The FAIR Principles
(https://doi.org/10.25504/FAIRsharing.WWI10U) provide guidelines for the publication
of digital resources such as datasets, code, workflows, and research objects, in a
manner that makes them Findable, Accessible, Interoperable, and Reusable (FAIR). The
Principles have rapidly been adopted by publishers, funders, and pan-disciplinary
infrastructure programmes and societies. The Principles are aspirational, in that
they do not strictly define how to achieve a state of "FAIRness", but rather they
describe a continuum of features, attributes, and behaviors that will move a digital
resource closer to that goal. This ambiguity has led to a wide range of interpretations
of FAIRness, with some resources even claiming to already "be FAIR"! The increasing
number of such statements, the emergence of subjective and self-assessments of FAIRness
, and the need of data and service providers, journals, funding agencies, and regulatory
bodies to qualitatively or quantitatively evaluate such claims, led us to self-assemble
and establish a FAIR Metrics group (http://fairmetrics.org) to pursue the goal of
defining ways to measure FAIRness.
As co-authors of the FAIR Principles and its associated manuscript, founding this
small focus group was a natural and timely step for us, and we foresee group membership
expanding and broadening according to the needs and enthusiasm of the various stakeholder
communities. Nevertheless, in this first phase of group activities we did not work
in isolation, but we gathered use cases and requirements from the communities, organizations
and projects we are core members of, and where discussions on how to measure FAIRness
have also started. Our community network and formal participation encompasses generic
and discipline-specific initiatives, including: the Global and Open FAIR (http://go-fair.org),
the European Open Science Cloud (EOSC; https://eoscpilot.eu), working groups of the
Research Data Alliance (RDA; https://www.rd-alliance.org) and Force11 (https://www.force11.org),
the Data Seal of Approval
, Nodes of the European ELIXIR infrastructure (https://www.elixir-europe.org), projects
under the USA National Institutes of Health (NIH)’s Big Data to Knowledge Initiative
(BD2K) and its new Data Commons Pilots (https://commonfund.nih.gov/bd2k/commons).
In addition, via the FAIRsharing network and advisory board (https://fairsharing.org),
we are also connected to open standards-developing communities and data policy leaders,
and also editors and publishers, especially those very active around data matters,
such as: Springer Nature’s Scientific Data, Nature Genetics and BioMedCentral, PloS
Biology, The BMJ, Oxford University Press’s GigaScience, F1000Research, Wellcome Open
Research, Elsevier, EMBO Press and Ubiquity Press.
The converging viewpoints on FAIR metrics and FAIRness, arising from our information-gathering
discussions with these various communities and stakeholders groups, can be summarized
as it follows:
Metrics should address the multi-dimensionality of the FAIR principles, and encompass
all types of digital objects.
Universal metrics may be complemented by additional resource-specific metrics that
reflect the expectations of particular communities.
The metrics themselves, and any results stemming from their application, must be FAIR.
Open standards around the metrics should foster a vibrant ecosystem of FAIRness assessment
Various approaches to FAIR assessment should be enabled (e.g. self-assessment, task
forces, crowd-sourcing, automated), however, the ability to scale FAIRness assessments
to billions if not trillions of diverse digital objects is critical.
FAIRness assessments should be kept up to date, and all assessments should be versioned,
have a time stamp, and be publicly accessible.
FAIRness assessments presented as a simple visualization, will be a powerful modality
to inform users and guide the work of producers of digital resources.
The assessment process, and the resulting FAIRness assessment, should be designed
and disseminated in a manner that positively incentivizes the providers of digital
resources; i.e., they should view the process as being fair and unbiased, and moreover,
should benefit from these assessments and use them as an opportunity to identify areas
Governance over the metrics, and the mechanisms for assessing them, will be required
to enable their careful evolution and address valid disagreements.
Here we report on the framework we have developed, which encompasses the first iteration
of a core set of FAIRness indicators that can be objectively measured by a semi-automated
process, and a template that can be followed within individual scholarly domains to
derive community-specific metrics evaluating FAIR aspects important to them.
From the outset, the group decided that it would focus on FAIRness for machines –
i.e., the degree to which a digital resource is findable, accessible, interoperable,
and reusable without human intervention. This was because FAIRness for people would
be difficult to measure objectively, as it would often depend on the experience and
prior-knowledge of the individual attempting to find and access the data. We further
agreed on the qualities that a FAIR metric should exhibit. A good metric should be:
Clear: anyone can understand the purpose of the metric
Realistic: it should not be unduly complicated for a resource to comply with the metric
Discriminating: the metric should measure something important for FAIRness; distinguish
the degree to which that resource meets that objective; and be able to provide instruction
as to what would maximize that value
Measurable: the assessment can be made in an objective, quantitative, machine-interpretable,
scalable and reproducible manner, ensuring transparency of what is being measured,
Universal: The metric should be applicable to all digital resources.
