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Challenge 8: Oceans of Big Data and Artificial Intelligence

AuthorsLogares, Ramiro CSIC ORCID ; Alós, Josep CSIC ORCID; Catalán, Ignacio Alberto CSIC ORCID ; Crespo Solana, Ana CSIC ORCID ; Campo, Javier del CSIC ORCID; Ercilla, Gemma CSIC ORCID ; Fablet, Ronan; Fernández-Guerra, Antonio; Galí, Martí CSIC ORCID; Gasol, Josep M. CSIC ORCID ; González, Ángel F. CSIC ORCID; Hernández-García, Emilio CSIC ORCID ; López, Cristóbal CSIC ORCID ; Massana, Ramon CSIC ORCID ; Montiel Fontanet, Lidia ; Palmer, Miquel CSIC ORCID; Pascual, Santiago CSIC ORCID; Pascual, Ananda CSIC ORCID; Pérez, Fernando CSIC ORCID; Pintado, José CSIC ORCID; Portabella, Marcos CSIC ORCID ; Ramasco, José J. CSIC ORCID ; Richter, Daniel J.; Sallarès, Valentí CSIC ORCID ; Sánchez, Pablo CSIC ORCID ; Sanllehi, Javier; Turiel, Antonio CSIC ORCID ; Villaseñor, Antonio CSIC ORCID CVN
Issue Date2020
PublisherConsejo Superior de Investigaciones Científicas (España)
CitationLibro Blanco CSIC 13: capítulo 8 (2020)
AbstractOceans are no longer inaccessible places for data acquisition. High-throughput technological advances applied to marine sciences (from genes to global current patterns) are generating Big Data sets at unprecedented rates. How to manage, store, analyse, useand transform this data deluge into knowledge is now a fundamental challenge for ocean sciences. Artificial Intelligence and Machine Learning are the most promising and exciting approaches addressing this challenge. These technologies are directly applicable to many data analysis problems and major challenges in the study of the ocean microbiome, ocean observation and forecasting, animal biology, ecology and conservation, resource management, and marine geosciences. We are only at the beginning of an era when machines are able to solve complex tasks that, until today, have required human expertise. We envision that the combination of ocean Big Data and Artificial Intelligence will provide the means for ground-breaking advances in our understanding of ocean functioning.
DescriptionChapter 8.
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