=Paper=
{{Paper
|id=Vol-2042/paper23
|storemode=property
|title=Overview of a Suite of Tools and Training Material for Implementing FAIR Data Principles
|pdfUrl=https://ceur-ws.org/Vol-2042/paper23.pdf
|volume=Vol-2042
|authors=Mark Thompson,Luiz Olavo Bonino Da Silva Santos,Kees Burger,Rajaram Kaliyaperumal,Erik Schultes,Annika Jacobsen,Claudio Carta,Richard Finkers,David van Enckevort,Mascha Jansen,Barend Mons,Marco Roos
|dblpUrl=https://dblp.org/rec/conf/swat4ls/ThompsonSBKSJCF17
}}
==Overview of a Suite of Tools and Training Material for Implementing FAIR Data Principles==
https://ceur-ws.org/Vol-2042/paper23.pdf
Overview of a suite of middle-ware services for
implementing FAIR data principles
Mark Thompson1 , Luiz Bonino6 , Mark D Wilkinson2 , Rajaram
Kaliyaperumal1 , Kees Burger6 , Shamanou van Leeuwen6 , Annika Jacobsen1 ,
Claudio Carta3 , Erik Schultes6 , David van Enckevort4 , Richard Finkers5 ,
Mascha Jansen6 , Barend Mons1 , and Marco Roos1
1
Leiden University Medical Centre, The Netherlands
{m.roos,m.thompson,r.kaliyaperumal,a.jacobsen}@lumc.nl
2
Universidad Politcnica de Madrid, Spain
markw@illuminae.com
3
Istituto Superiore di Sanitá, Italy
claudio.carta@iss.it
4
University Medical Center Groningen, The Netherlands
david.van.enckevort@umcg.nl
5
Wageningen Plant Research, The Netherlands
richard.finkers@wur.nl
6
Dutch Techcentre for Life Sciences, The Netherlands
{luiz.bonino,erik.schultes,mascha.jansen}@dtls.nl
1 Introduction
Principles of Findable, Accessible, Interoperable, and Reusable data for humans
and computers (FAIR)1 are widely endorsed by organizations such as the Euro-
pean Open Science Cloud, the life science data infrastructure ELIXIR, the NIH
via its commons program, the biobanking infrastructure consortium BBMRI-
ERIC, the G20 and the G7. Implementing a data ecosystem based on FAIR
principles requires guidelines, tools, and training, and FAIR data stewards to
help apply them. The principles as such do not recommend any particular im-
plementation: user communities will have to decide the most appropriate imple-
mentation for their domain. Here, we demonstrate the use of a suite of Semantic
Web-based middle-ware services that help communities implement FAIR data
principles2 . Aiming to facilitate adoption, the services are made to complement
existing data infrastructures, including local and centralised data resources, and
thus establish a robust, federated ecosystem of FAIR resources. The services
are also particularly suited for training data stewards. We demonstrate the ap-
plication of the services by rare disease and plant breeding communities where
the combination of Ontologies, Linked Data, and light-weight FAIR services are
being explored as the means to implement FAIR data principles.
1
Wilkinson et al. doi:10.1038/sdata.2016.18
2
https://github.com/DTL-FAIRData
2 Overview of a suite of FAIR services
2 FAIR services
We present the following middle-ware services and tools to implement FAIR
principles:
– F: a FAIR Data Search Engine based on harvesting metadata from FAIR
Data Points using widely adopted metadata structures and standards (see
“A” below). The FDP web interface also exposes (bio)schema.org-compatible
metadata for use by third-party search engines.
– A: the FAIR Data Point RESTful API that uses the Data Catalogue Vo-
cabulary (DCAT3 ) and Datacites Registry of Research Data Repositories
(RE3Data4 ) to provide high level metadata descriptors about data deposits,
and to provide instructions to access various distributions of data sets (such
as both an original CSV file and its fully interoperable RDF representation).
– I: a FAIRifier tool that is based on OpenRefine and its RDF plug-in and used
to convert tabular data into ontology-grounded RDF; this tool is actively
used for events such as the bring your own data workshops (BYODs) , and
other FAIR data training courses.
– R: for reusability we provide the FAIR Metadata Editor to support richer
(meta)data to optimise future reuse of FAIR data sources, and early pro-
totypes that apply machine readable metadata to govern access, such as
license, consent, and privacy preservation.
3 Conclusion
In the context of our contribution to cross-national infrastructure for data stew-
ardship in communities such as the rare disease community and the plant breed-
ing community, we consider that tools described above, and their implementation
based on Semantic Web technologies to help the adoption of the FAIR approach.
The services we present are light-weight and still allow communities sufficient
freedom to make design decisions together with FAIR data stewards. Training
data stewards is therefore an important objective for further adoption and we
found the service suite valuable for training events and BYODs. We also work
with software service providers in user communities. Turning for instance pa-
tient registry software and biobank cataloguing software such as MOLGENIS
into FAIR data generating tools will substantially lower the burden of FAIRifi-
cation.
Acknowledgments. We acknowledge the participants of the BYODs, FAIR
workshops and hackathons for their kind contributions and feedback, and FAIR-
dICT, ODEX4All, ELIXIR and ELIXIR-EXCELERATE, BBMRI-ERIC and
BBMRI-NL, RD-Connect, Istituto Superiore di Sanitá in Italy, the Dutch Tech-
centre for Life Science (representative of ELIXIR-NL), Ministerio de Economa
y Competitividad (Spain) grant number TIN2014-55993-RM, and the NBDC/
DBCLS BioHackathon series.
3
https://www.w3.org/TR/vocab-dcat
4
http://www.re3data.org/