The goal of this working group was to derive at least one metric for each of the FAIR
sub-principles that would be universally applicable to all digital resources in all
scholarly domains. We recognized, however, that what is considered FAIR in one community
may be quite different from the FAIRness requirements or expectations in another community
– different community norms, standards, and practices make this a certainty. As such,
our approach took into account that the metrics we derived would eventually be supplemented
by individual community members through the creation of domain-specific or community-specific
metrics. With this in mind, we developed (and utilized) a template for the creation
of metrics (Table 1), that we suggest should be followed by communities who engage
in this process.
The outcome of this process was 14 exemplar universal metrics covering each of the
FAIR sub-principles (the short names of the metrics are in brackets in the following
description). The metrics request a variety of evidence from the community, some of
which may require specific new actions. For instance, digital resource providers must
provide a publicly accessible document(s) that provides machine-readable metadata
(FM-F2, FM-F3) and details their plans with respect to identifier management (FM-F1B),
metadata longevity (FM-A2), and any additional authorization procedures (FM-A1.2).
They must ensure the public registration of their identifier schemes (FM-F1A), (secure)
access protocols (FM-A1.1), knowledge representation languages (FM-I1), licenses (FM-R1.1),
provenance specifications (FM-R1.2). Evidence of ability to find the digital resource
in search results (FM-F4), linking to other resources (FM-I3), FAIRness of linked
resources (FM-I2), and meeting community standards (FM-R1.3) must also be provided.
The current metrics are available for public discussion at the FAIR Metrics GitHub,
with suggestions and comments being made through the GitHub comment submission system
(https://github.com/FAIRMetrics). They are free to use for any purpose under the CC0
license. Versioned releases will be made to Zenodo as the metrics evolve, with the
first release already available for download
We performed an evaluation of these preliminary metrics by inviting a variety of resources
to participate in a self-evaluation, where each metric was represented by one or more
questions. Nine individuals/organizations responded to the questionnaire, where we
emphasized that the objective was not to evaluate their resource, but rather, to evaluate
the legitimacy, clarity, and utility of the metrics themselves. This process made
it clear that certain metrics (and in some cases, the FAIR Principle underlying it)
were not always well-understood. The questionnaire, responses, and evaluation are
available in the Zenodo deposit
, and a discussion around the responses, what constitutes a "good" answer, and how
to quantitatively evaluate an answer, is ongoing, and open to the public on GitHub.
Finally, we envision a framework for the automated evaluation of metrics, leveraging
on a core set of existing work and resources that will progressively become part of
an open ecosystem of FAIR-enabled (and enabling) tools. Each metric will be self-describing
and programmatically executable using the smartAPI
specification, an initiative that extends on the OpenApi specification with semantic
will provide source information on metadata, identifier schemas and other standards,
which are core elements to many metrics. A “FAIR Accessor”
will be used to publish groups of metrics together with metadata describing, for example,
the community to which this set of metrics should be applied, the author of the metrics
set, and so on. An application will discover an appropriate suite of metrics, gather
the information required by each metric’s smartAPI (through an automated mechanism
or through a questionnaire), and then execute the metric evaluation. The output will
be an overall score of FAIRness, a detailed explanation of how the score was derived
(inputs/outputs for each metric) and some indication of how the score could be improved.
Anyone may run the metrics evaluation tool in order to, for example, guide their own
FAIR publication strategies; however, we anticipate that community stakeholder organizations
and other agencies may also desire to run the evaluation over critical resources within
their communities, and openly publish the results. For example, FAIRsharing will also
be one of the repositories that will store, and make publicly available, FAIRness
grade assessments for digital resources evaluated by our framework, using the core
set of metrics.
Measurements of FAIRness are, in our opinion, tangential to other kinds of metrics,
such as measurements of openness
or measurements of reuse or citation. While we appreciate the added value that open
data provides, we have made it clear that openness is not a requirement of FAIRness
, since there are data that cannot be made public due to privacy or confidentiality
reasons. Nevertheless, these data can reach a high level of FAIRness by, for example,
providing public metadata describing the nature of the data source, and by providing
a clear path by which data access can be requested. With respect to reuse and citation,
we believe that increasing the FAIRness of digital resources maximizes their reuse,
and that the availability of an assessment provides feedback to content creators about
the degree to which they enable others to find, access, interoperate-between and reuse
their resources. We note, however, that the FAIR-compliance of a resource is distinct
from its impact. Digital resources are not all of equal quality or utility, and the
size and scope of their audience will vary. Nevertheless, all resources should be
maximally discoverable and reusable as per the FAIR principles. While this will aid
in comparisons between them, and assessment of their quality or utility, we emphasize
that metrics that assess the popularity of a digital resource are not measuring its
FAIRness. With this in-mind, and with a template mechanism in-place to aid in the
design of new metrics, we now open the process of metrics creation for community participation.
All interested stakeholders are invited to comment and/or contribute via the FAIR
Metrics GitHub site.
How to cite this article: Wilkinson, M. D. et al. A design framework and exemplar
metrics for FAIRness. Sci. Data 5:180118 doi: 10.1038/sdata.2018.118 (2018).
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