Related to other papers in this - IA de Investigación especializada en el MIT

Relacionado con otros artículos en este
special issue

27 (p264); 25 (p246); 6 (p56); 16 (p158); 3 (p30); 15 (p151); 9 (p87); 4 (p40);
7 (p66); 21 (p208); 18 (p181)

Addressing FAIR principles

F1, F2, F3, F4, A1, A1.1, A1.2, A2, I1, I2, I3, R1, R1.1, R1.2, R1.3

FAIR Principles: Interpretations and Implementation
Considerations

Annika Jacobsen1, Ricardo de Miranda Azevedo2, Nick Juty3, Dominique Batista4, Simon Coles5,
Ronald Cornet6, Mélanie Courtot7, Mercè Crosas8, Michel Dumontier2, Chris T. Evelo9, Carole Goble3,
Giancarlo Guizzardi10, Karsten Kryger Hansen11, Ali Hasnain12, Kristina Hettne13, Jaap Heringa14,
Rob W.W. Hooft14,15, Melanie Imming16, Keith G. Jeffery17, Rajaram Kaliyaperumal1, Martijn G.
Kersloot6,18, Christine R. Kirkpatrick19, Tobias Kuhn14, Ignasi Labastida20, Barbara Magagna21, Peter
McQuilton4, Natalie Meyers22, Annalisa Montesanti23, Mirjam van Reisen24, Philippe Rocca-Serra4,
Robert Pergl25, Susanna-Assunta Sansone4, Luiz Olavo Bonino da Silva Santos26, Juliane Schneider27,
George Strawn28, Mark Thompson1, Andra Waagmeester29, Tobias Weigel30, Marcos D.. Wilkinson31,
Egon L. Willighagen9, Peter Wittenburg32, Marco Roos1, Barend Mons†1,26 & Erik Schultes26,33

1Leiden University Medical Center, Leiden, 2333 ZA, Los países bajos

2Institute of Data Science, Maastricht University, Universiteitssingel 60, Maastricht 6229 ER, Los países bajos

3Departamento de Ciencias de la Computación, The University of Manchester, Oxford Road, Manchester M13 9PL, Reino Unido

4Oxford e-Research Centre, Department of Engineering Sciences, Universidad de Oxford, Oxford OX13PJ, Reino Unido

5School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, SO17 1BJ, Reino Unido

6Amsterdam UMC, University of Amsterdam, Ámsterdam 1000 GG, Los países bajos

7European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, Reino Unido

8Harvard University, Cambridge, Massachusetts 02138, EE.UU

9Department of Bioinformatics – BiGCaT, NUTRIM, Maastricht University, Maastricht 6229 ER, Los países bajos

10Conceptual and Cognitive Modeling Research Group (CORE), Free University of Bozen-Bolzano, Bolzano 39100, Italia

11Aalborg University, Aalborg DK-9220, Dinamarca

12Insight Centre for Data Analytics, National University of Ireland Galway, H91 TK33, Irlanda

13Centre for Digital Scholarship, Leiden University Libraries, Leiden, 2333 ZA, Los países bajos

14Departamento de Ciencias de la Computación, Vrije Universiteit Amsterdam, De Boelelaan 11051081 HV Amsterdam, Los países bajos

15Dutch Techcentre for Life Sciences (DTL), Utrecht, Los países bajos

16SURF, Utrecht 3511 EP, Los países bajos

17Keith G Jeffery Consultants, Faringdon, Reino Unido

18Castor EDC, Paasheuvelweg 25, Wing 5D, 1105 BP, Ámsterdam, Los países bajos

19San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, EE.UU

† Corresponding author: Barend Mons (Correo electrónico: barend.mons@go-fair.org, ORCID: 0000-0003-3934-0072).

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20Learning and Research Resources Centre (CRAI), Universitat de Barcelona, 08007 Barcelona, España

21Environment Agency Austria, A-1090 Vienna, Austria

22University of Notre Dame, 75004 París, Francia

23Health Research Board (HRB), Dublín 2, DO2 H638, Irlanda

24Liacs Institute of Advanced Computer Science, Universidad de Leiden, 2311 GJ Leiden, Los países bajos

25Czech Technical University in Prague, Faculty of Information Technology (FIT CTU), 160 00 Prague 6, Czech Republic

26GO FAIR International Support & Coordination Office (GFISCO), Leiden, Los países bajos

27Harvard Catalyst | Clinical and Translational Science Center, Bostón, MAMÁ 02115, EE.UU

28US National Academy of Sciences, Washington DC 20418, EE.UU

29Micelio, Ekeren, Antwerp, Bélgica

30Deutsches Klimarechenzentrum, Bundesstrasse 45a, 20146 Hamburg, Alemania

31Center for Plant Biotechnology and Genomics UPM-INIA, Madrid 28040, España

32Max Planck Computing and Data Facility, Gießenbachstraße 2, 85748 Garching, Alemania

33Leiden Center for Data Science, 2311 EZ Leiden, Los países bajos

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Palabras clave: FAIR guiding principles; FAIR implementation; FAIR convergence; FAIR communities; choices and
challenges

Citación: A. Jacobsen, R. de Miranda Azevedo, norte. Juty, D. Batista, S. Coles, R. Cornet, … & mi. Schultes. FAIR principles:

Interpretations and implementation considerations. Data Intelligence 2(2020), 10–29. doi: 10.1162/dint_r_00024

ABSTRACTO

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since
their publication in 2016. By intention, el 15 FAIR guiding principles do not dictate specific technological
implementations, but provide guidance for improving Findability, Accessibility, Interoperability and
Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles,
because individual stakeholder communities can implement their own FAIR solutions. Sin embargo, it has also
resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. De este modo,
while the FAIR principles are formulated on a high level and may be interpreted and implemented in different
maneras, for true interoperability we need to support convergence in implementation choices that are widely
accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist
accelerated global participation and convergence towards accessible, robusto, widespread and consistent
FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from
existing implementations, or when they spot a gap, accept the challenge to create the needed solution,
cual, ideally, can be used again by other communities in the future. Aquí, we provide interpretations and
implementation considerations (choices and challenges) for each FAIR principle.

