the public policy agenda of the
Union européenne. Following the concept of Kingdon, policy entrepreneurs played a critical role in creating a
policy window for this idea to reach the agenda by linking it to the policy of establishing a European Open
Science Cloud (EOSC). Tracing the development from idea to policy, this study highlights the critical role that
expert committees play in the European Union. The permeability of the complex governance structure is
increased by these committees, which allow experts to link up with the institutions and use the committees
to launch new ideas. The High Level Expert Groups on the EOSC provided the platform from which the FAIR
Guidelines were launched, and this culminated in the adoption of the FAIR Guidelines as a requirement for
all European-funded science. As a result, the FAIR Guidelines have become an obligatory part of data
management in European-funded research in 2020 and are now followed by other funders worldwide.

†

Corresponding author: Misha Stocker, Research Advisors and Experts Europe (E-mail: misha.stocker@gmail.com; ORCID:
0000-0003-0347-9953).

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Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

ACRONYMS

EOSC
ERAC
ESFRI
EU
FAIR
HLEG
IN
NIH
RDA

European Open Science Cloud
European Research Area and Innovation Committee
European Strategy Forum for Research Infrastructures
Union européenne
Findable, Accessible, Interoperable, Reusable
High Level Expert Group
implementation network
National Institute of Health
Research Data Alliance

1. INTRODUCTION

The exponentially growing amount of (published) data that is instrumental in finding a (preliminary)
answer to a problem is the key research challenge of our time. Cependant, data is generally not prepared for
reuse. From the perspective of the subsidy-providers of research, this leads to a situation in which public
funding is offered to support research when data may in fact already exist. Dans 2018 a report commissioned
by the European Commission found that the cost of duplication could be as high as 10 billion euro a year [1].
As this data is mostly generated from public money, it should be available for the common good.

To deal with this issue, the European Open Science Cloud (EOSC) was put forward. This platform seeks
to foster the sharing of data and other research information. Cependant, it is only a means of making data
accessible and does not solve the problem of finding data. Ici, the FAIR Guidelines—that data should be
Findable, Accessible, Interoperable, and Reusable (FAIR)—come into play.

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The concept behind the FAIR Guidelines has reached the highest levels of the European Commission
and other European institutions, and has even been translated into a regulatory framework that became
operational in 2000 for all European Union (EU)-funded research. This speed of adoption and implementation
is unusual in the EU. EU policymaking is a complex multi-layered process in which multiple actors, chaque
with their own interests and priorities, try to influence the agenda. With so many actors, agenda setting
and decision making can become a long, drawn-out process. This research investigates how the FAIR
Guidelines reached the EU policy agenda in such a short time and in such a significant way.

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2. PROBLEM STATEMENT

Kingdon’s framework [2] sheds light on how a new issue, such as the FAIR Guidelines, can reach the
public policy agenda, and also how its original ideas are modified, based on the various actors involved
in promoting the idea. While this theory was developed in the context of the American federal system [3],
it can be used as a lens through which to approach EU agenda setting.

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The question that will be addressed in this article is: How did the FAIR Guidelines reach the EU policy
agenda and how did the idea behind FAIR change during the social process of agenda setting? This question
is specifically addressed in the context of the EU polity. As the administration of the EU is relatively small,
expert groups often play a larger role than in other administrations. The research, donc, pays special
attention to policy entrepreneurs (advocates of the FAIR Guidelines) within these expert groups.

The article is structured as follows: the following section (Section 3) describes the role of expert groups
in the EU context through the lens of Kingdon’s multiple stream framework. Section 4 presents the research
methodology, which is followed by the findings of qualitative research in Section 5. Section 6 discusses the
findings, followed by a brief conclusion in Section 7.

3. AGENDA SETTING IN THE EU CONTEXT: EXPERT GROUPS

There are lots of problems competing for government attention, and governments will usually only
include a new issue on the policy agenda if the issue is considered highly relevant. Sometimes a crisis
event can highlight an issue [4] ou, as in the case of FAIR, the problem (data management) was already part
of the policy agenda (Jean-Claude Juncker, former President of the European Commission, put the Digital
Single Market on the politics agenda in 2015 and made it a priority in his mandate [1]). Agenda setting is
a social process and, whoever is involved, tends to determine the narrative, the priorities set, et le
solutions proposed. Setting the agenda enables actors to push their own priorities and ensure that their
ideas are used. Policy making is a constant competition between different actors interested in influencing
the agenda and setting the priorities that come with it [5].

The most prominent framework explaining agenda setting is Kingdon’s multiple streams framework [2].
Kingdon argues that it often takes a series of events and coalescing circumstances to enable a decisive
grab of the agenda. He calls this the ‘opening of a policy window’. A policy window opens when the three
streams in his framework—problem stream, policy stream, and politics s tream—come together.

In the multiple stream framework, each stream is influenced by different actors. Policy entrepreneurs are
those actors who are willing to invest resources (temps, energy, reputation and sometimes money) in promoting
the issue [6]. Policy entrepreneurs are usually well connected to the policy making institutions [7]. Successful
policy entrepreneurs come with a solution at hand, actively looking for a ‘problem’ to which the proposed
solution can be linked [8]. Successful policy entrepreneurs are embedded in a field and capable of linking
across the streams precipitating a window of opportunity [7]. Policy entrepreneurs are often most successful
when operating within a policy change that is disruptive to “the established ways of doing things” [6, p. 650].

