A Framework to Generate, Store, and Publish FAIR Data in Experimental Sciences

Purpose: FAIR data is a relatively new paradigm in research data management which aims to facilitate
reproducibility of research, knowledge generation, and knowledge retention in all scientific domains.
This paper presents a framework which enables the semantic generation, storage, and publication of
FAIR datasets in the field of experimental materials science with the help of controlled vocabularies.
Methodology: The framework presented in this work consists of multiple software tools developed by
the authors, as well as an external electronic lab notebook (ELN), which is used as a database. The
centerpiece of this solution is VocPopuli, a tool for the collaborative development of FAIR SKOS-based
controlled vocabularies. These vocabularies are used as the basis of further software components
developed by the authors, which enable the entry, processing, and publishing of FAIR datasets.
Findings: This paper shows that SKOS-based controlled vocabularies can be used as the cornerstone of
FAIR data management systems in experimental materials science, and, in research and development
as a whole. Furthermore, it demonstrates how these vocabularies can be part of common laboratory
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workflows in a seamless fashion which simplifies the generation, storage, and publication of FAIR data.
Value: The solution presented in this work enables the simplified creation of FAIR data without any
additional effort from lab scientists, as most of the infrastructure is set up by the data stewards and the
rest of the community. The controlled vocabularies, which are used to define the schemas of the
generated datasets, facilitate the linking of external semantic resources, and increase the
reproducibility of the research results. Furthermore, using our framework, these datasets can easily be
published to open science platforms, so that other researchers can also benefit.
Conclusions: Integrating FAIR metadata in the production of FAIR data is not just a technical, but also
a cultural issue. That is why, separating the creation of community and lab vocabularies, as well as, the
specific templates for data input by lab scientists turned out to be a strategy to be further developed.