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Supporting Cross-Domain System-Level Environmental and Earth Science ArchiMer
Vermeulen, Alex; Glaves, Helen; Pouliquen, Sylvie; Kokkinaki, Alexandra.
Answering the key challenges for society due to environmental issues like climate change, pollution and loss of biodiversity, and making the right decisions to tackle these in a cost-efficient and sustainable way requires scientific understanding of the Earth System. This scientific knowledge can then be used to inform the general public and policymakers. Scientific understanding starts with having available the right data, often in the form of observations. Research Infrastructures (RIs) exist to perform these observations in the required quality and to make the data available to first of all the researchers. In the current Big Data era, the increasing challenge is to provide the data in an interoperable and machine-readable and understandable form. The...
Tipo: Text Palavras-chave: Research Infrastructure; FAIR; Data management; Environmental and earth science; Societal challenges.
Ano: 2020 URL: https://archimer.ifremer.fr/doc/00644/75655/76528.pdf
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Streamlining Data and Service Centers for Easier Access to Data and Analytical Services: The Strategy of ODATIS as the Gateway to French Marine Data ArchiMer
Schmidt, Sabine; Maudire, Gilbert; Nys, Cecile; Sudre, Joel; Harscoat, Valerie; Dibarboure, Gérald; Huynh, Frédéric.
The past few decades have seen a marked acceleration in the amount of marine observation data derived using both in situ and remote sensing measurements. For example, high-frequency monitoring of key physical-chemical parameters has become an essential tool for assessing natural and human-induced changes in coastal waters as well as their consequences on society. The number and variety of data acquisition techniques require efficient methods of improving data availability. The challenge is to make ocean data available via interoperable portals, which facilitate data sharing according to Findable, Accessible, Interoperable, and Reusable (FAIR) principles for producers and users. Ocean DAta Information and Services (ODATIS) aims to become a unique gateway to...
Tipo: Text Palavras-chave: Ocean; Data repository; Interoperability; FAIR; Data and service center.
Ano: 2020 URL: https://archimer.ifremer.fr/doc/00667/77872/80019.pdf
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Ocean FAIR Data Services ArchiMer
Tanhua, Toste; Pouliquen, Sylvie; Hausman, Jessica; O’brien, Kevin; Bricher, Pip; De Bruin, Taco; Buck, Justin J. H.; Burger, Eugene F.; Carval, Thierry; Casey, Kenneth S.; Diggs, Steve; Giorgetti, Alessandra; Glaves, Helen; Harscoat, Valerie; Kinkade, Danie; Muelbert, Jose H.; Novellino, Antonio; Pfeil, Benjamin; Pulsifer, Peter L.; Van De Putte, Anton; 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.
Well-founded data management systems are of vital importance for ocean observing systems as they ensure that essential data are not only collected but also retained and made accessible for analysis and application by current and future users. Effective data management requires collaboration across activities including observations, metadata and data assembly, quality assurance and control (QA/QC), and data publication that enables local and interoperable discovery and access and secures archiving that guarantees long-term preservation. To achieve this, data should be findable, accessible, interoperable, and reusable (FAIR). Here, we outline how these principles apply to ocean data and illustrate them with a few examples. In recent decades, ocean data...
Tipo: Text Palavras-chave: FAIR; Ocean; Data management; Data services; Ocean observing; Standardization; Interoperability.
Ano: 2019 URL: https://archimer.ifremer.fr/doc/00509/62068/66248.pdf
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Effects of Ignoring Survey Design Information for Data Reuse ArchiMer
Foster, Scott D.; Vanhatalo, Jarno; Trenkel, Verena; Schulz, Torsti; Lawrence, Emma; Przeslawski, Rachel; Hosack, Geoffrey R..
Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: “Are aggregated databases currently providing the right information to enable effective and unbiased reuse?” We investigate this question, with a focus on designs that purposefully favour the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified. We perform a simulation experiment by creating datasets with progressively more uneven inclusion probabilities, and examine the resulting estimates of the average number of individuals per unit area...
Tipo: Text Palavras-chave: Bias; Survey Design; Database; Population Density Estimate; Model; Horvitz‐Thompson; FAIR; Reuse; Data; Inclusion Probability.
Ano: 2021 URL: https://archimer.ifremer.fr/doc/00691/80339/83422.pdf
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