Web Map Service for Emodnet Chemistry

Serveur wms public de l'Ifremer, Accès aux données du Sismer

In the context of contamination of shellfish species by domoic acid produced by microalgal species of the genus Pseudo-nitzschia, we studied the particular case of depuration kinetics of king scallops, Pecten maximus. The study was based on the REPHYTOX dataset (https://doi.org/10.17882/47251) which includes, among others, long-term time series of domoic acid in shellfish species. We selected only the locations along the English Channel and the Atlantic coastline. Contamination events were defined for each locations, depuration rates were estimated fitting an exponential decay model using a non-linear least squares regression. Spatio-temporal variability was assessed as well as correlations to environmental conditions, using REPHY dataset (https://doi.org/10.17882/47248). Finally, scenarios for predictions of either the dynamics of depuration or the domoic acid contamination at a precise date were performed. Four files are available as data used for the study and results : (i) subset of REPHYTOX dataset, (ii) subset of REPHY dataset, used in this study and (iii) contamination event information (i.e., initial and end date of the event, initial domoic acid concentration) and depuration rate estimations, and (iv) predictions of depuration dynamics with different scenarios. Information on each file is detailed in the end user manual and methodology and results are linked to an article in preparation.

Donnees publiques de la Directive Cadre Strategie pour le Milieu Marin (DCSMM)

Argo is a global array of 3,000 free-drifting profiling floats that measures the temperature and salinity of the upper 2000 m of the ocean. This allows, for the first time, continuous monitoring of the temperature, salinity, and velocity of the upper ocean, with all data being relayed and made publicly available within hours after collection. The array provides 100,000 temperature/salinity profiles and velocity measurements per year distributed over the global oceans at an average of 3-degree spacing. Some floats provide additional bio-geo parameters such as oxygen or chlorophyll. All data collected by Argo floats are publically available in near real-time via the Global Data Assembly Centers (GDACs) in Brest (France) and Monterey (California) after an automated quality control (QC), and in scientifically quality controlled form, delayed mode data, via the GDACs within six months of collection. The BGC-Argo Sprof snapshot is a subset of the global Argo data snapshot.  It is created to ease BGC-Argo data usage.  The content is the same if you are to download the global Argo data snapshot, and then select all the BGC-Argo Sprof files.  Please use the same DOI and citation as the global Argo data snapshot.

Within the ESA Coastal Blue Carbon project, the LIENSs laboratory contributed drone-derived products, SP80 ground survey points (with longitude, latitude, and plant data) and biomass measurements to test classification models for mapping salt marsh vegetation (e.g., Esnandes) and associated biomass/carbon stocks. Links to associated datasets are provided at the bottom of this sheet.

15 years of L-Band remote sensing Sea Surface Salinity (SSS) measurements have proven the capability of satellite SSS to resolve large scale to mesoscale SSS features in tropical to subtropical ocean. In mid to high latitude, L-Band measurements still suffer from large scale and time varying biases. Here, a simple method is proposed to mitigate the large scale and time varying biases. First, in order to estimate these biases, an Optimal Interpolation (OI) using a large correlation scale is used to map SMOS and SMAP L3 products and is compared to equivalent mapping of in situ observations. Then, a second mapping is performed on corrected SSS at scale of SMOS/SMAP resolution (~45 km). This procedure allows to correct and merge both products, and to increase signal to noise ratio of the absolute SSS estimates. Using thermodynamic equation of state (TEOS-10), the resulting L4 SSS product is combined with microwave satellite SST products to produce sea surface density and spiciness, useful to fully characterize the surface ocean water masses. The new L4 SSS products is validated against independent in situ measurements from low to high latitudes. The L4 products exhibits a significant improvement in mid-and high latitude in comparison to the existing SMOS and SMAP L3 products.

