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FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards
Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives pro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10065194/ https://www.ncbi.nlm.nih.gov/pubmed/37007976 http://dx.doi.org/10.3389/fgene.2023.1086802 |
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author | Reer, Aaron Wiebe, Andreas Wang, Xu Rieger, Jochem W. |
author_facet | Reer, Aaron Wiebe, Andreas Wang, Xu Rieger, Jochem W. |
author_sort | Reer, Aaron |
collection | PubMed |
description | Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives promise some relief to the problem, but only if the data are provided in a usable way. The FAIR principles state very general requirements for useful data sharing: they should be findable, accessible, interoperable, and reusable. This article will focus on two challenges to implement the FAIR framework for human neuroscience data. On the one hand, human data can fall under special legal protection. The legal frameworks regulating how and what data can be openly shared differ greatly across countries which can complicate data sharing or even discourage researchers from doing so. Moreover, openly accessible data require standardization of data and metadata organization and annotation in order to become interpretable and useful. This article briefly introduces open neuroscience initiatives that support the implementation of the FAIR principles. It then reviews legal frameworks, their consequences for accessibility of human neuroscientific data and some ethical implications. We hope this comparison of legal jurisdictions helps to elucidate that some alleged obstacles for data sharing only require an adaptation of procedures but help to protect the privacy of our most generous donors to research … our study participants. Finally, it elaborates on the problem of missing standards for metadata annotation and introduces initiatives that aim at developing tools to make neuroscientific data acquisition and analysis pipelines FAIR by design. While the paper focuses on making human neuroscience data useful for data-intensive AI the general considerations hold for other fields where large amounts of openly available human data would be helpful. |
format | Online Article Text |
id | pubmed-10065194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100651942023-04-01 FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards Reer, Aaron Wiebe, Andreas Wang, Xu Rieger, Jochem W. Front Genet Genetics Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives promise some relief to the problem, but only if the data are provided in a usable way. The FAIR principles state very general requirements for useful data sharing: they should be findable, accessible, interoperable, and reusable. This article will focus on two challenges to implement the FAIR framework for human neuroscience data. On the one hand, human data can fall under special legal protection. The legal frameworks regulating how and what data can be openly shared differ greatly across countries which can complicate data sharing or even discourage researchers from doing so. Moreover, openly accessible data require standardization of data and metadata organization and annotation in order to become interpretable and useful. This article briefly introduces open neuroscience initiatives that support the implementation of the FAIR principles. It then reviews legal frameworks, their consequences for accessibility of human neuroscientific data and some ethical implications. We hope this comparison of legal jurisdictions helps to elucidate that some alleged obstacles for data sharing only require an adaptation of procedures but help to protect the privacy of our most generous donors to research … our study participants. Finally, it elaborates on the problem of missing standards for metadata annotation and introduces initiatives that aim at developing tools to make neuroscientific data acquisition and analysis pipelines FAIR by design. While the paper focuses on making human neuroscience data useful for data-intensive AI the general considerations hold for other fields where large amounts of openly available human data would be helpful. Frontiers Media S.A. 2023-03-13 /pmc/articles/PMC10065194/ /pubmed/37007976 http://dx.doi.org/10.3389/fgene.2023.1086802 Text en Copyright © 2023 Reer, Wiebe, Wang and Rieger. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Reer, Aaron Wiebe, Andreas Wang, Xu Rieger, Jochem W. FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards |
title | FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards |
title_full | FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards |
title_fullStr | FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards |
title_full_unstemmed | FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards |
title_short | FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards |
title_sort | fair human neuroscientific data sharing to advance ai driven research and applications: legal frameworks and missing metadata standards |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10065194/ https://www.ncbi.nlm.nih.gov/pubmed/37007976 http://dx.doi.org/10.3389/fgene.2023.1086802 |
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