Numerous actors play a role in EU decision making, which is characterised by its unique policy making
structure. Compared to a federal system, it offers different points of entry for policy entrepreneurs [9].
Remembering that the EU has many different stakeholders [10], and within this complex social structure,
presence in decision making circles is important in order to be accurately represented. En outre, politique
proposals that are easy to implement and have a low cost will be more influential than others [11]. En tant que tel,
policies that require little action and little coordination tend to score highest. On the other hand, due to
the very rigid budget of the European Commission, costly programmes tend to be avoided [12].

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Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

Within European policy making, the group of actors who decide whether or not something is important
is limited to the individuals directly involved in the policy making [13]. The broader EU population do not
generally play a part. Plutôt, it is the lobbyists, business community, member state governments, and civil
society involved in specific fields that have the largest influence over the EU policy agenda. En fait, le
European Commission is the only European Union institution that has the right of initiative, donc, much of
the agenda setting is done by this institution. The Commission has become a hub for policy entrepreneurs
to have conversations and exchange information [11]. As it is not a large institution, it is open to external
technical advice, which creates opportunities for policy entrepreneurs to gain broad support for their ideas.

Regarding external technical advice, the European Commission uses a registry of experts to inform its
policies. These include lobbyists, representatives from national governments, and academic experts [12].
From this list, expert groups can be created that perform multiple roles in informing the different European
Commission policies or Directorate-Generals (DGs). These expert groups usually meet regularly, hence a
steady stream of expert information is provided along the entire process of European policy making. Le
convergence of ideas from those operations with the EU policy agenda is what Wessels coined ‘fusion’ [14]
—a mechanism that brings member states together for a common agenda. Au cours des dernières années, the use of expert
groups has increased, and some have become ingrained in European policy making as permanent additions
to the European Commission [15].

An expert group can be a small unit with a limited number of members, but on average it consists of 30
individuals representing different interests.(cid:99) Generally, cependant, the committees must represent different
stakeholders and parts of society. Par conséquent, expert groups can have members representing member state
governments, academia, and non-governmental organisations, as well as representatives of the business
community. The expert groups represent interest groups at the negotiation table and provide information
and knowledge in policy fields where the European Commission lacks expertise. This information can be
used to improve policy making by extending policy options. As Gornitzka and Sverdrup identify, le
European Commission will change the set-up and aim of these expert groups, depending on the task to be
accomplished [16].

The ambiguity that surrounds the use of expert groups, the informal nature and the lack of information
has been a concern for a long time [17, 18]. One of the regular demands of the European Parliament is
that more information is made public about the use of expert groups. While a register was compiled in
2004 with the names and affiliations of members sitting on expert groups, this only provides a small insight
into what is potentially said in these group meetings [19]. As Dur [20] points out, the different channels
through which interest groups access European policy making can make it hard to establish where influence
is generated. He suggested three methods through which influence could be measured: process tracing,
attributed influence, and preference attainment [20]. In this research a process tracing approach will
be used.

(cid:99) https://www.ombudsman.europa.eu/en/multimedia/infographics/en/29

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Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

4. RESEARCH APPROACH AND METHOD

This research can be characterised as a case-study [20, pp. 8–18]. It is framed as a theoretically informed
pattern matching [20] based on causal process tracing (CPT) [21]. This approach is well suited to answer
‘why’ and ‘how’ questions, because it focuses on the causal conditions, configurations and mechanisms
that make a specific outcome possible. The research makes use of an outcome (Oui)-centred approach, avec
the adoption of FAIR Guidelines by the EU as the focal point. With such a focus, the research is interested
in the many and complex causes of a specific outcome (in this case, the adoption of FAIR Guidelines) et
not so much in the effects of a specific cause (X). Autrement dit, using causal process tracing, the research
attempts to answer questions like ‘why did this (Oui) happen?' [21].

In this research, Kingdon’s framework [2] identifies the causes of the adoption of FAIR Guidelines by the
EU. It suggests that the problem, policy and politics stream develop independently from one another and
that a policy window opens if the three streams converge at a specific moment in time [21]. The opening
of the policy window can be regarded as the time that changes the course of a mechanism or process [22].
After such a point, it becomes near impossible to change course [22].

Based on this perspective, the following elements need to be investigated regarding the FAIR Guidelines

and the EOSC:

• The introduction of the FAIR Guidelines (as part of the EOSC) in the policy stream
• Timeline of the development of the concept of FAIR
• Timeline of the politics stream
•

Convergence of the three streams: the opening of a policy window and the adoption of the FAIR
Guidelines by the EU

In order to explore these elements, it is critical to trace the agenda-setting process and identify the
particular moments when change occurred. Such a task can be carried out through a theory testing process
tracing method [23]. In order to trace the process through the actors that played a role in it, three kinds of
empirical data were collected and analysed for this study: documents, interviews with key players, et le
observations of the first author during his internship with the GO FAIR Foundation.

