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SCALEUS-FD: A FAIR Data Tool for Biomedical Applications

The Semantic Web and Linked Data concepts and technologies have empowered the scientific community with solutions to take full advantage of the increasingly available distributed and heterogeneous data in distinct silos. Additionally, FAIR Data principles established guidelines for data to be Findab...

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Detalles Bibliográficos
Autores principales: Pereira, Arnaldo, Lopes, Rui Pedro, Oliveira, José Luís
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471816/
https://www.ncbi.nlm.nih.gov/pubmed/32908882
http://dx.doi.org/10.1155/2020/3041498
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author Pereira, Arnaldo
Lopes, Rui Pedro
Oliveira, José Luís
author_facet Pereira, Arnaldo
Lopes, Rui Pedro
Oliveira, José Luís
author_sort Pereira, Arnaldo
collection PubMed
description The Semantic Web and Linked Data concepts and technologies have empowered the scientific community with solutions to take full advantage of the increasingly available distributed and heterogeneous data in distinct silos. Additionally, FAIR Data principles established guidelines for data to be Findable, Accessible, Interoperable, and Reusable, and they are gaining traction in data stewardship. However, to explore their full potential, we must be able to transform legacy solutions smoothly into the FAIR Data ecosystem. In this paper, we introduce SCALEUS-FD, a FAIR Data extension of a legacy semantic web tool successfully used for data integration and semantic annotation and enrichment. The core functionalities of the solution follow the Semantic Web and Linked Data principles, offering a FAIR REST API for machine-to-machine operations. We applied a set of metrics to evaluate its “FAIRness” and created an application scenario in the rare diseases domain.
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spelling pubmed-74718162020-09-08 SCALEUS-FD: A FAIR Data Tool for Biomedical Applications Pereira, Arnaldo Lopes, Rui Pedro Oliveira, José Luís Biomed Res Int Research Article The Semantic Web and Linked Data concepts and technologies have empowered the scientific community with solutions to take full advantage of the increasingly available distributed and heterogeneous data in distinct silos. Additionally, FAIR Data principles established guidelines for data to be Findable, Accessible, Interoperable, and Reusable, and they are gaining traction in data stewardship. However, to explore their full potential, we must be able to transform legacy solutions smoothly into the FAIR Data ecosystem. In this paper, we introduce SCALEUS-FD, a FAIR Data extension of a legacy semantic web tool successfully used for data integration and semantic annotation and enrichment. The core functionalities of the solution follow the Semantic Web and Linked Data principles, offering a FAIR REST API for machine-to-machine operations. We applied a set of metrics to evaluate its “FAIRness” and created an application scenario in the rare diseases domain. Hindawi 2020-08-26 /pmc/articles/PMC7471816/ /pubmed/32908882 http://dx.doi.org/10.1155/2020/3041498 Text en Copyright © 2020 Arnaldo Pereira et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Pereira, Arnaldo
Lopes, Rui Pedro
Oliveira, José Luís
SCALEUS-FD: A FAIR Data Tool for Biomedical Applications
title SCALEUS-FD: A FAIR Data Tool for Biomedical Applications
title_full SCALEUS-FD: A FAIR Data Tool for Biomedical Applications
title_fullStr SCALEUS-FD: A FAIR Data Tool for Biomedical Applications
title_full_unstemmed SCALEUS-FD: A FAIR Data Tool for Biomedical Applications
title_short SCALEUS-FD: A FAIR Data Tool for Biomedical Applications
title_sort scaleus-fd: a fair data tool for biomedical applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471816/
https://www.ncbi.nlm.nih.gov/pubmed/32908882
http://dx.doi.org/10.1155/2020/3041498
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