Plankton was sampled with a Continuous Underway Fish Egg Sampler (CUFES, 315µm mesh size) at 4 m below the surface, and a WP2 net (200µm mesh size) from 100m to the surface, or 5 m above the sea floor to the surface when the depth was < 100 m, in the Bay of Biscay. The full images were processed with the ZooCAM software and the embedded Matrox Imaging Library (Colas et a., 2018) which generated regions of interest (ROIs) around each individual object and a set of features measured on the object. The same objects were re-processed to compute features with the scikit-image library http://scikit-image.org. The 1, 286, 590 resulting objects were sorted by a limited number of operators, following a common taxonomic guide, into 93 taxa, using the web application EcoTaxa http://ecotaxa.obs-vlfr.fr. For the purpose of training machine learning classifiers, the images in each class were split into training, validation, and test sets, with proportions 70%, 15% and 15%. The archive contains : taxa.csv.gz Table of the classification of each object in the dataset, with columns : - objid : unique object identifier in EcoTaxa (integer number). - taxon_level1 : taxonomic name corresponding to the level 1 classification - lineage_level1 : taxonomic lineage corresponding to the level 1 classification - taxon_level2 : name of the taxon corresponding to the level 2 classification  - plankton : if the object is a plankton or not (boolean) - set : class of the image corresponding to the taxon (train : training, val : validation, or test) - img_path : local path of the image corresponding to the taxon (of level 1), named according to the object id features_native.csv.gz Table of morphological features computed by ZooCAM. All features are computed on the object only, not the background. All area/length measures are in pixels. All grey levels are in encoded in 8 bits (0=black, 255=white). With columns : - area : object's surface - area_exc : object surface excluding white pixels - area_based_diameter : object's Area Based Diameter: 2 * (object_area/pi)^(1/2) - meangreyobjet : mean image grey level - modegreyobjet : modal object grey level - sigmagrey : object grey level standard deviation - mingrey : minimum object grey level - maxgrey : maximum object grey level - sumgrey : object grey level integrated density: object_mean*object_area - breadth : breadth of the object along the best fitting ellipsoid minor axis - length : breadth of the object along the best fitting ellipsoid majorr axis - elongation : elongation index: object_length/object_breadth - perim : object's perimeter - minferetdiam : minimum object's feret diameter - maxferetdiam : maximum object's feret diameter - meanferetdiam : average object's feret diameter - feretelongation : elongation index: object_maxferetdiam/object_minferetdiam - compactness : Isoperimetric quotient: the ration of the object's area to the area of a circle having the same perimeter - intercept0, intercept45 , intercept90, intercept135 : the number of times that a transition from background to foreground occurs a the angle 0ø, 45ø, 90ø and 135ø for the entire object - convexhullarea : area of the convex hull of the object - convexhullfillratio : ratio object_area/convexhullarea - convexperimeter : perimeter of the convex hull of the object - n_number_of_runs : number of horizontal strings of consecutive foreground pixels in the object - n_chained_pixels : number of chained pixels in the object - n_convex_hull_points : number of summits of the object's convex hull polygon - n_number_of_holes : number of holes (as closed white pixel area) in the object - roughness : measure of small scale variations of amplitude in the object's grey levels - rectangularity : ratio of the object's area over its best bounding rectangle's area - skewness : skewness of the object's grey level distribution - kurtosis : kurtosis of the object's grey level distribution - fractal_box : fractal dimension of the object's perimeter - hist25, hist50, hist75 : grey level value at quantile 0.25, 0.5 and 0.75 of the object's grey levels normalized cumulative histogram - valhist25, valhist50, valhist75 : sum of grey levels at quantile 0.25, 0.5 and 0.75 of the object's grey levels normalized cumulative histogram - nobj25, nobj50, nobj75 : number of objects after thresholding at the object_valhist25, object_valhist50 and object_valhist75 grey level - symetrieh :index of horizontal symmetry - symetriev : index of vertical symmetry - skelarea : area of the object skeleton - thick_r : maximum object's thickness/mean object's thickness - cdist : distance between the mass and the grey level object's centroids features_skimage.csv.gz Table of morphological features recomputed with skimage.measure.regionprops on the ROIs produced by ZooCAM. See http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops for documentation. inventory.tsv Tree view of the taxonomy and number of images in each taxon, displayed as text. With columns : - lineage_level1 : taxonomic lineage corresponding to the level 1 classification - taxon_level1 : name of the taxon corresponding to the level 1 classification - n : number of objects in each taxon group map.png Map of the sampling locations, to give an idea of the diversity sampled in this dataset. imgs Directory containing images of each object, named according to the object id objid and sorted in subdirectories according to their taxon.

To deliver the best Argo data to users in the simplest way, No QC flags; No data mode; No manuals - Just straight forward good data The Argo program provides an unprecedented volume of oceanographic data, yet its operational complexity — involving multiple data modes, quality control flags, and metadata conventions — often hinders its direct usage. The EasyOneArgo initiative addresses this challenge by delivering simplified, high-quality subsets of Argo data, specifically designed to streamline user access and integration. We introduce two core products: EasyOneArgoTS, a curated selection of temperature-salinity profiles filtered by strict quality criteria and optimized across real-time, adjusted, and delayed modes; and EasyOneArgoTSLite, its vertically interpolated counterpart standardized over 102 pressure levels. Each profile is packaged as a standalone CSV file with structured metadata, and indexed for seamless retrieval. Visual comparisons reveal clear advantages in usability and consistency, notably between raw and interpolated datasets. The approach is being extended to biogeochemical variables via EasyOneArgoBGC and EasyOneArgoBGCLite, currently under development. EasyOneArgo products are publicly available through monthly FAIR-compliant releases and invite community feedback for continued refinement. This work represents a user-centric shift in Argo data delivery: no flags, no manuals — just clean, structured ocean data ready for immediate scientific application.