Data Intelligence

FAIR Principles: Interpretations and Implementation Considerations

1. INTRODUCCIÓN

The notion of good data stewardship (es decir., maximizing the opportunities for the efficient discovery and
reuse of research outputs) has been around for decades and many implementation choices have already
been made by pioneering communities to extend stewardship with the notion of machine-actionability. El
FAIR principles can be seen as a consolidation of these earlier efforts and emerged from a multi-stakeholder
vision of an infrastructure supporting machine-actionable data reuse, es decir., reuse of data that can be processed
by computers [1], which was later coined the “Internet of FAIR Data and Services” (IFDS) [2].

The FAIR principles are intended as a guide to enable digital resources to become more Findable,
Accessible, Interoperable and Reusable for machines and thus also for humans. These four foundational
principles are more explicitly and measurably described by 15 FAIR guiding principles. Any interpretation
or implementation of the FAIR principles may in essence be chosen as long as they lead to machine-
actionable results. This purposely means that individual stakeholder communities can define their own
solutions and that these can be adapted over time as technologies evolve. While this freedom of choice
may have contributed to the rapid and widespread adoption of the FAIR principles by stakeholders
encompassing scientists, publishers, funding agencies and policy makers (for an overview see Budroni et
Alabama. [3]), it has also brought the inherent risk of incompatible solutions between stakeholder communities.

To reach the goal of an Internet of FAIR Data and Services [2], a global convergence towards accessible,
robusto, widespread and consistent FAIR implementations is required [4]. The first step is to share a common,
high-level interpretation of the FAIR principles. Mons et al. [5] discussed early emerging misinterpretations
of the FAIR foundational principles and clarified their original intent and interpretation. They emphasize
that “FAIR is not a standard … FAIR is not equal to RDF, Linked Data, or the Semantic Web … FAIR is not
just about humans being able to find, access, reformat and finally reuse data … FAIR is not equal to Open
… FAIR is not a Life Science hobby”.

Además, a desire to expand the purposely limited scope of the principles has led to suggestions to
extend the FAIR acronym with additional letters [6], often unrelated to the specific objective of facilitating
data reuse by machines. De este modo, a more detailed and common understanding of the scope, aim and
representative implementation choices for each FAIR principle would be helpful to improve their stepwise
application by diverse stakeholders, and stimulate FAIR adoption in more geographies and new scientific
communities [7][8].

There are several alternative routes towards the implementation of the FAIR principles, some specialized
for different types of digital resources. Communities have already published documents that can guide
implementation choices. Examples are: “the FAIR metrics” [9] and the follow-up Maturity Indicators [10],
“the FAIRy tale” [11], “Top 10 FAIR Data & Software Things” [12], the RDA FAIR Data Maturity Model,
the EC report on “turning FAIR into reality” [13], and the “FAIR principles explained” described on the GO

 https://www.rd-alliance.org/groups/fair-data-maturity-model-wg.

Data Intelligence

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FAIR Principles: Interpretations and Implementation Considerations

FAIR website. Some common community considerations can already be identified: 1) existing technologies
should be used where possible, 2) The process of making resources FAIR (“FAIRification”) can typically be
broken down into steps, allowing the different facets of FAIRness to be prioritized depending on the
resource under consideration [14] and the cost-benefit to the implementer and their community stakeholders,
3) different types of stakeholders adopt complementary roles with respect to implementing FAIR principles
(p.ej. a domain expert, an information scientist, a system engineer, a data archivist, a data mining agent)
where the implementation decisions for certain kinds of stakeholders can be shared and reused across
domains or communities.

To facilitate the harmonization of FAIR implementation choices between and within communities, nosotros
provide, aquí, a directed set of FAIR implementation considerations, which include: a discussion and non-
technical interpretation of the relevant principle being considered; some examples of existing solutions;
and discussions of the challenges that must be considered when approaching the design of a novel solution.
Guided by these implementation considerations, a stakeholder community may choose to reuse a solution
from among existing implementations, or if none of these appear suitable, will have a clear roadmap
describing the challenge in creating a de novo solution for the identified gap. A platform where stakeholder
communities can declare their FAIR choices and challenges – the FAIR Convergence Matrix – is described
in a separate paper [15].

Although maximizing the freedom to operate is a key feature of the “hourglass” approach that drove the
rapid development of the Internet, and allows a multitude of FAIR solutions to flourish, a common
understanding around the original intentions of the guiding principles is crucial to avoid divergence into
non-interoperability once again. The purpose of this article, por lo tanto, is to express the opinions of the
original creators of the principles, supported by discussions of the experiences of pioneering FAIR
implementers.

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2. FROM INTERPRETATION TO IMPLEMENTATION

Before presenting an interpretation of the FAIR principles, it is useful to provide context around some of
the concepts used in the formulation of the guiding principles that seem to have generated confusion in
the early adopter community. De estos, the most prominent are:

Machine-actionability: The four foundational principles – Findability, Accessibility, Interoperability and
Reusability – describe the core objectives of the principles that, if achieved, should enable machines to
make optimal use of data resources. In layman’s terms: FAIR requires that “the machine knows what we
mean”. This is achieved, technically, by making every digital resource FAIR [13] via some technical
implementation choice. De este modo, after implementation, the digital resource may be used as an agent or as the

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 https://www.go-fair.org/fair-principles/.

Data Intelligence

FAIR Principles: Interpretations and Implementation Considerations

substrate for machine learning and AI approaches, in keeping with the interim advice to the US’ National
Institutes of Health (NIH) where it is stated that data should be “AI-Ready”.