4.1 Documents

This part of the research consisted of following the ‘paper-trail’ exhaustively to establish all relevant
parameters in time that have been set out on paper and can be objectively traced. From this work, a time-
line of events was established based on: (je) publicly available documents from GO FAIR and the European
Commission; et (ii) documents obtained through the interviews with actors involved in the process.
The documents were identified through a combination of a systematic document review, by exploring the
European Union databases using particular keywords and snowballing (retracing of documents through the
references contained in a start document). The selection of documents was based on: (je) the relationship
between the FAIR Guidelines and the EOSC; (ii) the moments in time identified; et (iii) the actors identified
in the process.

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Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

4.2 Observations

Ideally, a large and exhaustive list of relevant actors involved in the agenda-setting process should be
produced. Cependant, the different channels through which interest groups access European policy makes it
hard to establish where influence is generated [also see 19]. The internship of the first author at the GO
FAIR Foundation(cid:100) provided an opportunity to gather inside information. As one of his responsibilities during
the internship was to support different members in communication and meetings, a broad understanding
of the topic was gleaned. This included a strong understanding of the different actors and roles involved,
as well as the processes of implementing the FAIR Guidelines. Cependant, understanding of the other agents
involved in the process (not engaged with the GO FAIR foundation) was still limited. Due to this shortcoming,
some bias is involved.

4.3 Interviews

On the basis of the understanding of the different actors and their roles, five interviews were conducted
with individuals considered relevant to understanding key issues in the agenda-setting process. These
interviewees were/are involved in: (je) drafting the FAIR Guidelines; (ii) the European Commission; et (iii)
the implementation of the FAIR Guidelines. Three of the five interviewees participated in the 2014 Lorentz
Conference, which marked the beginning of the FAIR Guidelines: Barend Mons (BM), George Straw (GS),
Mark Wilkinson (MW). Barend Mons was heavily involved in the process of introducing FAIR in the
European Commission and EOSC. Two of the other interviewees were also very much involved in the
implementation of the FAIR Guidelines form a conceptual perspective and a semantic technical perspective,
namely, Erik Schultes (ES) and Luiz Bonino (LB), respectivement.

The interviews were carried out on the basis of topics prepared individually for each interviewee by one
of the researchers. The interviews were adapted to each interviewee in order to take advantage of their
expertise and role in the situation. To facilitate comparability, broad topics guided each interview, and this
helped the researcher to gain a more coherent understanding. The broad themes were: the origins of the
FAIR Guidelines, the timeline to move FAIR on to the policy agenda, and the implementation of the FAIR
Guidelines, specifically within the EOSC, but also on a broader basis to better understand the role that the
EOSC plays in the implementation of FAIR (see Appendix 1). The interviews were carried as a natural
conversation. Some interviews were conducted over several subsequent conversations, giving the researcher
time to reflect on the findings and present follow up questions to the interviewee.

5. RÉSULTATS

The ques tion this research seeks to answer is: How did the FAIR Guidelines reach the EU policy agenda
and how did the idea behind FAIR change during the social process of agenda setting? This section presents
the results of the research according to the four topics outlined in the research approach (timeline of the

(cid:100) https://www.gofairfoundation.org/

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Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

EOSC, timeline of FAIR, politics stream, and convergence of the three themes) in an attempt to answer this
question.

5.1 Timeline of EOSC

The development of the EOSC started in 2000, when the EU created the European Research Area [24],
an organisation to foster cooperation in scientific fields. In the European Council resolution of November
2007, it was noted that digital data had emerged as “key for modern science” [25, p. 3]. The European
Commission was considering centralised storage spaces where data and papers could be catalogued and
searched for. Entre 2011 et 2013 the need to develop better data management technologies evolved.
Dans 2011, the European Commission allocated EUR 100 million to research on “improving data-handling
technologies” [26]. Subsequent studies in 2011 et 2012 showed that only 25% of practitioners and
researchers shared their data [27]. At this time the rhetoric also started to shift towards the need for data to
be reusable. A prototype system, based on ‘green’ open access and ‘gold’ open access was established [26, 28],
both aimed at ensuring that all data were published. Cependant, the focus remained on data storage. Depuis
2012 onwards, the focus shifted to data interoperability [28, 13]. This eventually led to the broad consultation
on a European open science policy from 2014 onwards [29, 30]. Ainsi, the problem stream evolved
around the management of research data.

The Euro pean Open Data Strategy (2015) states that [1]:

Member States are wrestling with similar problems on a national basis which is too limited to allow
them to seize all the opportunities and deal with all the challenges of this transformational change.
For many issues the European level offers the right framework. That is why the European Commission
has set the creation of a Digital Single Market as one of its key priorities.

This was an important turning point in the problem stream, and paved the way for the European Open
Science Cloud, as part of the European Digital Single Market Strategy. A summary of the timeline of EOSC
is given in Figure 1.

The objective of the EOSC was equally focused on engaging the borderless opportunities offered by

digital research data and strengthening Europe’s position as a leader in global research:

[. . .] to give the [européen] Union a global lead in research data management and ensure that
European scientists reap the full benefits of data-driven science, by offering 1.7 million European
researchers and 70 million professionals in science and technology a virtual environment with free at
the point of use, open and seamless for storage, management, analyses and reuse of research data,
across borders and scientific disciplines. [31]

Due to the ambitious scale of the EOSC, experts involved focused on different aspects of the initiative,

which resulted in a fragmented image of the EOSC [31].

