This dataset is composed of 1,153,507 zooplankton individuals, zooplankton parts, non-living particles and imaging artefacts, ranging from 300 µm to 3.39 mm Equivalent Spherical Diameter, individually imaged and measured with the ZooScan (Gorsky et al., 2010). The objects were sorted in 127 taxonomic and morphological groups. The imaged objects originate from samples collected on the Bay of Biscay continental shelf, in spring, from 2004 to 2016 during the PELGAS ecosystemic surveys (Doray et al., 2018). The samples were collected with a WP2 200 µm mesh size fitted with a Hydrobios (back-run stop) mechanical flowmeter, generally from 100 m depth to the surface, or 5 m above the sea floor (if bottom depth less than 100 m) in vertical hauls, at night. From 2004 to 2006, vertical WP2 net tows were performed in the anchovy core distribution area in the southern Bay of Biscay and North of it until the Loire estuary only. Since 2009, WP2 sampling has been carried out at all PELGAS stations, up to the southern coast of Brittany. The samples were preserved in 4% buffered formaldehyde seawater solution directly after collection, until 2019-2020 where they were imaged with the ZooScan, in the lab, on land. Each imaged object is geolocated, associated to a station, a cruise, a year and other metadata that enable the reconstruction of quantitative zooplankton communities for ecological studies (i.e. Grandrémy et al., 2023a). Each object is described by 46 morphological and grey level based features (8 bits encoding, 0 = black, 255 = white), including size, automatically extracted on each individual image by the Zooprocess. Each object was taxonomically identified using the web based application Ecotaxa with built-in, random forest and CNN based, semi-automatic sorting tools followed by expert validation or correction (Picheral et al., 2017). This dataset is intended to be used for ecological studies as well as machine learning applied to plankton studies. The archive contains: - One tab separated file (PELGAS ZooScan zooplankton dataset) containing all data and metadata associated to each imaged and identified object. Metadata and features are in columns (n =71) and objects are in rows (n = 1,153,507). - One comma separated file containing the name, type, definition and unit of each field (column) in the .tsv (dataset_descriptor_zooscan). - One comma separated file containing the taxonomic list of the dataset, with counts and nature of the content of the category, i.e. “T” for taxonomical category, and “M” for morphological category (taxonomy_descriptor_zooscan). - A individual_images directory containing images of each imaged object sorted in subdirectories named according to objects’ identifications object_taxon appended to an Ecotaxa internal taxon numerical id classif_id (i.e. taxon__123456789) across years and sampling stations. Within subdirectories, each object is named after its unique internal Ecotaxa identifier, objid. - A Map of the sampling station location over the 2004-2016 period

This dataset is composed of 702,111 zooplankton individuals, zooplankton pieces, non-living particles and imaging artefacts, ranging from 300 µm to 3.39 mm Equivalent Spherical Diameter, individually imaged and measured with the ZooCAM (Colas et al., 2018). The objects were sorted in 127 taxonomic and morphological groups. The imaged objects originate from samples collected on the Bay of Biscay continental shelf, in spring, from 2016 to 2019 during the PELGAS ecosystemic surveys (Doray et al., 2018). The samples were collected with a WP2 200 µm mesh size fitted with a Hydrobios (back-run stop) mechanical flowmeter, generally from 100 m depth to the surface, or 5 m above the sea floor (if bottom depth less than 100 m) in vertical hauls, at night. The samples were imaged on board, live, after collection and subsampling, and preserved in 4% buffered formaldehyde seawater. Each imaged object is geolocated, associated to a station, a cruise, a year and other metadata that enable the reconstruction of quantitative zooplankton communities for ecological studies (i.e. Grandrémy et al., 2023a). Each object is described by 52 morphological and grey level based features (8 bits encoding, 0 = black, 255 = white), including size, automatically extracted on each individual image by the ZooCAM software. Each object was taxonomically identified using the ZooCAM software and the web based application Ecotaxa with built-in, random forest and CNN based, semi-automatic sorting tools followed by expert validation or correction (Picheral et al., 2017). Images from 2016-2017 contain ROI bounding box limits, metadata at the bottom of each image, and non-homogenised background within and around the ROI bounding box; Images from 2018 contain non-homogenised background within the ROI bounding box only; images from 2019 have a completely homogeneous and thresholded background around the object.  The differences arose from successive ZooCAM software updates that do not modify the calculation of object’s features. This dataset is intended to be used for ecological studies as well as machine learning applied to plankton studies. The archive contains : - One tab separated file (PELGAS ZooCAM zooplankton dataset) containing all data and metadata associated to each imaged and identified object. Metadata and features are in columns (n =72) and objects are in rows (n = 702,111). - One comma separated file containing the name, type, definition and unit of each field (column) in the .tsv (dataset descriptor zoocam). - One comma separated file containing the taxonomic list of the dataset, with counts and nature of the content of the category, i.e. “T” for taxonomical category, and “M” for morphological category (taxonomy descriptor zoocam). - A individual_images directory containing images of each object, named according to the object id objid and sorted in subdirectories according to their taxonomic identification, across years and sampling stations. - A Map of the sampling station location over the 2016-2019 period.

