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Conference Paper: MarineGEO: Big Data from a Big Ocean

TitleMarineGEO: Big Data from a Big Ocean
Authors
Issue Date2016
Citation
Big Data Research Cluster Launch, University of Hong Kong, Hong Kong, 7 December 2016 How to Cite?
AbstractBiodiversity has intrinsic value that can only be assessed by quantifying the true extent of biodiversity itself in addition to the way all species contribute to ecosystem function. These tasks are complex because (a) traditional methods of marine environmental monitoring are largely in-situ visual surveys that are time-consuming, focusing on conspicuous macrofauna (>2mm) and (b) many organisms in the ocean are unknown, unnamed and invisible to the naked eye (<2mm, ‘cryptic’). This represents a major knowledge gap given that more than 500,000 marine species (>60%) are cryptic and unknown to science (coml.org). MarineGEO uses a novel investigative approach that combines standardized census methods with latest molecular technologies to quantify the ocean biodiversity. Specifically, metagenomics and metatranscriptomics when combined with standardized samplers, constitutes a highly efficient new route to quantify biodiversity (species richness) and connect it to ecosystem processes (e.g. production, carbon storage, metabolic pathways, pathogenicity, antibiotic resistance, biogeochemical cycling etc.). The major outcomes that can be expected from this study are as follows: 1) A multifaceted biodiversity database – physical specimens, images, and genetic material; 2) Community changes in coastal waters mapped spatially and seasonally; 3) Long term measurements added to MarineGEO’s global and local data repository.
DescriptionOrganised by HKU Big Data Research Cluster, Faculty of Science, the University of Hong Kong
Persistent Identifierhttp://hdl.handle.net/10722/252441

 

DC FieldValueLanguage
dc.contributor.authorMc Sharry Mc Ilroy, SE-
dc.contributor.authorBaker, DM-
dc.contributor.authorHui, J-
dc.contributor.authorPanagiotou, I-
dc.contributor.authorRussell, BD-
dc.date.accessioned2018-04-23T07:04:27Z-
dc.date.available2018-04-23T07:04:27Z-
dc.date.issued2016-
dc.identifier.citationBig Data Research Cluster Launch, University of Hong Kong, Hong Kong, 7 December 2016-
dc.identifier.urihttp://hdl.handle.net/10722/252441-
dc.descriptionOrganised by HKU Big Data Research Cluster, Faculty of Science, the University of Hong Kong-
dc.description.abstractBiodiversity has intrinsic value that can only be assessed by quantifying the true extent of biodiversity itself in addition to the way all species contribute to ecosystem function. These tasks are complex because (a) traditional methods of marine environmental monitoring are largely in-situ visual surveys that are time-consuming, focusing on conspicuous macrofauna (>2mm) and (b) many organisms in the ocean are unknown, unnamed and invisible to the naked eye (<2mm, ‘cryptic’). This represents a major knowledge gap given that more than 500,000 marine species (>60%) are cryptic and unknown to science (coml.org). MarineGEO uses a novel investigative approach that combines standardized census methods with latest molecular technologies to quantify the ocean biodiversity. Specifically, metagenomics and metatranscriptomics when combined with standardized samplers, constitutes a highly efficient new route to quantify biodiversity (species richness) and connect it to ecosystem processes (e.g. production, carbon storage, metabolic pathways, pathogenicity, antibiotic resistance, biogeochemical cycling etc.). The major outcomes that can be expected from this study are as follows: 1) A multifaceted biodiversity database – physical specimens, images, and genetic material; 2) Community changes in coastal waters mapped spatially and seasonally; 3) Long term measurements added to MarineGEO’s global and local data repository.-
dc.languageeng-
dc.relation.ispartofBig Data Research Cluster Launch, University of Hong Kong, Hong Kong-
dc.titleMarineGEO: Big Data from a Big Ocean-
dc.typeConference_Paper-
dc.identifier.emailMc Sharry Mc Ilroy, SE: smcilroy@hku.hk-
dc.identifier.emailBaker, DM: dmbaker@hku.hk-
dc.identifier.emailPanagiotou, I: gipa@hku.hk-
dc.identifier.emailRussell, BD: brussell@hku.hk-
dc.identifier.authorityBaker, DM=rp01712-
dc.identifier.authorityPanagiotou, I=rp01725-
dc.identifier.authorityRussell, BD=rp02053-
dc.identifier.hkuros282506-

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