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Chiffre 1. Overview of the timeline of EOSC [31, p. 7].

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Depuis 2016 à 2017, four initiatives are of importance regarding the EOSC [31]. Dans 2016, a Commission
High Level Expert Group (established in September 2015) reported on a possible strategy for establishing
the EOSC [32]. This Expert Group advised the Commission to frame the EOSC as Europe’s contribution to
a future global Internet of FAIR Data and Services (IFDS) [32]. One of the founders of FAIR was appointed
as chairman of this Expert Group. In a second initiative, the Commission services examined the relationship
between the EOSC and long-standing European research infrastructures, such as the European Strategy
Forum for Research Infrastructures (ESFRI) [31]. One of the main conclusions was that connecting research
infrastructures should be included in EOSC. Thirdly, the consultation on the long-term sustainability of
ESFRI suggested that the reuse of data generated by research infrastructures is essential for long-term
sustainability [31]. Interoperability, common services, policies and open data obligations were also
mentioned as important requirements to improve data management and facilitate the reuse of research data.
Enfin, the Commission services collected specific independent advice and analysed relevant documents,
as well as the current governance practices of large-scale scientific networks, on an appropriate governance
structure for the EOSC [31]. These contributions formed the basis of the EOSC requirements, one of which
was focused on data FAIRness: “Initial services to gather and organize FAIR research data and data-related

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research products, to be available via a service platform with easy access and re-use” [31, p. 11]. Par
accepting these requirements, the core ideas of FAIR were included in the EOSC.

Alongside these initiatives, in the period 2016 à 2017, the development of the EOSC itself was also
started as part of Horizon 2020. The EOSC-Hub, Par exemple, focused on the federation of core infrastructure
and the EOSC-pilot looked at the initial rules of participation. Dans 2018, two important reports about the
EOSC were published by the Commission. The first report was entitled Prompting an EOSC in practice:
Report of the Commission 2nd High Level Expert Group on EOSC’ (EOSC 2nd HLEG) [33]. This report covers
a range of elements of the EOSC, such as defining the minimum viable research data ecosystem, establishing
the rules of participation, and describing governance and possible business models. It presents
recommendations for the implementation, engagement and steering of the EOSC. The second report,
Turning FAIR into reality: Report of the Commission Fair Expert Groups [34], describes what is needed to
implement FAIR and makes recommendations and sets out actions for stakeholders in Europe and beyond.
By accepting the second report, a roadmap for implementing FAIR in the EOSC and other data platforms
was realised.

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5.2 Timeline of FAIR

FAIR was originally conceived in 2014 during the Lorentz Conference ‘Jointly designing a FAIRport’ [35].
Many important actors took part in this workshop (see Appendix 2), including academics from prominent
universities such as Leiden, Stanford and Harvard, representatives of large corporate publishers (such as
Elsevier), journaux (such as Nature), as well as small IT innovation organisations.(cid:101) On the last day of the
conference, representatives of the Dutch government and the EU joined [35]. Over the next two years, le
FAIR Guidelines were refined, including through a process of public consultation. Dans 2016, the main ideas
were published in the scientific journal Nature [36]. A couple of revisions took place in 2017 [37], further
refining the original ideas, cependant, le 2016 article is still the main reference for FAIR Guidelines [38].

Fait intéressant, when asked to describe the origins of FAIR in more detail, all of the interviewees agreed
that the concept did not start in January 2014 at the Lorenz Conference, but at least a decade earlier. Le
interviewees all identified different moments as the origin of the idea. Some interviewees saw the genealogy
of the formation of the concept of the FAIR Guidelines in the early 2000s (interviewees ES, GS). During
that time, several articles were written proposing solutions to the data storage and management issue.
Another interviewee placed the provenance of the idea even earlier, at around 1995, soon after the
management of the Internet was privatised. The problem of the exponential increase in data had already
been anticipated by those implementing the Internet (such as interviewee GS). Since 2000, when the
Semantic Web was launched, many different proposals have been made (EG, GS, BM). These were, cependant,
all disconnected from each other and there was no capacity to scale them up (LB). It was only in 2014 que
the problem of massive data became so acute that the authors of the 2016 article on FAIR Guidelines were

(cid:101) https://www.datafairport.org/participants/

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able to receive funding and work with the Lorentz Centre to organise the workshop in January 2014 (EG,
BM), resulting in the launch of the FAIR Guidelines.

The interviewees also see the FAIR Guidelines as recycled ideas that have been proposed before, bien que
not in the same way. The FAIR Guidelines brought all of the existing ideas together, and then refined them
into an understandable framework that could be actionable. This process is captured in the creation of the
brand ‘FAIR’ (BM).

At the conference in 2014, it was decided that the development and implementation of the FAIR
Guidelines would be based on an hourglass model—a model designed in the 1950s that has been credited
with the emergence of the Internet [39]. The main idea of the hourglass model is to use many diverse
system architectures and make them all work together. After a while, the different system architectures
converge—the more operational and versatile ones move forward and start to combine with others. Dans ce
process of convergence, common areas in different networks are identified and start to align to allow for
interoperability and cross network use (GO FAIR(cid:102)). The aim is to get a critical mass of users to use a select
type of network, which will attract others to join [40]. In an hourglass model, the implementation is
spearheaded by implementation networks (INs) (par exemple., GO FAIR INs). These networks, which are all centred
around specific fields or tasks, exchange FAIRifying data and are self-contained systems working together.
By identifying areas in which INs share tools, platforms and resources, a convergence process has started
between INs, as a result of which data, system architecture and infrastructure can be shared between
réseaux.

