Ocean FAIR Data Services

Tanhua, Toste; Pouliquen, Sylvie; Hausman, Jessica; O’Brien, Kevin; Bricher, P.; Bruin, T. De; Buck, Justin; Burger, E. F.; Carval, Thierry; Casey, Kenneth S.; Diggs, Stephen; Giorgetti, Alessandra; Glaves, Helen; Harscoat, Valérie; Kinkade, Danie; Muelbert, Jose H; Novellino, Antonio; Pfeil, Benjamin; Pulsifer, Peter; Putte, Anton P. Van de; Robinson, Erin; Schaap, Dick; Smirnov, Alexander; Smith, Neville; Snowden, Derrick; Spears, Tobias; Stall, Shelley; Tacoma, Marten; Thijsse, Peter; Tronstad, Stein; Vandenberghe, Thomas; Wengren, Micah; Wyborn, Lesley; Zhao, Zhiming

doi:10.3389/fmars.2019.00440

Cited by 124 publications

(88 citation statements)

References 27 publications

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“…FAIR provides a high-level conceptual framework useful to the design of contemporary information systems in support of ocean observation, the more detailed technical underpinnings of which are based on widely applied Earth science data interoperability standards that we now describe. While the focus of this paper is on data interoperability, it is difficult to decouple Interoperability from the other elements of FAIRness when describing the GOOS Data System current and future states (Tanhua et al, 2019).…”

Section: Fair Principlesmentioning

confidence: 99%

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Data Interoperability Between Elements of the Global Ocean Observing System

et al. 2019

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The data management landscape associated with the Global Ocean Observing System is distributed, complex, and only loosely coordinated. Yet interoperability across this distributed landscape is essential to enable data to be reused, preserved, and integrated and to minimize costs in the process. A building block for a distributed system in which component systems can exchange and understand information is standardization of data formats, distribution protocols, and metadata. By reviewing several data management use cases we attempt to characterize the current state of ocean data interoperability and make suggestions for continued evolution of the interoperability standards underpinning the data system. We reaffirm the technical data standard recommendations from previous OceanObs conferences and suggest incremental improvements to them that can help the GOOS data system address the significant challenges that remain in order to develop a truly multidisciplinary data system.

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Section: Fair Principlesmentioning

confidence: 99%

“…In the decade since OceanObs '09, CF/netCDF has been further cemented as the de facto standard for file based storage and exchange of in situ, remotely sensed, and model generated data. Further progress since OceanObs '09 is nicely summarized in Tanhua et al (2019).…”

Section: Common Elements Of Data Interoperabilitymentioning

confidence: 99%

Data Interoperability Between Elements of the Global Ocean Observing System

et al. 2019

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“…For a modeler interested in constructing a holistic view of the ecosystem during a particular field effort, they have to invest significant effort to find and access to relevant data in all the different repositories. Linking between them could significantly facilitate more complete exploitation of the data (Benway et al, 2019;Tanhua et al, 2019b). Indeed, OBIS has recently adopted the Event Core format of Darwin Core and developed the Extended Measurement or Fact Extension, enabling linking sampling facts including environmental measurements to an event hierarchy and biotic measurements (e.g., biomass, absence/presence, fatty acids, pigments) to the occurrence records (De Pooter et al, 2017).…”

Section: Managing and Integrating The Massive Data Flow Originating Fmentioning

confidence: 99%

“…We suggest that the following itemized strategies should be used to prioritize investments and provide below each some example activities (some may contribute to more than one priority): Promote free data and information sharing by using open access publication strategies of both articles and source data and following a "FAIR" principle (Wilkinson et al, 2016). Build robust distributed networks for collection, distribution and curation of data (like Argo and EcoTaxa) that do not depend on one country's funding and that serve the full scientific community worldwide (see Tanhua et al, 2019b). Channel the data to global and consistent public databases such as OBIS or other existing platforms in consultation with modelers.…”

Section: Recommendations On How To Proceed During the Next Ten Yearsmentioning

confidence: 99%

Globally Consistent Quantitative Observations of Planktonic Ecosystems

et al. 2019

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In this paper we review the technologies available to make globally quantitative observations of particles in general-and plankton in particular-in the world oceans, and for sizes varying from sub-microns to centimeters. Some of these technologies have been available for years while others have only recently emerged. Use of these technologies is critical to improve understanding of the processes that control abundances, distributions and composition of plankton, provide data necessary to constrain and improve ecosystem and biogeochemical models, and forecast changes in marine ecosystems in light of climate change. In this paper we begin by providing the motivation for plankton observations, quantification and diversity qualification on a global scale. We then expand on the state-of-the-art, detailing a variety of relevant and (mostly) mature technologies and measurements, including bulk measurements of plankton, pigment composition, uses of genomic, optical and acoustical methods as well

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“…The FAIR (Findable, Accessible, Interoperable, and Re-usable) principles must guide the nutrient data management system (Tanhua et al, 2019). Quality control processes and standards are critical for producing precise and accurate data .…”

Section: Quality Control Of the Data Setmentioning

confidence: 99%