@misc{garabedian_a_framework_2023, 
author={Garabedian, N.,Bagov, I.,Flachmann, M.,Ye, N.,Meller, M.,Bresser, F.,Greiner, C.},
title={A Framework to Generate; Store; and Publish FAIR Data in Experimental Sciences},
year={2023},
howpublished = {conference proceedings},
doi = {https://doi.org/10.5445/IR/1000165254},
abstract = {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 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.},
note = {Online available at: \url{https://doi.org/10.5445/IR/1000165254} (DOI). Garabedian, N.; Bagov, I.; Flachmann, M.; Ye, N.; Meller, M.; Bresser, F.; Greiner, C.: A Framework to Generate; Store; and Publish FAIR Data in Experimental Sciences. CEUR-WS. 2023. DOI: 10.5445/IR/1000165254}}