The initial efforts to apply an hourglass model originated from the Lorentz Conference by defining the
idea of a ‘Data FAIRport’. A FAIRport consists of a small group of volunteer data stewards (initially: Mark
Wilkinson, Merce Crosas, Barend Mons and Paul Groth), who review and propose FAIR Guidelines. In line
with the hourglass model, a Data FAIRport is an open initiative that actively engages all ‘enablers’ in the
field of data publishing and reuse. A FAIRport stimulates the development of generic data stewardship (comme
a form of governance of data networks), which is implemented in ‘partner’ communities and formal
organisations (par exemple., FORCE 11, ELIXIR, BD2K, RDA, ODEX4all, ENPADASI, BBMRI-NL and FAIRdom). These
partners are free to implement any data management solution that fits with the FAIR Guidelines and share
it with other partners. Its aim is to provide fully FAIR mobile data that can be used for a range of applications,
both scientific and commercial, for entertainment or information.(cid:103)

At the end of the Lorentz Conference in 2014, three working groups were created (LB). The first would
focus on funding, ensuring that money was available to continue working on the project. The second would
work on the technical side of FAIR, focusing on the different challenges facing implementation. The last
group would focus on the promotion of the concept of FAIR to third parties.

(cid:102) https://www.go-fair.org/today/fair-digital-framework/
(cid:103) https://www.datafairport.org/

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As early as 2014, the FAIR Guidelines [41], followed by an extended guideline [42], were published as
discussion papers in FORCE 11, a movement of like-minded stakeholders who seek to advance digital
scholarly publishing. This discussion lasted almost two years and resulted in clear, concise, broadly-
supported principles published in the journal Scientific Data [36]. The aim of FAIR is commonly understood
as providing clear guidelines that help ensure all data and associated services in the emergent ‘Internet of
Data’ will be findable, accessible, interoperable and reusable. The recognition that computers must be
capable of accessing a data publication autonomously, unaided by their human operators, is at the core of
the FAIR Guidelines.

All of the interviewees mention that the Working Group on FAIR Promotion was extraordinarily successful.
The success was in the brand ‘FAIR’, which is easy to pronounce and remember. Investigation of the use
of FAIR in non-Western geographies shows that the acronym has also gained traction in these languages
without translation [43]. The principles put forward by FAIR, were widely accepted and considered necessary
(BM, LB, ES). This group was thus able to successfully ‘sell’ the concept to multiple partners, y compris
institutions and organisations in the United States and the European Commission. As well as large institutions,
many smaller organisations saw the value of FAIR and the benefits they might gain from it, leading to its
adoption (ES, BM, LB).

Ainsi, in a short period of time, many people (and organisations) became aware of the idea and adopted
the FAIR Guidelines. This can partly be explained by the open policy of the Data FAIRport, which stimulated
the co-creation of how to implement the FAIR Guidelines in a specific data context. Cependant, this large-
scale adoption did not always mean that the FAIR Guidelines were interpreted as intended by the founders
(ES, GS, BM, LB). The Data FAIRport was an avenue through which other organisations could create and
propose their own tools and methods of implementation (LB). Due to the limited scope of the FAIR
Guidelines, in a context where organisations were wanting to drive their own solutions, some organisations
were proposing forms of FAIR implementation that do not take into account all of the FAIR Guidelines.

The rise of these new groups, coupled with other larger platforms, created a race to come up with an
actionable proposition. One of the interviewees described it as “who codes first wins” (LB). À cet égard
the group behind FAIR needed to work fast to present its own proposal for FAIR-actionability. Otherwise,
it risked being supplanted by other organisations trying to push their own implementation schemes (GS).
This is especially relevant within the EOSC. With the European Union trying to balance the different
interests, it has been slow in decisively adopting one structure or another (GS).

5.3 Politics Stream

The report on the Lorentz Conference mentions that on the last day of the meeting, European Commission
officials were present to discuss the results of the conference [35]. It was decided that a demonstration of
a minimum viable product prototype would be presented at the Research Data Alliance (RDA) conference
in September 2014. The Directorate-General for Research and Technology Development at the European
Commission was present at the RDA conference and delivered a speech elaborating on the interest of the

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EU in taking action on the problem of data management. This speech took place one year before the launch
of the Digital Single Market initiative in 2015, which is seen as a starting point of the EOSC. This shows a
very early knowledge by the European Commission of the FAIR Guidelines.

Networks have played an important role in the promotion of FAIR (BM, ES). The group of people who
guided the development of the FAIR Guidelines come from a dozen different institutions: Leiden University
Medical Centre, Vrije Universiteit (VU) Amsterdam, Spanish and French universities, and universities in the
United States. These organisations also support FAIR. En outre, connections exist beyond individual
organisations, forming a large network of connections between FAIR, Dutch government-related research
funding institutions (such as ZonMw), business interests and European institutions (ES). Regular dialogue
happens between these actors, who communicate about developments in the field, funding and
implementation (ES). De plus, the FAIR Guidelines have been published in FORCE 11, which has boosted
the discussion about FAIR data [41, 42]. These connections have created a diverse platform pushing the
FAIR Guidelines and have been important in facilitating contacts with the European Commission.

