=Paper=
{{Paper
|id=Vol-2969/paper9-ECS
|storemode=property
|title= A Goal-Based Method to Support the Process of Making Data FAIR:
From Planning to Conceptual Modelling
|pdfUrl=https://ceur-ws.org/Vol-2969/paper9-ECS.pdf
|volume=Vol-2969
|authors=César Henrique Bernabé
|dblpUrl=https://dblp.org/rec/conf/jowo/Bernabe21
}}
== A Goal-Based Method to Support the Process of Making Data FAIR:
From Planning to Conceptual Modelling==
https://ceur-ws.org/Vol-2969/paper9-ECS.pdf
A Goal-Based Method to Support the Process of
Making Data FAIR: From Planning to Conceptual
Modelling
César Henrique Bernabé1
1
Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
Abstract
The FAIR principles [1] provide guidance for improving the Findability, Accessibility, Interoper-
ability and Reusability of data and metadata. Workflows for the process of making data FAIR
(‘FAIRification’) [2] describe how the principles can be realized through a set of steps, such as
identification of objectives, (meta)data conceptual modelling and validation.
As a multidisciplinary activity, FAIRification relies on clear understanding among the different
expertise involved. However, FAIRification workflows currently do not specify approaches
to meet this need. With this in mind, we are designing a method that uses ‘goal-oriented
modelling’ to support FAIRification, based on the assumption that goals facilitate planning and
communication [3]. Goal-oriented models focus on the use of goals as descriptions of desired
situations, which are realized by tasks (which can use resources), qualified by qualities, and
assigned to stakeholders.
Our method adapts existing goal-based modelling approaches (e.g., iStar, KAOS) to support
two FAIRification steps. First, in the ‘identify FAIRification objective’ step, the motivation(s) for
the need for FAIR data are identified. Here, goal languages will be used to create a set of FAIRi-
fication goal models. Secondly, in the ‘semantic modelling of (meta)data’ step, (meta)concepts
about data are defined with existing terms from widely used ontologies and standards. Here, we
propose that three sub-activities are performed: (i) identification of scope; (ii) goal modelling;
and (iii) conceptual modelling. In (i), domain concepts are extracted from the FAIRification goal
models. In (ii), the concepts serve as a basis for domain goal modelling, producing a second
set of goal models. In (iii), the new goal models are used to guide the (meta)data conceptual
modelling. The difference between the two sets of goal models is the context: while the first set
focuses on FAIRification objectives and aid the process planning, the second explains why the
concepts are needed in the data domain.
For the ‘conceptual modelling’ activity, the method describes best practices and procedures for
conceptual modelling, which are adapted from “Goal-Based Ontology Engineering” methods [4,
FOIS 2021 Early Career Symposium (ECS), held at FOIS 2021 - 12th International Conference on Formal Ontology in
Information Systems, September 13-17, 2021, Bolzano, Italy
" c.h.bernabe@lumc.nl (C. H. Bernabé)
0000-0003-1795-5930 (C. H. Bernabé)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
5]. Guidelines are based on the use of goal models to support the grounding of domain elements
into foundational ontologies concepts.
In essence, it is expected that the goal-based approach will improve the quality of FAIRification,
based on the clearer and easier communication of constraints and intentions among everyone
involved in the project. We expect a positive effect on the products of FAIRification and the
efficiency and reproducibility of the process. The approach can enhance the interoperability of
FAIRified data, based on the conceptual modelling good practices that will follow it. We are
currently studying what solutions can compose the approach, considering also the feasibility
of similar paradigms (e.g., value modelling [6]), as we need to find balance between added
functionality and complexity. As a next step, we will finalize the design of the method and run
a set of proofs-of-concept to validate and adjust it.
Acknowledgments
I thank Annika Jacobsen, Barend Mons, Luiz Olavo Bonino da Silva Santos, Marco Roos, Núria
Queralt Rosinach, Rajaram Kaliyaperumal, the EJP RD FAIRification stewards team, and Vitor E.
Silva Souza for their contributions to this research. This initiative has received funding from the
European Union’s Horizon 2020 research and innovation programme under grant agreement
N°825575 (The European Joint Programme on Rare Diseases).
References
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N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, et al., The fair guiding
principles for scientific data management and stewardship, Scientific data 3 (2016) 1–9.
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