| one publication added to basket [367487] | A framework for FAIR robotic datasets
Motta, C.; Aracri, S.; Ferretti, R.; Bibuli, M.; Bruzzone, G.; Caccia, M.; Odetti, A.; Ferreira, F.; de Pascalis, F. (2023). A framework for FAIR robotic datasets. Scientific Data 10(1): 620. https://dx.doi.org/10.1038/s41597-023-02495-3
In: Scientific Data. Nature Publishing Group: London. ISSN 2052-4463; e-ISSN 2052-4463, more
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| Authors | | Top |
- Motta, C.
- Aracri, S.
- Ferretti, R.
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- Bibuli, M.
- Bruzzone, G.
- Caccia, M.
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- Odetti, A.
- Ferreira, F.
- de Pascalis, F.
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| Abstract |
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. |
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