The FAIR Guidelines were incrementally adopted by the European Commission in 2015 et 2016 [44].
In the first half of 2015, while policy options were still being investigated, documents from the European
Council noted FAIR as a recommendation for policy. The FAIR Guidelines appeared in the report of the
debate at the same meeting at which the EOSC was recommended in May 2015 [45]. While FAIR in its
entirety is recommended in those documents, in the final conclusions of that meeting only brief mention
is made of making data discoverable, accessible, reusable and interoperable [46]. At the beginning of 2016,
the European Research Area and Innovation Committee (ERAC), also recommended the adoption of FAIR [47].
The Dutch Presidency also subsequently promoted the FAIR Guidelines in the conclusions of a meeting in
Avril 2016 [44]. Simultaneously, the European Commission linked the success of the EOSC to the use and
implementation of FAIR, noting that all Horizon 2020 data would have to be made open by default [44].
The Dutch (2016), Bulgarian (2018), and Austrian (2018) governments [48] played a role in getting FAIR
and EOSC implemented during their presidencies of the European Commission. The Dutch government
had been an early proponent of FAIR and played a role in kick-starting the concept during its presidency
dans 2016. The subsequent presidencies of Bulgaria and Austria helped push towards the EOSC declaration
dans 2018.

5.4 Convergence of the Three Streams

If one looks at the timeline of the EOSC and FAIR, as well as the politics stream, it can be concluded
that the convergence of the three streams is an ongoing process that started in 2014, resulting in the opening
of a policy window with the instalment of the European Commission High Level Expert Group on the
European Open Science Cloud in September 2015, which was chaired by Barend Mons, one of the initiators
of FAIR. This expert group was to advise the Commission to frame the EOSC as Europe’s contribution to
the global Internet of FAIR Data and Services. The report indicates that:

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[The OESC] should enable trusted access to services, systems and the re-use of shared scientific data
across disciplinary, social and geographical borders. The term cloud is understood by the High-level
Expert Group (HLEG) as a metaphor to help convey both seamlessness and the idea of a commons
based on scientific data. This report approaches the EOSC as a federated environment for scientific
data sharing and re-use, based on existing and emerging elements in the Member States, avec
lightweight international guidance and governance and a large degree of freedom regarding practical
implementation. The EOSC is indeed a European infrastructure, but it should be globally interoperable
and accessible. It includes the required human expertise, ressources, normes, best practices as well
as the underpinning technical infrastructures. An important aspect of the EOSC is systematic and
professional data management and long-term stewardship of scientific data assets and services in
Europe and globally. Cependant, data stewardship is not a goal in itself and the final realm of the EOSC
is the frontier of science and innovation in Europe. [32, p.6]

The timelines of the three streams are summarised in Figure 2.

In the recommendations of the first HLEG, which started in 2015 and which were published its conclusions
dans 2018, the FAIR Guidelines, as well as the ideas put forward in Data FAIRport to develop and implement
FAIR Guidelines along the hourglass model in 2016, are clearly present.

The interviews revealed that Barend Mons played an important role in pushing the FAIR Guidelines at
the European level. His background with the European Commission gave him contacts with whom he could
correspond about the issue. Notably, among these contacts were the Directorate-General for Research and
Technology Development at the European Commission of Research and Innovation and the Head of the
Open Science unit. In his interview, Mons mentioned that the process started when the European Commission
approached him, which gave him the opportunity to explain what FAIR was early on in the process, before
it had been defined by others and opposition created. He was able to frame the problem and set the
narrative. The establishment of the Expert Group in September 2015 was a turning point. Mons, as the chair
for the First High Level Expert Group, pushed the idea of FAIR strongly, although he did face opposition
from certain groups.

A s the structure and governance of the EOSC was not yet determined in September 2015, the First High
Level Expert Group had the opportunity to lay the governance foundations of EOSC. This Expert Group
decided to be ambitious and implement the ‘hourglass’ model to get things started. They urged the immediate
start of training of the data stewards (facilitated by the Data FAIRPORTS), who would need to manage the
data networks, establishing an early implementation plan to quickly set up the basics (as introduced by the
publications on FORCE and discussed in the FORCE community) and work with existing infrastructure (tel
as e-infrastructure communities, and ESFRI communities)

H owever, working with existing infrastructure is best suited to a federal arrangement, in which information,
identities and other systems are stored across multiple platforms. Such a system prompts questions about
interoperability. This contradicted the initial governance model proposed for the EOSC, that it should be a
European Portal, where data can be stored and accessed in a uniform way [49]. In such a platform
interoperability is not such a big issue, as convergence is driven by compliance [49].

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Chiffre 2. Overview of the three streams.

