Big Data in Marine Science

Lionel Guidi (Contributor), Antonio Fernandez Guerra (Contributor), Carlos Canchaya, Edward Curry, Federica Foglini, Jean Olivier Irisson, Ketil Malde, C. Tara Marshall, Matthias Obst, Rita P. Ribeiro, Jerry Tjiputra, Sheila JJ Heymans (Editor), Britt Alexander (Editor), Ángel Muñiz Piniella (Editor), Paula Kellett (Editor), Joke Coopman (Editor)

Research output: Book/ReportCommissioned Report


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, as introduced in Chapter 1. Expansions in the
scope and scale of ocean observations, as well as automated sampling and ‘smart sensors’, 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 and disciplinary analyses, which can be based on artificial intelligence. The marine science community has not yet reached the big data revolution and the ‘data deluge’ introduces a unique set of challenges that are new to many marine scientists. This document identifies bottlenecks and opportunities related to data acquisition, data handling and management, computing infrastructures and interoperability, data sharing, big data analytics, data validation, and training and collaboration. Specific challenges should be overcome to ensure the
maximum value of marine big data can be reaped. We present topics and case studies of some recent advances in the application of big data to support marine science that demonstrate these challenges and recommendations. Chapter 2
covers climate science and marine biogeochemistry, with particular focus on European and global initiatives to integrate carbon and other biogeochemical data that are used to inform global climate negotiations. Chapter 3 discusses how big data could be used to create high-resolution, multidisciplinary habitat maps for planning new marine protected areas. Chapter 4 looks at marine biological observations including genetic sequences, imagery and hydro-acoustic data and calls for a globally connected network of long-term biological observations for more complex interdisciplinary analyses using big data. Chapter 5 addresses food provision from the ocean and seas with a focus on aquaculture and the management of sea-lice outbreaks and escaped, farmed salmon using artificial intelligence.
Original languageEnglish
Place of PublicationBelgium
PublisherEuropean Marine Board
Number of pages52
ISBN (Electronic)9789492043931
Publication statusPublished - Apr 2020

Publication series

NameFuture Science Brief 6 of the European Marine Board
ISSN (Electronic)2593-5232

Bibliographical note

Dorothee Bakker thanks the EU Horizon2020 INFRADEV RINGO project (730944) for enabling her work. The many researchers and funding agencies responsible for data collection and quality control are thanked for their contributions to the Surface Ocean CO2 Atlas (SOCAT) and the Global Data Analysis Project (GLODAP). Tara Marshall thanks the Marine Alliance for Science and Technology
for Scotland (MASTS) for their funding support. Matthias Obst acknowledges supported by Nordic e-Infrastructure Collaboration (NeIC), the Swedish Research Council (LifeWatch program, grant N°. 2017-00634), the Ocean Data Factory program funded by the Swedish Innovation Agency (Grant No. 2019-02256) and the Swedish Agency for Marine and Water Management (Grant N°. 956-19).
Jerry Tjiputra thanks funding from the Research Council of Norway (275268). Edward Curry thanks funding from Science Foundation Ireland grant SFI/12/RC/2289_P2 and the European Union’s Horizon 2020 research programme Big Data Value ecosystem (BDVe) grant N° 732630.


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