This has implications for all four foundational principles:

Findability: Digital resources should be easy to find for both humans and computers. Extensive
machine-actionable metadata are essential for automatic discovery of relevant datasets and services,
and are therefore an essential component of the FAIRification process [14].
Accessibility: Protocols for retrieving digital resources should be made explicit, for both humans and
máquinas, including well-defined mechanisms to obtain authorization for access to protected data.
Interoperability: When two or more digital resources are related to the same topic or entity, it should
be possible for machines to merge the information into a richer, unified view of that entity. Similarmente,
when a digital entity is capable of being processed by an online service, a machine should be capable
of automatically detecting this compliance and facilitating the interaction between the data and that
tool. This requires that the meaning (semantics) of each participating resource – be they data and/or
services service – is clear.
Reusability: Digital resources are sufficiently well described for both humans and computers, semejante
that a machine is capable of deciding: if a digital resource should be reused (es decir., is it relevant to the
task at-hand?); if a digital resource can be reused, and under what conditions (es decir., do I fulfill the
conditions of reuse?); and who to credit if it is reused.

(Meta)datos: The concepts of “data” and “metadata” occur throughout the 15 FAIR guiding principles. En
the original paper [1], it is stated that data is used to refer to all digital resources (not just data in the
restricted sense, pero también, Por ejemplo, software tools). Metadata is any description of a resource that can
serve the purpose of enabling findability and/or reusability and/or interpretation and/or assessment of that
resource. Avoiding the “one person’s metadata is another person’s data” confusion, FAIR addresses this by
treating every data/metadata pair in-isolation; eso es, metadata is the descriptor, and data is the thing being
descrito, unambiguously, within the context of that pair. Por lo tanto, this holds true even if, in another
contexto, the thing being described is, sí mismo, metadata. This inherently implies that metadata must also be a
FAIR digital resource in its own right.

Other concepts used in the 15 FAIR guiding principles, such as “searchable resource”, “protocol”,
“knowledge representation language”, “vocabularies”, “qualified reference”, “usage license”, y
“standards” are further defined here, in the form of abbreviated interpretations of each FAIR principle. En
addition, to support the interpretation, we provide implementation considerations and illustrative examples
where these already exist. These are available as a FAIR resource.

 https://acd.od.nih.gov/documents/presentations/06132019AI.pdf.
 https://w3id.org/fair/icc/terms/FAIR-ICC-Model.

Data Intelligence

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FAIR Principles: Interpretations and Implementation Considerations

3. INTERPRETATIONS AND IMPLEMENTATION CONSIDERATIONS PER FAIR GUIDING
PRINCIPLE

3.1 Principle F

3.1.1 Principle F1: (meta)data are assigned a globally unique and persistent identifier

1) Interpretation

Principle F1 states that digital resources, es decir., data and metadata, must be assigned a globally unique and
persistent identifier in order to be found and resolved by computers. This is the most fundamental of the
FAIR principles, as globally unique and persistent identifiers are essential elements found in all of the other
FAIR principles. Globally unique means that the identifier is guaranteed to unambiguously refer to exactly
one resource in the world (please note that global should be interpreted as universal as there are digital
assets outside the world). Por lo tanto, it is insufficient for it to be unique only locally (p.ej. unique within a
single, local database). Persistence refers to the requirement that this globally unique identifier is never
reused in another context, and continues to identify the same resource, even if that resource no longer
exists, or moves. In practice, this often involves using a third-party to generate an identifier that has
guaranteed longevity and is project/organization-independent.

2) Implementation considerations

Current challenges relate to ensuring the longevity of identifiers – in particular, that identifiers created
by a project/community should survive the termination of the project or the dissolution of the community.
Obtaining a persistent identifier, por lo tanto, may require reliance on a third-party organization that promises
longevity, and maintains these identifiers independently of the project/community. Current choices are
for each community to choose, for all appropriate digital resources (es decir., data and metadata), identifier
registration service(s) such as these that ensure global uniqueness and that also comply with the
community-defined criteria for identifier persistence and resolvability.

A common example of a useful identifier is the Digital Object Identifier (DOI) which is guaranteed by
the DOI specification to be globally unique and persistent. DOIs provide an additional service, bajo
principle A1, of being able to direct calls to the source data to the location of that data, even if the identified
data moves. This ensures that identifiers are stable and valid beyond the project that generated them. En
some circumstances, again with DOIs being an example, third-party persistent identifiers may also provide
support for principle A2 (that metadata exists beyond the lifespan of the data) since these identifiers may
still be responsive to Web calls, and be capable of providing metadata, even if the source resource is no
longer active. For a discussion on identifiers see [16][17].

3.1.2 Principle F2: data are described with rich metadata

1) Interpretation

Whereas principle F1 enables unambiguous identification of resources of interest, principle F2 speaks
to the ability to discover a resource of interest through, Por ejemplo, search or filtering. Digital resources

Data Intelligence

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FAIR Principles: Interpretations and Implementation Considerations

must be described with rich metadata – descriptors of the content of the resource referred to by that
identifier. It is hard to generally define the minimally required “richness” of this metadata, except that the
more generous it is, both for humans and computers, the more specifically findable it becomes in refined
searches. While other principles speak to the specific kinds of metadata that should be included, principle
F2 simply says that a digital resource that is not well-described cannot be accurately discovered. De este modo, este
principle encourages data providers to consider the various facets of search that might be employed by a
user of their data, and to support those users in their discovery of the resource. To enable both global and
local search engines to locate a resource, generic and domain-specific descriptors should be provided.

2) Implementation considerations

It is a challenge for each domain-specific community to define their own metadata descriptors necessary
for optimizing findability. The minimal “richness” of the metadata should be defined so that it serves its
intended purpose and should also be guided by the requirements of the other FAIR principles. This then
poses a challenge to each community to create machine-actionable templates that facilitate capturing
uniform and harmonized metadata about similar data resources among all community stakeholders, y
to provide a means to ensure that this metadata is updated and curated [17].

Examples of metadata schemata can be found in FAIRsharing [18][19] and include for instance the Data
Documentation Initiative (DDI), the HCLS Dataset Descriptors, and many domain-specific “minimal
information” models that have been invented.