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Another disagreement with the proposed governance model related to ideas about permission to
participate in the network. The First High Level Expert Group in 2015 suggested minimalistic governance
and a low bar to participate (in accordance with the ‘hourglass model’). Their vision was that everyone
should have access to the network and that it would be favourable to all. As FAIR does not mean that all
data is open and directly accessible to everyone, it should serve a function more akin to that of the Internet,
where everyone can use and access data. De plus, anyone is free to search for a role within the FAIR
ecosystem and try to establish systems conducive to their own needs, as long as they are developed
according to the proposed hourglass model. Cependant, the initial governance model proposed for the EOSC
[49] pushed for central governance in the start-up phase and conditional requirements in terms of processing
pouvoir, storage capacity and EOSC certification. And they also recommended a federal government model
for the EOSC once it matured [49].

Elements of the visions of both expert groups can be found in the current proposals for the structure of
the EOSC [25, 50, 51]. D'une part, the rules of participation according to the hourglass model can
be found throughout the implementation policy. The EOSC is meant to be community driven and have
minimal standards for accession [51]. Different actors will be able to provide a variety of different services,
such as repositories or FAIRification. De plus, the EOSC will keep its intervention low and the ruling
mechanisms will be light. On the other hand, the EOSC will provide some centralised features. An EOSC
portal would remain in place to function as a search engine [50]. The EOSC would at the same time provide
some FAIRification features such as analysis and data mining. These features, cependant, would not be limited
to the EOSC. Another aspect that will be retained is some distinction between actors. Minimal requirements
will be maintained, but there could be different treatment of actors depending on geography and role [50].

6. DISCUSSION

Kingdon’s multiple stream model was used to gain an understanding of the circumstances and factors
that explain the rapid inclusion of the FAIR Guidelines in the EOSC and beyond. Following Kingdon’s
framework, the main task of this study was to explore the causal conjunction of the three streams and the
opening of a policy window.

The inclusion of the FAIR Guidelines in EU policy is indisputable. The European Commission High Level
Expert Group on the European Open Science Cloud, which was established in September 2015 and reported
dans 2016 [32], set the stage for the acceptance and implementation of the FAIR Guidelines (as an hourglass
model) in the EOSC and beyond. From January 2020, all European-funded research must include FAIR data
management and many other research funders, tel que, par exemple, the Dutch ZonMw and United States
National Institute of Health (NIH), have followed suit.

The timeline of the development of the EOSC indicates that the issue of sharing data and data management
had been a high priority for the EU for a long time. Notwithstanding the urgency awarded to EOSC, it was
observed that the introduction of FAIR-Guidelines into this problem stream was rapid. After presenting the
idea at the Lorentz Conference in late January 2014, it took just over a year for FAIR to be discussed at

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high European levels (in February 2015). As the interviews indicate, the ideas and principles behind the
FAIR Guidelines are not new, but the FAIR proposition represents a synthesis of earlier ideas, lequel
combined with the energetic actions of the founders of the FAIR network propelled the FAIR Guidelines
forward as a solution to the problem of the exponential growth of data.

The agenda-setting process of the FAIR Guidelines in EOSC can be split into two periods. The first period
takes place between 2014 and the establishment of a High Level Expert Group (HLEG) in 2015–2016,
which delivers its report in 2016, mentioning the FAIR Guidelines [32]. The second period starts in 2016
with discussions in the HLEG, two reports that pave the way for the FAIR Guidelines to be strongly anchored
in the EOSC [33, 34] and it ends with the inclusion of the FAIR Guidelines in Horizon Europe. This period
is characterised by discussions on how FAIR should be adopted in the EOSC.

The advocates of FAIR promoted the idea and its preliminary implementation as a feasible solution to
data management problems. Initially, the FAIR Guidelines and its basic developments were published in
the FORCE papers [41, 42], and Data FAIRport, the idea of data stewards, and the hourglass model as a
guideline for the governance and implementation of FAIR were put forward. When the conference took
place in January 2014, FAIR was not a working prototype and not even an elaborated concept. Le
participants of the conference would continue to discuss the design specifications of FAIR for two years,
before an article, detailing the mechanisms, was published in 2016. Entre-temps, the EOSC initiative was
gaining political urgency in the European Commission, and was waiting for a solution regarding data
management. The idea of FAIR, even at this premature state, seems to fit the problem. This means that while
the relatively vague idea of FAIR was adopted in the preliminary EOSC proposals, any more detailed design
specifications for the Internet of FAIR Data, as envisaged by the FAIR conference participants, were not
adopted.

Depuis 2016 onwards, FAIR advocates with differing interests and policy proposals on FAIR managed to
influence the idea of the EOSC, resulting in a policy window opening for it on the public policy agenda.
The HLEG was set up to advise on the governmental structure of the EOSC, with no intention to facilitate
the FAIR Guidelines. This is clear in the first HLEG report, which recommends a central top-down governance
structure for EOSC, which runs counter to the federated hourglass model suggested for the design of FAIR
data systems [32]. Nevertheless, the work done in the first HLEG paid off in the second period. The second
HLEG report mentions the FAIR Guidelines and the federal governance system as important features of the
EOSC [33]. Hence during the second period, the nature of how FAIR would be integrated in the EOSC
remained subject to debate. The influence of the FAIR-advocates on the policy agenda mainly happened
through the HLEG and the impact is reflected in the second period.

