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Big Data in Marine Science

Guidi, Lionel; Fernàndez-Guerra, Antonio; Bakker, Dorothee; Canchaya, Carlos; Curry, Edward; Foglini, Federica; Irission, Jean-Olivier; Malde, Ketil; Marshall, C. Tara; Obst, Matthias; Ribeiro, Rita P.; Tjiputra, Jerry


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{
  "description": "<p>This document explores the potential of big data, i.e. large volumes of high variety data collected at high velocity, to advance marine science. Marine science is rapidly entering the digital age. Expansions in the scope and scale of ocean observations, as well as automated sampling and &lsquo;smart sensors&rsquo;, are leading to a continuous flood of data. This provides opportunities to transform the way we study and understand the ocean through more complex and interdisciplinary analyses, and offers novel approaches for the management of marine resources. However, more data do not necessarily mean that we have the right data to answer many critical scientific questions and to make well-informed, data-driven management decisions on the sustainable use of ocean resources. To increase the value of the wealth of marine big data, it must be openly shared, interoperable and integrated into complex transdisciplinary analyses using artificial intelligence.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Villefrance Oceanography Laboratory (LOV), CNRS", 
      "@type": "Person", 
      "name": "Guidi, Lionel"
    }, 
    {
      "affiliation": "Lundbeck Foundation GeoGenetics Centre, University of Copenhagen", 
      "@type": "Person", 
      "name": "Fern\u00e0ndez-Guerra, Antonio"
    }, 
    {
      "affiliation": "University of East Anglia (UEA)", 
      "@type": "Person", 
      "name": "Bakker, Dorothee"
    }, 
    {
      "affiliation": "University of Vigo", 
      "@type": "Person", 
      "name": "Canchaya, Carlos"
    }, 
    {
      "affiliation": "National University of Ireland Galway (NUIG)", 
      "@type": "Person", 
      "name": "Curry, Edward"
    }, 
    {
      "affiliation": "Consiglio Nazionale delle Ricerche (CNR)", 
      "@type": "Person", 
      "name": "Foglini, Federica"
    }, 
    {
      "affiliation": "Villefrance Oceanography Laboratory (LOV), CNRS", 
      "@type": "Person", 
      "name": "Irission, Jean-Olivier"
    }, 
    {
      "affiliation": "Institute of Marine Research (IMR)", 
      "@type": "Person", 
      "name": "Malde, Ketil"
    }, 
    {
      "affiliation": "University of Aberdeen", 
      "@type": "Person", 
      "name": "Marshall, C. Tara"
    }, 
    {
      "affiliation": "University of Gothenburg", 
      "@type": "Person", 
      "name": "Obst, Matthias"
    }, 
    {
      "affiliation": "University of Porto", 
      "@type": "Person", 
      "name": "Ribeiro, Rita P."
    }, 
    {
      "affiliation": "Norwegian Research Centre (NORCE)", 
      "@type": "Person", 
      "name": "Tjiputra, Jerry"
    }
  ], 
  "headline": "Big Data in Marine Science", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-04-29", 
  "url": "https://zenodo.org/record/3755793", 
  "keywords": [
    "Big data", 
    "Marine science", 
    "Artificial intelligence", 
    "Machine learning", 
    "Aquaculture", 
    "Climate change", 
    "Marine biogeochemistry", 
    "Marine biological observations", 
    "Habitat mapping", 
    "Marine protected areas"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3755793", 
  "@id": "https://doi.org/10.5281/zenodo.3755793", 
  "@type": "ScholarlyArticle", 
  "name": "Big Data in Marine Science"
}
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