3.1.3 Principle F3: metadata clearly and explicitly include the identifier of the data it describes

1) Interpretation

Principle F3 states that any description of a digital resource must contain the identifier of that resource
being described. Por ejemplo, the description of a computational workflow, should explicitly contain the
identifier for that workflow in a manner that is unambiguous. This is especially important where the resource
and its metadata are stored independently, but persistently linked, which is generally considered good
practice in FAIR. The purpose of this principle is twofold. Primero, it is perhaps trivial to say that a descriptor
should explicitly say what object it is describing; sin embargo, there is a second, less-obvious reason for this
principle. Many digital objects (such as workflows, as mentioned above) have well-defined structures that
may disallow the addition of new fields, including fields that could point to the metadata about that digital
object. Por lo tanto, if you have one of these digital objects in-hand, the only way to discover its metadata is
through a search using the identifier of that digital object. De este modo, by requiring that a metadata descriptor
contains the identifier of the thing being described, that identifier may then successfully be used as the
search term to discover its metadata record.

 https://fairsharing.org/standards/.
 https://doi.org/10.25504/FAIRsharing.1t5ws6.
 https://fairsharing.org/FAIRsharing.s248mf.

Data Intelligence

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FAIR Principles: Interpretations and Implementation Considerations

2) Implementation considerations

It is a challenge to each community to choose a machine-actionable metadata model that explicitly links

a resource and its metadata.

An example of a technology that provides this link is FAIR Data Point [20], which is based on the Data
Catalogue model (DCAT) that provides not only unique identifiers for potentially multiple layers of
metadata, but also provides a single, predictable, and searchable path through these layers of descriptors,
down to the data object itself.

3.1.4 Principle F4: (meta)data are registered or indexed in a searchable resource

1) Interpretation

Principle F4 states that digital resources must be registered or indexed in a searchable resource. El
searchable resource provides the infrastructure by which a metadata record (F1) can be discovered, usando
either the attributes in that metadata (F2) or the identifier of the data object itself (F3) [21].

2) Implementation considerations

Current challenges are numerous, significantly limiting, and largely outside of the control of the average
data provider. Primero, there is no single-source for search that currently indexes all possible metadata fields
in all domains. Segundo, there is no uniform way to execute a search, and thus every search tool must be
accessed with tool-specific software. Finalmente, many search engines forbid automated searches, precluding
their use by FAIR-enabled software. Various initiatives are emerging that attempt to address this, at least in
part, by providing a well-defined, machine-accessible search interface over indexed metadata. Sin embargo,
to our knowledge, none of these currently index all possible metadata properties, nor do they span all
possible domains/communities; bastante, they focus on specific metadata schemas such as schema.org, en el
expense of other well-established metadata formats such as DCAT, and/or are limited to specific communities
such as biotechnology, astronomía, law, or government/administration. Current choices are for each
community to choose, and publicly declare, what search engine to use for their own purposes, general or
field-specific, and should at a minimum provide metadata following the standard that is indexed by the
search engine of choice. They should also provide a machine-readable interface definition that would allow
an automated search without human intervention.

An example of a generic searchable resource that supports manual exploration is Google Dataset Search;
sin embargo, this suffers from several of the problems mentioned above, En particular, that it indexes only
certain types of metadata (schema.org) and the search cannot be automated under the Google Terms of
Servicio, and therefore cannot be implemented within FAIR software.

 https://www.w3.org/TR/vocab-dcat/.
 https://toolbox.google.com/datasetsearch.

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FAIR Principles: Interpretations and Implementation Considerations

3.2 Principle A

3.2.1 Principle A1: (meta)data are retrievable by their identifier using a standardized communications
protocol

1) Interpretation

A primary purpose of identifying a digital resource is to simultaneously provide the ability to retrieve the
record of that digital resource, in some format, using some clearly-defined mechanism: hence the retrievability
is a facet of FAIR Accessibility. Aquí, the emphasis is on “ability”: there should be no additional barrier
retrieval of the record by some agent when its access protocol (A1.1) results in permitted access to that
record. Note that the agent may be a machine working behind a firewall, if that agent has been permitted
access. For fully mechanized access, this requires that the identifier (F1) follows a globally-accepted schema
that is tied to a standardized, high-level communication protocol. The “standardized communication
protocol” is critical here. Its purpose is to provide a predictable way for an agent to access a resource,
regardless of whether unrestricted access to the content of the resource is granted or not.

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An example of a standardized access protocol is the Hypertext Transfer Protocol (HTTP); sin embargo, FAIR
does not preclude non-mechanized access protocols, such as a verbal request to the data holder in the
case of highly sensitive data, so long as the access protocol is explicit and clearly defined. Conditions of
compliance are further specified in sub-principles A1.1 and A1.2.

3.2.2 Sub-Principle A1.1: the protocol is open, free and universally implementable

1). Interpretation

The protocol (mechanism) by which a digital resource is accessed (p.ej. queried) should not pose any
bottleneck. It describes an access process, hence does not directly pertain to restrictions that apply to using
the resource. The protocols underlying the World-Wide Web, such as HTTP, are an archetype for an open,
gratis, and universally implementable protocol. Such protocols reduce the cost of gaining access to digital
resources, because they are well defined and open and allow any individual to create their own standards-
compliant implementation. That the use of the protocols is free ensures that those lacking monetary means
can equitably access the resource. That it is universally implementable ensures that the technology is
available to all (and not restricted, por ejemplo, by country or a sub-community), thus encompassing both
the “gratis” and “libre” meaning of “free”11.

2) Implementation considerations

Current challenges are to explicitly and fully document access protocols that are not open/free (para
ejemplo, access only after personal contact) and make those protocols available as a clearly identified facet

 https://www.w3.org/Protocols/.
11 https://dash.harvard.edu/handle/1/4322580.

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FAIR Principles: Interpretations and Implementation Considerations

of the machine-readable metadata. Current choices are for communities to choose standardized
communication protocols that are open, free and universally implementable.

