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FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network

The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a...

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Autores principales: Touré, Vasundra, Krauss, Philip, Gnodtke, Kristin, Buchhorn, Jascha, Unni, Deepak, Horki, Petar, Raisaro, Jean Louis, Kalt, Katie, Teixeira, Daniel, Crameri, Katrin, Österle, Sabine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006404/
https://www.ncbi.nlm.nih.gov/pubmed/36899064
http://dx.doi.org/10.1038/s41597-023-02028-y
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author Touré, Vasundra
Krauss, Philip
Gnodtke, Kristin
Buchhorn, Jascha
Unni, Deepak
Horki, Petar
Raisaro, Jean Louis
Kalt, Katie
Teixeira, Daniel
Crameri, Katrin
Österle, Sabine
author_facet Touré, Vasundra
Krauss, Philip
Gnodtke, Kristin
Buchhorn, Jascha
Unni, Deepak
Horki, Petar
Raisaro, Jean Louis
Kalt, Katie
Teixeira, Daniel
Crameri, Katrin
Österle, Sabine
author_sort Touré, Vasundra
collection PubMed
description The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a common standard infrastructure with a fit-for-purpose strategy to bring together health-related data and ease the work of both data providers to supply data in a standard manner and researchers by enhancing the quality of the collected data. As a result, the SPHN Resource Description Framework (RDF) schema was implemented together with a data ecosystem that encompasses data integration, validation tools, analysis helpers, training and documentation for representing health metadata and data in a consistent manner and reaching nationwide data interoperability goals. Data providers can now efficiently deliver several types of health data in a standardised and interoperable way while a high degree of flexibility is granted for the various demands of individual research projects. Researchers in Switzerland have access to FAIR health data for further use in RDF triplestores.
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spelling pubmed-100064042023-03-12 FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network Touré, Vasundra Krauss, Philip Gnodtke, Kristin Buchhorn, Jascha Unni, Deepak Horki, Petar Raisaro, Jean Louis Kalt, Katie Teixeira, Daniel Crameri, Katrin Österle, Sabine Sci Data Article The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a common standard infrastructure with a fit-for-purpose strategy to bring together health-related data and ease the work of both data providers to supply data in a standard manner and researchers by enhancing the quality of the collected data. As a result, the SPHN Resource Description Framework (RDF) schema was implemented together with a data ecosystem that encompasses data integration, validation tools, analysis helpers, training and documentation for representing health metadata and data in a consistent manner and reaching nationwide data interoperability goals. Data providers can now efficiently deliver several types of health data in a standardised and interoperable way while a high degree of flexibility is granted for the various demands of individual research projects. Researchers in Switzerland have access to FAIR health data for further use in RDF triplestores. Nature Publishing Group UK 2023-03-10 /pmc/articles/PMC10006404/ /pubmed/36899064 http://dx.doi.org/10.1038/s41597-023-02028-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Touré, Vasundra
Krauss, Philip
Gnodtke, Kristin
Buchhorn, Jascha
Unni, Deepak
Horki, Petar
Raisaro, Jean Louis
Kalt, Katie
Teixeira, Daniel
Crameri, Katrin
Österle, Sabine
FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network
title FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network
title_full FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network
title_fullStr FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network
title_full_unstemmed FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network
title_short FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network
title_sort fairification of health-related data using semantic web technologies in the swiss personalized health network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006404/
https://www.ncbi.nlm.nih.gov/pubmed/36899064
http://dx.doi.org/10.1038/s41597-023-02028-y
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