The upper branch of the Atlantic Meridional Overturning Circulation (AMOC) plays a critical role in ocean circulation and climate change, yet its variability during the last glacial period is poorly documented. Here, we investigate the northward-flowing Glacial Eastern Boundary Current (GEBC) in the North Atlantic, known today as the European Slope Current, and representing the easternmost portion of the upper branch of the AMOC. Based on flow speed (sortable silt, XRF) and radiogenic/stable isotopic records, we show that Dansgaard-Oeschger (D/O) interstadials (stadials) correspond to a faster (weaker) GEBC during the ~50-15 ka period. This, by analogy to present-day conditions, suggests enhanced (reduced) strength of the subpolar gyre and, by extension, of northern-sourced water production and AMOC during D-O interstadials (stadials). Concomitant fluctuations of both the European Ice Sheet and the GEBC between ~30 and 17 ka suggest an active role of the upper branch of AMOC in the poleward transport of heat and freshwater to the northern North Atlantic, with direct impacts on deep water formation and AMOC strength. Our GEBC reconstruction is the first physical (non-chemical) record documenting dynamic upper AMOC variability at high resolution in the eastern basin of the North Atlantic. Together with the deep North Atlantic records of northern-sourced water export, they confirm the central role of the AMOC in the generation of abrupt climate changes.

The MAGEST observation network aims to continuously monitor the physico-chemical quality of the waters of the estuaries of Northern Aquitaine. In 2023, this regional network is composed of 12 stations.

Le drone marin PAMELi est né d'un besoin d’observations répétées, géolocalisées et simultanées de différentes disciplines, et de l’archivage et l’accessibilité dans le temps de ces différentes observations. Les premières missions ont été réalisées en juin 2020.

La problématique concerne l'impact du changement global sur les zones côtières et son importance relative par rapport aux activités humaines locales (global vs local). L'objectif général de SOMLIT est orienté vers la détection de ce forçage en zones côtières fortement influencées par les impacts locaux des activités humaines. L'étude du littoral français dans son ensemble, rendue possible par le choix de sites à situation de normalité, d'un suivi de paramètres pertinents, d'un échantillonnage bimensuel et de la mise en place d'une charte qualité, permet une approche multi-paramètres et multi-sites.

En réponse à la sensibilité des littoraux face aux aléas naturels et anthropiques, le SNO DYNALIT, labellisé par le CNRS INSU en 2014, permet, au travers de ces observations, de quantifier les évolutions du littoral en France métropolitaine et ultra-marine, et de comprendre les processus qui sont à l'origine des changements morphodynamiques littoraux. Il est dédié à la métrologie récurrente et de long terme visant à caractériser le fonctionnement du littoral et gère ainsi l’acquisition, la collecte et la mise en cohérence de données métrologiques de qualité de 35 sites ateliers (côtes sableuses, falaises, embouchures) répartis sur toutes les façades maritimes françaises.