The most common example of a compliant protocol is the HTTP protocol that underlies the majority of
Web traffic. It has additional useful features, including the ability to request metadata in a preferred format,
and/or to inquire as to the formats that are available. It is also widely supported by software and common
programming languages.

3.2.3 Sub-Principle A1.2: the protocol allows for an authentication and authorization procedure, dónde
necessary

1) Interpretation

This principle clearly demonstrates that FAIR is not equal to “open”. Some digital resources, such as data
that have access restrictions based on ethical, legal or contractual constraints, require additional measures
to be accessed. This often pertains to assuring that the access requester is indeed that requester
(authentication), that the requester’s profile and credentials match the access conditions of the resource
(authorization), and that the intended use matches permitted use cases (p.ej. non-commercial purposes only)
(see also R1.1, where there are requirements to provide explicit documentation about who may use the
datos, and for what purposes). At the level of technical implementation, an additional authentication and
authorization procedure must be specified, if it is not already defined by the protocol (see A1.1). A requester
can be a human or a machine agent. In the latter case it is probably a proxy for a human or an organization
to which the authentication and authorization protocol should be applied, in which case, the machine
should be expected to present the appropriate credentials. The principle requires that a FAIR resource must
provide such a protocol, but the protocol itself is not further specified. En la práctica, an Internet of FAIR Data
and Services cannot function without implementing Authentication and Authorization Infrastructure (AAI,
see also [22]).

2) Implementation considerations

Current choices are for communities to choose protocols to use when controlling access of agents to
meta(datos). Preferably these should be as generic as possible and as domain specific as necessary. Attempts
to harmonize AAI approaches are numerous, but not covered in this article.

De nuevo, the most common example of a compliant protocol is the HTTP protocol. Another example is
the life science AAI protocol. Brewster et al. [22] describe an early implementation of an ontology-based
approach to this challenge.

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FAIR Principles: Interpretations and Implementation Considerations

3.2.4 Principle A2: metadata are accessible, even when the data are no longer available

1) Interpretation

There is a continued focus on keeping relevant digital resources available in the future. Data may no
longer be accessible either by design (p.ej. a defined life-span within limited financial resources or legal
requirements to destroy sensitive data) or by accident. Sin embargo, given that those data may have been used
and are referenced by others, it is important that consumers have, at the very least, access to high quality
metadata that describes those resources sufficiently to minimally understand their nature and their
provenance, even when the relevant data are not available anymore. This principle relies heavily on the
“second purpose” of principle F3 (the metadata record contains the identifier of the data), because in the
case where the data record is no longer available, there must be a clear and precise way of discovering its
historical metadata record. This aspect of accessibility is further elaborated in the Joint Declaration of Data
Citation Principles [23].

2) Implementation considerations

Current choices/challenges are for communities to choose/define a persistence policy for metadata that
describes data that may not always be available, choose/define machine-actionable templates for a
persistence policy document for metadata, and in addition choose/define a machine-actionable scheme to
reference the metadata persistence policy.

Examples of early attempts to address this critical principle relates closely to the principles of digital
curation12 including the concept of a FAIR compliant DMP (Data Management Plan)13 [24]. Many other
efforts are underway to improve the long-term stewardship of reusable digital resources.

3.3 Principle I

3.3.1 Principle I1: (meta)data use a formal, accessible, shared, and broadly applicable language for
knowledge representation

1) Interpretation

Consumers spend a disproportionate amount of time trying to make sense of the digital resources they
need and designing accurate ways to combine them. This is most often due to a lack of suitably unambiguous
content descriptors, or a lack of such descriptors entirely with respect to non-machine-interpretable data
formats such as tables or “generic” XML. Community-defined data exchange formats work reasonably well
within their original scope of a few types of data and a relatively homogeneous community, but not well
beyond that. This makes interoperation and integration an expensive, often impossible task (even for

12 http://www.dcc.ac.uk/.
13 http://www.dcc.ac.uk/resources/data-management-plans.

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FAIR Principles: Interpretations and Implementation Considerations

humanos), but also means that machines cannot easily make use of digital resources, which is the primary
goal of FAIR. Por ejemplo, when a machine visits two data files in which a field “temperature” is present,
then it will need more contextual descriptions to distinguish between weather data in one file and body
temperature measurements in another. Achieving a “common understanding” of digital resources through
a globally understood “language” for machines is the purpose of principle I1, with an emphasis on
“knowledge” and “knowledge representation”. This becomes critical when many differently formatted
resources need to be visited or combined across organizations and countries and is especially challenging
for interdisciplinary studies or for meta-analyses, where results from independent organizations, pertaining
to the same topic, must be combined. In this context, the principle says that producers of digital resources
are required to use a language (es decir., a representation of data/knowledge) that has a defined mechanism for
mechanized interpretation – a machine-readable “grammar” – where, Por ejemplo, the difference between
an entity, as well as any relevant relationship between entities, is defined in the structure of the language
sí mismo. This allows machines to consume the information with at least a basic “understanding” of its content.
It is a step towards a common understanding of digital resources by machines, which is a prerequisite for
a functional Internet of FAIR Data and Services. Several technologies can be chosen for principle I1.

2) Implementation considerations

Communities will have to choose an available technology or decide how they will otherwise deal with
multiple representations and languages. In any case, they will have to make sure that each data item that
is the same in multiple resources is interpreted in exactly the same way by every agent (human and
computer), and that how items across resources relate to one another can be unambiguously understood
by all agents [25]. The key consideration in this regard is that FAIR speaks to the ability of data to be reused
by a generic agent, rather than a community-specific agent. This is most easily accomplished by making
the knowledge available in the most widely used format(s), even if this means duplication of the information
in the community-specific format.