FAIR advocates were successful policy entrepreneurs throughout the two periods. They brought the
different streams together, creating a policy window for the inclusion of FAIR on the European policy
agenda. While equivalent ideas existed prior to the Lorentz Conference of 2014, the coining of the guidelines
as FAIR followed the period after the conference. The FAIR Guidelines offered an attractive, easy to remember
acronym that worked well in the political stream. The initiative was closely observed by key people from

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the European Commission, who were present at the Lorentz Conference and who had a mandate to take
this forward. Donc, the policy stream converged with the problem and politics streams quite quickly. Linking
it to the EOSC, lifted the interest further into in the political stream. The FAIR Guidelines were proposed
as a solution to the emerging problem of the exponential growth of data. FAIR provided a solution to this
problem. The FAIR advocates made good use of their embeddedness in the European networks to push their
proposed solution forward. The early introduction of FAIR at high European levels was facilitated by the
social capital of the founders of FAIR, who were in close contact with the Directorate-General for Research
and Technology Development. Key agents, both in the politics stream and the policy stream, were aware
of each other’s work relating to data management since the beginning of the first period.

This study shows that policy entrepreneurs can also be powerful agents of change within expert groups
set up by the European Commission, despite the relative distance of these groups from the decision-making
body. The role that expert groups play in Commission decision making, and the early access it gives
members of those groups to be involved in the exploration of the problems, policies and politics concerning
a new issue, offers a space in which they can have a significant impact on new policies still being shaped.
One of the founders of FAIR was appointed as chairman of the HLEG on the EOSC, enabling him to push
the concept of FAIR further into EOSC.

7. CONCLUSION

This article considers the speed of the adoption of the FAIR Data Guidelines = in the creation of the
European Open Science Cloud. Using the concept of policy entrepreneurs developed by Kingdon, ce
research investigates their role in the European agenda setting, which resulted in the decision to adopt the
FAIR-Guidelines for scientific data-management. The meteoric speed of moving from an early idea to a
decision that disrupted earlier data management approaches is investigated in this study with an explorative
conception. The FAIR Guidelines reached the EU policy agenda in record time. During the first period of policy
formation (2014–2016), the role of policy entrepreneurs was critical to linking a solution (FAIR-Guidelines)
to a problem (a desire for a European Platform for Open Science). During the second period (2016–2019)
the negotiations started with the institutional set up of expert groups on how to concretise the FAIR concept
in policy. This limited study corroborates the relevance of policy entrepreneurs. Dans ce cas, their engagement
facilitated the convergence of the three streams. Ici, the problem, policy and politics streams did not
evolve independently, and the policy entrepreneurs acted in all three streams at all times. The policy
entrepreneurs succeeded in elevating the political relevance by strong networking. Their engagement with
the expert committees of the EU allowed them to engage directly with policymakers in the EU policy-
making bodies. A policy window emerged because key persons were constantly engaged in promoting the
FAIR Guidelines as a central component of the establishment of the EOSC. This experience shows that
expert groups are an interesting mechanism in the complex system of EU governance that allow players
outside EY institutions to bring new ideas to the public policy agenda.

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ACK NOWLEDGMENTS

We would like to thank Susan Sellars for copyediting and proofreading. We would also like to acknowledge
VODAN-Africa, the Philips Foundation, the Dutch Development Bank FMO, CORDAID, and the GO FAIR
Foundation for supporting this research.

AUTHORS’ CONTRIBUTIONS

Misha Stocker (0000-0003-0347-9953, Mishastocker@gmail.com): Conceptualisation. Méthodologie. Formal
analyse. Enquête. Writing—original draft preparation. Visualisation. Mia Stokmans (0000-0002-7593-
9632, M.J.W.Stokmans@tilburguniversity.edu): Conceptualisation. Méthodologie. Analyse formelle. Writing—
original draft preparation. Visualisation. Mirjam van Reisen (0000-0003-0627-8014, mirjamvanreisen@
gmail.com): Conceptualisation. Writing—review and editing. Surveillance.

CON FLICT OF INTEREST

All of the authors declare that they have no competing interests.

ETH ICS STATEMENT

The study was conducted as part of an internship at GO FAIR Foundation, which approved the study and
provided orientation on the interviews conducted. All participants provided voluntary informed consent
before each interview. The privacy of the participants was ensured by conducting the interviews in private
and not including identifiable information. Individual autonomy to participate in the study was guaranteed,
as participants were free to decline to participate. All who consented to participate were informed about
their freedom to withdraw from the study at any time. No participant withdrew from the study. Permission
to tape-record the data was similarly obtained. All the audio recorded material and transcripts were safely
stored by the lead author.

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APPENDIX 1. INTERVIEW TOPICS

The interviews were conducted through Zoom or Skype and followed the following broad themes:

1. On the origin of FAIR and the problem stream:

• What are origins of FAIR?
• When was FAIR first conceptualised?
• What is the debate that surrounded the initial idea of FAIR?
• What are the problems that FAIR is trying to solve?

2. On FAIR policy, implementation and actionability:

•

What are the options for the implementation of the FAIR Guidelines? (Note: Depending on the
expertise of the interviewee, different angles were approached. These could be general or specific
to the EOSC.)

3. On FAIR and the political processes to promote it:

•

How is FAIR related to the EOSC? (Note: The process here was focused on the European political
side of FAIR.)

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APPENDIX 2. PARTICIPANTS IN THE LORENTZ WORKSHOP (2014) [35]

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