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A framework for FAIR robotic datasets
It is essential to publish and make available environmental data gathered by emerging robotic platforms to contribute to the Global Ocean Observing System (GOOS), supported by the United Nations - Decade of Ocean Science for Sustainable Development (2021–2030). The transparency of these unique obser...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499808/ https://www.ncbi.nlm.nih.gov/pubmed/37704657 http://dx.doi.org/10.1038/s41597-023-02495-3 |
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author | Motta, Corrado Aracri, Simona Ferretti, Roberta Bibuli, Marco Bruzzone, Gabriele Caccia, Massimo Odetti, Angelo Ferreira, Fausto de Pascalis, Francesca |
author_facet | Motta, Corrado Aracri, Simona Ferretti, Roberta Bibuli, Marco Bruzzone, Gabriele Caccia, Massimo Odetti, Angelo Ferreira, Fausto de Pascalis, Francesca |
author_sort | Motta, Corrado |
collection | PubMed |
description | It is essential to publish and make available environmental data gathered by emerging robotic platforms to contribute to the Global Ocean Observing System (GOOS), supported by the United Nations - Decade of Ocean Science for Sustainable Development (2021–2030). The transparency of these unique observational datasets needs to be supported by the corresponding robotic records. The data describing the observational platform behaviour and its performance are necessary to validate the environmental data and repeat consistently the in-situ robotic deployment. The Free and Open Source Software (FOSS), proposed in this manuscript, describes how, using the established approach in Earth Sciences, the data characterising marine robotic missions can be formatted and shared following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The manuscript is a step-by-step guide to render marine robotic telemetry FAIR and publishable. State-of-the-art protocols for metadata and data formatting are proposed, applied and integrated automatically using Jupyter Notebooks to maximise visibility and ease of use. The method outlined here aims to be a first fundamental step towards FAIR interdisciplinary observational science. |
format | Online Article Text |
id | pubmed-10499808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104998082023-09-15 A framework for FAIR robotic datasets Motta, Corrado Aracri, Simona Ferretti, Roberta Bibuli, Marco Bruzzone, Gabriele Caccia, Massimo Odetti, Angelo Ferreira, Fausto de Pascalis, Francesca Sci Data Article It is essential to publish and make available environmental data gathered by emerging robotic platforms to contribute to the Global Ocean Observing System (GOOS), supported by the United Nations - Decade of Ocean Science for Sustainable Development (2021–2030). The transparency of these unique observational datasets needs to be supported by the corresponding robotic records. The data describing the observational platform behaviour and its performance are necessary to validate the environmental data and repeat consistently the in-situ robotic deployment. The Free and Open Source Software (FOSS), proposed in this manuscript, describes how, using the established approach in Earth Sciences, the data characterising marine robotic missions can be formatted and shared following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The manuscript is a step-by-step guide to render marine robotic telemetry FAIR and publishable. State-of-the-art protocols for metadata and data formatting are proposed, applied and integrated automatically using Jupyter Notebooks to maximise visibility and ease of use. The method outlined here aims to be a first fundamental step towards FAIR interdisciplinary observational science. Nature Publishing Group UK 2023-09-13 /pmc/articles/PMC10499808/ /pubmed/37704657 http://dx.doi.org/10.1038/s41597-023-02495-3 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Motta, Corrado Aracri, Simona Ferretti, Roberta Bibuli, Marco Bruzzone, Gabriele Caccia, Massimo Odetti, Angelo Ferreira, Fausto de Pascalis, Francesca A framework for FAIR robotic datasets |
title | A framework for FAIR robotic datasets |
title_full | A framework for FAIR robotic datasets |
title_fullStr | A framework for FAIR robotic datasets |
title_full_unstemmed | A framework for FAIR robotic datasets |
title_short | A framework for FAIR robotic datasets |
title_sort | framework for fair robotic datasets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499808/ https://www.ncbi.nlm.nih.gov/pubmed/37704657 http://dx.doi.org/10.1038/s41597-023-02495-3 |
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