The most widely-accepted choice to adhere to this principle, at the present time, is the Resource
Description Framework (RDF) which is the W3C’s recommendation for how to represent knowledge on the
Web in a machine-accessible format14. Other choices may also be acceptable, for instance when they are
already in widespread use within a given community. In that case, it would be helpful for the community
to also provide a “translator” between their preferred format, and a more widely used format such as RDF.

3.3.2 Principle I2: (meta)data use vocabularies that follow FAIR principles

1) Interpretation

Principle I2 uses “vocabularies” to refer to the methods that unambiguously represent concepts that exist
in a given domain. The use of shared, and formally structured (I1), sets of terms is an essential part of FAIR.
Terminology systems, including flat “vocabularies”, hierarchical “thesauri” and more granular specifications

14 https://www.w3.org/RDF/.

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FAIR Principles: Interpretations and Implementation Considerations

of knowledge such as data models and ontologies, play an important role in community standards. Sin embargo,
the vocabularies used for metadata or data also need to be findable, accessible, interoperable, and reusable
in their own right so that users (including machines) can fully understand the meaning of the terms used
in the metadata. This principle has been criticized as “circular” but as has been made clear earlier in this
artículo, the simple use of a “label” (p.ej. “temperature”) is insufficient to enable a machine to understand
both the intent of that label (Body temperature? Melting temperature?) and the contexts within which it can
be properly linked – same-with-same – to other similarly-labelled data. I2, por lo tanto, requires that the
vocabulary terms used in the knowledge representation language (principle I1) can be sufficiently
distinguished, by a machine, to ensure detection of “false agreements” as well as “false disagreements”.

2) Implementation considerations

Current considerations are for communities to ensure that terminology systems and, por ejemplo, el
units of measure, classifications, and relationship definitions are themselves FAIR. Thesauri that are
proprietary and not universally accessible should be avoided wherever possible, because machines (y
indeed particular countries, regions or communities as a whole) may not have the authority to access their
definitions, such that even data that is accessible after authentication via A1.2 may not be useful to an
agent that has no authority to access the concept definitions used within that data.

Ontologies defined in the “Web Ontology Language” (OWL) and shared via a publicly accessible registry
(p.ej. BioPortal for life science ontologies15) are examples of formally represented, accessible, mapped, y
shared knowledge representations in a broadly applicable language for knowledge representation, that are
also compliant with the Findability requirements of FAIR, since BioPortal provides a machine-accessible
search interface.

3.3.3 Principle I3: (meta)data include qualified references to other (meta)datos

1) Interpretation

An important aspect of FAIR is that data or metadata, generally speaking, does not exist in a silo – we
must do what is necessary to ensure that the knowledge representing a resource is connected to that of
other resources to create a meaningfully interlinked network of data and services. A “qualified reference”
is a reference to another resource (es decir., referencing that external resource’s persistent identifier), en el cual
the nature of the relationship is also clearly specified. Por ejemplo, when multiple versions of a metadata
file are available, it may be useful to provide links to prior or next versions using a named relation such as
“prior version” or “next version” (preferably using an appropriate community standard relationship that
itself conforms to the FAIR principles). In the case of data, imagine a dataset that specifies the population
of cities around the world. To be FAIR with respect to principle I3, the data could contain links to a resource
containing city data (p.ej., Wikidata16 [26]), geographical and geospatial data, or other related domain

15 https://bioportal.bioontology.org/.
16 http://wikidata.org/.

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FAIR Principles: Interpretations and Implementation Considerations

resources that are generated by that city, so long as they are properly qualified references using meaningful,
clearly-interpretable relationships. It is also important to note that many different metadata files (containers)
being FAIR digital resources in themselves, can be pointing to the same “target” object (a data set or a
workflow for instance). We can for instance have intrinsic metadata (“what is this”) and how was it created
(provenance type metadata) as well as “secondary” metadata that are for instance created (separately and
later in time) by reusers of a particular digital resource. These could all be metadata containers essentially
describing the same digital resource from different perspectives. This principle therefore also relates to the
good practice to clearly distinguish between metadata (files/containers) and the resources they describe.

2) Implementation considerations

The considerations and choices made here are based on the same reasoning as the decisions made for
principle I2. Vocabularies (often formal ontologies) of both concepts and relationships exist, and an
appropriate relationship should either be selected from one of these, or “coined” and properly published
following the FAIR Principles.

It is worth noting as an example that several “upper ontologies” such as the SemanticScience Integrated
Ontology17 have a wide range of precisely-defined relationships that can be used as-is, or as a starting-point
for a newly-minted relationship that is more specific than the one provided in the upper-ontology. El
benefit of “inheriting” from higher-level relationships is that agents capable of understanding these higher
level concepts, can infer at least a basic interpretation of the intent of the new relationship coined within
the community, and therefore enhances interoperability.

3.4 Principle R

3.4.1 Principle R1: (meta)data are richly described with a plurality of accurate and relevant attributes

1) Interpretation

On its surface, principle R1 appears very similar to principle F2. Sin embargo, the rationale behind principle
F2 is to enable effective attribute-based search and query (findability), while the focus of R1 is to enable
machines and humans to assess if the discovered resource is appropriate for reuse, given a specific task.
Por ejemplo, not all gene expression data for a given locus are relevant to a study of the effects of heat
stress. While inappropriate data may be discovered by the agent’s initial search (principle F2) for expression
data about a given gene, here we address the ability to assess the discovered data based on suitability-for-
purpose. This reiterates the need for providers to consider not only high-level metadata facets, that will
assist in generic search, but also to consider more detailed metadata that will provide much more
“operational” instructions for re-use. In this setting, a wide variety of factors may be needed to determine
whether a resource is suitable for inclusion in an analysis, and how to adequately process it.

17 https://bioportal.bioontology.org/ontologies/SIO.

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FAIR Principles: Interpretations and Implementation Considerations

The term “plurality” is used to indicate that the metadata author should be as generous as possible, no
presuming who the consumer might be, and therefore provide as much metadata as possible to support
the widest variety of use-cases and agent needs. The sub-principles R1.1, R1.2 and R1.3 define some critical
types of attributes that contribute to R1.

