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https://ceur-ws.org/Vol-2277/abstract02.pdf
FAIR Data, FAIR Services and the FAIR Data Action Plan
© Simon Hodson
ICSU Committee on Data for Science and Technology (CODATA),
Paris, France
simon@codata.org
Due to the work of initiatives such as CODATA, were received.
Research Data Alliance (RDA), those who resulted in the The report is structured in seven sections reporting on
FAIR principles and some others we can observe a trend the urgency of making data FAIR, the need to change
towards increased agreements on how we need to change culture with respect to data, the implications of the
our data practices to improve data sharing and reuse and principles for establishing a FAIR compliant ecosystem,
make data-intensive work much more efficient. In 2014, the urgency for skills development and capacity building,
the FAIR principles were published in the realm of the need for instruments to measure the change towards
FORCE11 [1]. They summarise a longer discussion in a FAIRness in community practices and the implications
perfect way and are being accepted globally as guidelines for funding programs. For all these sections
in science. Important to note is that they not only discuss recommendations and actions have been extracted. From
human usage of data & metadata, but in particular the total 34 recommendations 14 have been indicated as
address the need to make data & metadata ready for primary ones and major stakeholder groups were
machine usage. According to the F-A-I-R Principles data associated with them.
needs to be
1. A broader definition of the FAIR principles is
• Findable (data & metadata need to have a globally given and their relevance is being stressed. Data
unique persistent identifier, need to be described with should be FAIR even if it is not open.
rich metadata, should be searchable via portals, and 2. It is stressed data should be as open as possible
the identifier needs to be findable in the metadata), and as closed as necessary. This holds in
• Accessible (data & metadata needs to be retrievable particular for data that is being created as part of
by their identifier using standard communication publicly funded research.
protocols, protocols need to be free, open and support 3. FAIR Objects as being defined in RDA [3] are
authentication and authorisation, and metadata needs introduced as a way to organise data compliant
to be accessible even when the data is not available with the FAIR principles where the use of
anymore), persistent identifiers (PIDs) plays a crucial role.
• Interoperable (data & metadata needs to be encoded 4. A number of components are identified to
using agreed representation standards, make use of implement a FAIR compliant ecosystem such as
FAIR compliant vocabularies and include relevant repositories, registries, identifier resolution
references), systems, standards for structures and semantics,
• Re-Usable (data & metadata are associated with policies and data management plans.
5. It is stressed that there is a need of sufficient and
relevant attributes, are released with clearly defined
sustainable funding to maintain all these
usage licenses, are associated with their provenance
components.
and meet community standards)
6. Funding of services should be tied to FAIR
There is now a broad agreement about these metrics and depend on impact and community
principles and a number of implicit implications such as adoption.
data should be as open as possible and be preserved 7. Further support should be given to research
where necessary for future generations. Based on the communities to continue the development and
broad support in science the European Commission and maintenance of their disciplinary interoperability
the member states have given the FAIR principles a frameworks including principles and policies for
central role for their plans to establish the European Open data management and sharing, data
Science Cloud - a stepwise evolving eco-system of formats/structures, semantics, tools etc.
research and data infrastructures. The EC established an 8. The need to open ways for cross-disciplinary
expert group to develop plans for "Turning FAIR data FAIRness by developing and adopting common
into reality". This expert group published recently its standards where possible is stressed to enable
interim report [2] for open discussion by the community interdisciplinary research. It is important that
and is currently including the many comments which these are being developed in a international
context.
9. The development and implementation of robust
Proceedings of the XX International Conference FAIR metrics is important to assess progress in
“Data Analytics and Management in Data Intensive the FAIRification of data in the research
Domains” (DAMDID/RCDL’2018), Moscow, Russia,
October 9-12, 2018
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communities. [4] CoreTrustSeal Data Repository.
10. An utterly important role is assigned to https://www.coretrustseal.org/
trustworthy repositories since they need to
support access to and reuse of the data. The tasks
range from managing the stored bit sequences up
to the stewardship of structures and embedded
semantics. Repositories are motivated to
participate in regular quality assessments
according to standards such as CoreTrustSeal [4]
which is already applied worldwide by many
institutions.
11. A special concern is the assessment of the
FAIRness of services in addition to data. New
certification standards have to be developed based
on existing models.
12. Data Management Plans should be made and
regularly updated by all funded projects that
include data. They help to make arrangements
with the required services providers such as
repositories, to plan sufficient resources for data
management and stewardship and developing
plans how data can be made FAIR compliant.
13. Measurements are required to develop two
cohorts of professionals to support FAIR data.
Data scientists who have insights in the intended
scientific work and data stewards who have deep
knowledge to ensure proper management and
curation of data.
14. Finally it is requested that there is more
recognition of the professions of data stewards
and that the efforts in creating FAIR data are
rewarded.
Funders in Europe seem to be willing to intensively
discuss these recommendations and to anchor them in
their funding programs. The publication of the FAIR
principles and the broad support they receive can thus be
seen as a milestone to improve data sharing and re-use
across disciplinary and regional borders. They will help
to reduce the huge inefficiencies and thus costs that can
be found in data-driven projects and that are estimated at
around 80% of waste of time due to what is called the
data wrangling.
References
[1] The FAIR Data Principles.
https://www.force11.org/group/fairgroup/fairpri
nciples
[2] Hodson, S., Jones, S., Collins, S., Genova, F.,
Harrower, N., Laaksonen, L., Mietchen, D.,
Petrauskaité, R., Wittenburg, P. Turning FAIR
data into reality: interim report from the
European Commission Expert Group on FAIR
data. 2018.
https://zenodo.org/record/1285272#.W5y7uPm
YTb0
[3] Berg-Cross, G., Ritz, R., Wittenburg, P. RDA
DFT Core Terms and Model. 2018.
http://hdl.handle.net/11304/5d760a3e-991d-
11e5-9bb4-2b0aad496318
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