3.4.2 Sub-Principle R1.1: (meta)data are released with a clear and accessible data usage license

1) Interpretation

Digital resources and their metadata must always, without exception, include a license that describes
under which conditions the resource can be used, even if that is “unconditional”. By default, resources
cannot be legally used without this clarity. Note also that a license that cannot be found by an agent, es
effectively the same as no license at all. Además, the license may be different for a data resource and
the metadata that describes it, which has implications for the indexing of metadata v.v. findability. Esto es
a clear public domain statement, an equivalent such as terms of use or computer protocol to digitally
facilitate an operation (for instance a smart contract). De este modo, the absence of a license does not indicate
“open”, but rather creates legal uncertainty that will deter (En realidad, in many cases legally prevent) reuse.
Note also that the combination of resources with restrictive license conditions may lead to adverse effects,
and ultimately preclude the use of the combined resources. In order to facilitate reuse, the license chosen
should be as open as possible.

2) Implementation considerations

A current challenge is that there is currently no well-defined relationship(s) that can be used to distinguish
a license that applies to the data being described, versus a license that applies to the metadata record itself,
resulting in potential ambiguity in the interpretation of a license referred-to in the metadata record. Actual
choices are for communities to choose which usage license(s) or licensing requirements to reusable digital
resources as well as to their metadata for its own purposes, but also consider broader reuse than originally
anticipated or intended.

There are good reasons for choosing a CC0 license for data18 and these considerations should be assessed,
alongside all other considerations, when a community decides on the license they wish to apply. It is
critical, sin embargo, that a license is chosen. The community should then ensure that a qualified link to that
license is contained in the metadata record.

3.4.3 Sub-Principle R1.2: (meta)data are associated with detailed provenance

1) Interpretation

Detailed provenance includes facets such as how the resource was generated, why it was generated, por
whom, under what conditions, using what starting-data or source-resource, using what funding/resources,

18 http://sulab.org/2016/08/open-data-should-mean-cc0/.

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FAIR Principles: Interpretations and Implementation Considerations

who owns the data, who should be given credit, and any filters or cleansing processes that have been
applied post-generation. Provenance information helps people and machines assess whether a resource
meets their criteria for their intended reuse, and what data manipulation procedures may be necessary in
order to reuse it appropriately.

2) Implementation considerations

Current choices are for communities to choose a set of metadata descriptions to optimize provenance
to optimally enable machine and human reusability for its own purposes. These choices, y, as argued
before the richness of the provenance associated with a digital resource will strongly influence its actual
reuse. Por lo tanto, the implementation considerations for implementing according to this principle are
inherently the same as described for principle F2, but now more focused on appropriateness for reuse than
on findability per se.

Provenance descriptions can for instance be implemented following community specific templates
according to the PROV-Template19 approach. These templates allow to predefine the structure of the intended
collection of provenance information using variables which are later instantiated with appropriate data
extracted from existing process output. Such templates also reduce the burden on community members to
deeply understand the highly structured PROV ontology, and the well-defined data structures that emerge
from its use – that is to say, PROV should not be treated as a simple vocabulary from which terms can be
selected, but rather as a model that constrains how those terms must be used in relation to one another.
Several early tools are under development to make the construction of FAIR metadata easier, including for
instance CEDAR20, CASTOR21 and the knowledge models in the Data Stewardship Wizard22 [24].

3.4.4 Sub-Principle R1.3: (meta)data meet domain-relevant community standards

1) Interpretation

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Where community standards or best practices for data archiving and sharing exist, they should be
seguido. Several disciplinary communities have defined Minimal Information Standards describing most
often the minimal set of metadata items required to assess the quality of the data acquisition and processing
and to facilitate reproducibility. Such standards are a good start, noting that true (interdisciplinary) reusability
will generally require richer metadata. For a list of such standards, consult FAIRsharing23.

2) Implementation considerations

Current choices are for a community to choose which practices to use for data and metadata, taking into
full consideration the relevant inter-domain interoperability requirements. Communities must then take-on

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19 https://provenance.ecs.soton.ac.uk/prov-template/.
20 https://more.metadatacenter.org/tools-training/outreach/cedar-template-model.
21 https://www.castoredc.com/for-researchers/.
22 https://ds-wizard.org.
23 https://fairsharing.org/standards/ / https://doi.org/10.1038/s41587-019-0080-8.

Data Intelligence

FAIR Principles: Interpretations and Implementation Considerations

the challenge of deciding which metadata elements, addressed within their community’s “boutique”
standard(s), should be additionally represented using a more global standard (principles F2 and R1.2), incluso
if this results in duplication of metadata, such that it can be used for search and interpretation by more
generic, third-party agents.

An example of minimal information standards is the MIAME standard [27], and various metadata profiles

have been defined on top of specifications (p.ej. various DCAT profiles).

4. DISCUSIÓN

The high level foundational principles of Findability, Accessibility (under well defined conditions),
Interoperability (also across prior silos), which together enable the ultimate aim to enable trusted, effective
and sustained Reuse of research resources are widely endorsed. Sin embargo, the examples given in this paper
already demonstrate that interpretation of the derived guiding principles for implementation is far from
straightforward. For some implementation considerations there are already existing solutions, so communities
can choose to reuse such solutions. The prerequisite is of course that these solutions are themselves FAIR,
so that people (and machines) first of all know about them and can reuse them as solutions in their own
implementations. In some cases, sin embargo, implementation of a component of the Internet of FAIR Data
and Services has not been addressed before within a particular setting, and solutions developed in other
settings may not (fully) suffice. In that case a community of practice is faced with an implementation
challenge. To make this difference explicit, we have distinguished two different FAIR implementation
considerations – choices and challenges. Aquí, we have tried to re-address the guiding principles from two
perspectives: Primero, a short interpretation and second, the perspective of choices and challenges of some
pioneering implementers. Based on the citation record of the original paper we can anticipate that well
encima 1000 groups around the world have undertaken efforts to make specific implementation choices and
actions24. Interoperability (arguably the most challenging aspect of FAIR) is of course very much dependent
on convergence on solutions and standards, but history has taught us that top down standard setting, y
enforcement is very cumbersome and, in many cases, also inhibitory and undesirable. We therefore highly
commend the efforts of communities and consortia such as the ESFRI scheme in Europe, the Innovative
Medicines initiative, but also international organizations such as RDA, CODATA and GO FAIR to gently
guide convergence based on community-emerging best practices. No-one ever said FAIR was easy, but we
have to go through the hardship of making our resources FAIR to enable better science together. It benefits
everyone to make it as easy as possible for communities to make steps in the direction of optimally
achievable FAIRness in their domain. This obviously critically includes reuse of each other’s solutions where
posible. Initiatives such as FAIRsharing [18][19] are examples of attempts to support stakeholder
communities in sharing and reusing FAIR solutions. Eventualmente, agreement of the FAIR implementation
choices between different communities should lead to convergence [4] Sin embargo, the question remains:
convergence to what? This process will not lead to the ultimate goal of FAIR (optimal Reuse) unless we at

24 At the publication date of this article the original paper [1] had close to 1600 citations counted in Google Scholar.

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FAIR Principles: Interpretations and Implementation Considerations

least agree on the intentions of the principles we try to follow. Próximo, convergence needs to be technologically
enabled, such as by a community governed platform e.g. the GO FAIR Convergence Matrix [15].

Choices and challenges have no impact on convergence in isolation, which is why the role of convening
communities is essential. There is, sin embargo, a fluidity in the concept of community. There are many existing
implementation-oriented communities, such as scientific unions, research infrastructures and global
communities of practice. These should be optimally enabled to make choices together. Implementation
choices made in smaller self-identified communities of practice could eventually be accepted and merged
with larger organizations. Using “stick” based compliance incentives (p.ej., government health ministries or
funding agencies that create FAIR certifications or requirements for funding) could prove a strong driving
force towards convergence. Sin embargo, this process needs to be guided and will not always occur
spontaneously; not so much because communities do not want to reach convergence and hence
interoperability, but because they are “too busy minding their own business”. International coordination
and a platform to address exactly that convergence process is needed.

In actual practice, implementation choices and challenges should be known and will be implemented
mainly by FAIR-aware data stewards, who ultimately work in the institutes or projects alongside those who
are generating the data and metadata. Their choices should constitute a large part of the Data Stewardship
Plans of researchers [24]. En otras palabras, convergence will only happen if data stewards collectively decide
to converge.

We hope that the interpretations of the FAIR guiding principles and the exemplar implementation choices
and challenges presented here will inspire developers to contribute to infrastructure, software, and services
that support FAIR implementation, and communities to choose their specific focus with the FAIRification
process striving towards the common goals of an Internet of FAIR Data and Services.

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ACKNOWLEDGEMENTS

The work of A. Jacobsen, C. Evelo, METRO. Thompson, R. Cornet, R. Kaliyaperuma and M. Roos is supported
by funding from the European Union’s Horizon 2020 research and innovation program under the EJP RD
COFUND-EJP N° 825575. The work of A. Jacobsen, C. Evelo, C. Goble, METRO. Thompson, norte. Juty, R. Hooft,
METRO. Roos, S-A. Sansone, PAG. McQuilton, PAG. Rocca-Serra and D. Batista is supported by funding from ELIXIR
EXCELERATE, H2020 grant agreement number 676559. R. Hooft was further funded by NL NWO NRGWI.
obrug.2018.009. norte. Juty and C. Goble were funded by CORBEL (H2020 grant agreement 654248). norte. Juty,
C. Goble, S-A. Sansone, PAG. McQuilton, PAG. Rocca-Serra and D. Batista were funded by FAIRplus (IMI grant
agreement 802750). norte. Juty, C. Goble, METRO. Thompson, METRO. Roos, S-A. Sansone, PAG. McQuilton, PAG. Rocca-Serra
and D. Batista were funded by EOSClife H2020-EU (grant agreement number 824087). C. Goble was
funded by DMMCore (BBSRC BB/M013189/). METRO. Thompson, METRO. Roos received funding from NWO (VWData
400.17.605). S-A. Sansone, PAG. McQuilton, PAG. Rocca-Serra and D. Batista have been funded by grants awarded
to S-A. Sansone from the UK BBSRC and Research Councils (BB/L024101/1; BB/L005069/1), EU (H2020-EU
634107; H2020-EU 654241, IMI (IMPRiND 116060), NIH Data Common Fund, and from the Wellcome

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FAIR Principles: Interpretations and Implementation Considerations

Confianza (ISA-InterMine 212930/Z/18/Z; FAIRsharing 208381/A/17/Z). The work of A. Waagmeester has been
funded by grant award number GM089820 from the National Institutes of Health. METRO. Kersloot was funded
by the European Regional Development Fund (KVW-00163). The work of N. Meyers was funded by the
Fundación Nacional de Ciencia (OAC 1839030). The work of M.D. Wilkinson is funded by Isaac Peral/Marie
Curie cofund with the Universidad Politécnica de Madrid and the Ministerio de Economía y Competitividad
grant number TIN2014-55993-RM. The work of B. Magagna, mi. Schultes, l. da Silva Santos and K. jeffery
is funded by the H2020-EU 824068. The work of B. Magagna, mi. Schultes and L. da Silva Santos is funded
by the GO FAIR ISCO grant of the Dutch Ministry of Science and Culture. The work of G. Guizzardi is
supported by the OCEAN Project (FUB). METRO. Courtot received funding from the Innovative Medicines
Initiative 2 Joint Undertaking under grant agreement No. 802750. R. Cornet was further funded by
FAIR4Health (H2020-EU grant agreement number 824666). k. Jeffery received funding from EPOS-IP
H2020-EU agreement 676564 and ENVRIplus H2020-EU agreement 654182.

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