'''Short description:''' Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding products. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. The ouput products corresponds to one file per month gathering all the swell systems at a global scale. This product is processed by the WAVE-TAC multi-mission SAR and CFOSAT/SWIM data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The spectral data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. '''DOI (product) :''' https://doi.org/10.48670/moi-00175

Data were collected by the Laboratoire de Ressources Halieutiques d'Aquitaine (Ifremer). The observations were carried out between April 1992 and July 2006. Observations and measures were made on board commercial vessels, practicing on Adour River generally in springtime and in summer. Sampling of catches took place on the river between March and July; The researchers took scales, a blood sample, took measurements of length and weight on the fish caught by the fishermen during the observed fishing trips. Sampling was of sampling-by-opportunity type. The driftnet is an emblematic and very old métier in this area, targeting different species as atlantic salmon (Salmo salar), sea trout (Salmo trutta), sea Lamprey (Pétromyzon marinus) and also allis shad (Alosa alosa). This activity is framed by particular regulation as quota of fishing days and licenses. Between 15 and 20 costal boats practice this métier during the year and the fleet characteristics are homogenous. The boats put the net in the water (around 100 m long) and let it drift with the surface current (downstream) while the tide is rising; the fish swimming upstream the river are then trapped in the water column. Different meshes can be used.

Good Environmental Status assessment (GES) for descriptor 8 (contaminants, D8) of the Marine Strategy Framework Directive (MSFD) is reached when concentrations of contaminants are at levels not giving rise to pollution effects. It is described by 4 criteria among which the first one focus on the concentration of the contaminants in the environment (criteria 1 of the D8, D8C1). The environmental status for D8 in France includes assessment of contaminant concentrations in sediment, bivalves, fish, birds, mammals to cover the French marine area the continental shelf from the coast line). The 8 tables below present the assessment of the chemical contamination in sediment and bivalves on the coastal area of the 4 French marine subregions for D8 as part of the 2024 GES assessment. These tables report the status and temporal trends of each station x matrice x substance triplet in each of the 4 French marine subregions. Explanation on how to read the cells is given in the “read file”. The environmental assessment for D8 in France can be found in Mauffret al., 2023 (DOI:10.13155/97214). It includes 17 national indicator assessments, 4 OSPAR indicators and integrated assessment in selected assessment units at the level of the criteria 1 and 2. 

C-RAID: Comprehensive Reprocessing of Drifting Buoy Data (1979-2018) The C-RAID (Copernicus - Reprocessing of Drifting Buoys) project delivers a comprehensive global reprocessing of historical drifting buoy data and metadata, providing climate-quality observations for marine and atmospheric research. Dataset Overview The C-RAID dataset encompasses metadata from 21 858 drifting buoys deployed between 1979 and 2018. Of these, 17 496 buoys have undergone complete reprocessing with scientific validation in delayed mode, including comparison against ERA5 reanalysis. Project Context Managed by the WMO DBCP Drifting Buoys Global Data Assembly Centre (GDAC) through Ifremer, Météo-France, and Ocean Sciences Division of Fisheries and Oceans Canada, C-RAID focuses on enhanced quality control and delivery of climate-quality drifting buoy data for the Marine Climate Data System (MCDS). Objectives - Complete reprocessing and clean-up of the historical drifting buoy data archive - Recovery and rescue of missing datasets - Reprocessing of Argos data with improved positioning using Kalman filter algorithms - Homogenization of quality control procedures across marine and atmospheric parameters Funding & Governance C-RAID was funded by the Copernicus Programme through the European Environment Agency (Contract # EEA/IDM/15/026/LOT1), supporting cross-cutting coordination activities for the in-situ component of Copernicus Services. Stakeholders & Partnerships The project is led by the DB-GDAC consortium (Ifremer, Météo-France) in collaboration with EUMETNET's E-SURFMAR programme, NOAA AOML, and JCOMMOPS. Key Achievements - Reprocessing of approximately 24 000 buoy-years of observations - Recovery of missing datasets and metadata through data rescue efforts - Implementation of homogeneous, rich metadata and data formats - Enhanced Argos location accuracy using Kalman filter reprocessing - Standardized quality control and validation procedures Data Access & FAIR Principles C-RAID provides FAIR (Findable, Accessible, Interoperable, Reusable) data access through: - Web-based data discovery portal for human users - API services for data discovery, subsetting, and download (machine-to-machine access) Target Users The dataset serves major operational and research programmes including: - Copernicus Climate Change Service (C3S) - Copernicus Marine Environment Monitoring Service (CMEMS) - iQuam (in-situ SST Quality Monitor) - ICOADS (International Comprehensive Ocean-Atmosphere Data Set) - GHRSST (Group for High Resolution Sea Surface Temperature) - ISPD (International Surface Pressure Databank) - ICDC (Integrated Climate Data Center)  

The dataset made available here is the monthly climatology (i.e. 12 months) of ocean surface Mixed Layer Depth (MLD) over the global ocean, at 1 degree x 1 degree spatial resolution. The climatology is based on about 7.3 million casts/profiles of temperature and salinity measurements made at sea between January 1970 and December 2021. Those profiles data come from the ARGO program and from the NCEI-NOAA World Ocean Database (WOD, Boyer et al. 2018). The MLD is computed on each individual cast/profile using a threshold criterion. The depth of the mixed layer is defined as the shallowest depth where the surface potential density of the profile is superior to a reference value taken close to the surface added with the chosen threshold. Here we take a threshold value for the density of 0.03kg/m3, and a surface reference depth fixed at 10m (de Boyer Montégut et al., 2004). This mixed layer is by definition homogeneous in density (up to 0.03 kg/m3 variations) and can also be called an isopycnal layer. It is especially intended for validation of MLD fields of the Ocean General Circulation Models of the ocean sciences community (e.g. Tréguier et al., 2023, Iovino et al. 2023, using v2022 of this dataset). More information and some other related datasets can be found at : https://cerweb.ifremer.fr/mld (or https://www.umr-lops.fr/en/Data/MLD redirecting to previous page).

Sardine physiological measurments from september to november 2020

Rocch, the french "mussel watch", provides regulatory data for shellfish area quality management. Once a year, molluscs (mainly mussels and oysters) were sampled at fixed periods (currently mid-February, with a tolerance of one tide before and after the target date) on 70 to 80  monitoring stations in areas used as bivalve molluscs production. For each monitoring station, molluscs are collected in wild beds or facilities, ensuring a minimum stay of 6 months on-site before sampling. The individuals selected are adults of a single species and uniform size (30 to 60 mm long for mussels, 2 to 3 years old for oysters, and commercial size for other species). A minimum of 50 mussels (and other species of similar size) or 10 oysters is required to constitute a representative pooled sample. Lead, mercury, cadmium, PAHs, PCBs, dioxins and, since 2023, regulated PFASs are analysed in molluscs tissues.

'''Short description:''' The NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 is produced by a coupled hydrodynamic-biogeochemical model system with tides, implemented over the North East Atlantic and Shelf Seas at 7 km of horizontal resolution and 24 vertical levels. The product is updated daily, providing 7-day forecast for temperature, salinity, currents, sea level and mixed layer depth. Products are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency. '''DOI (product) :''' https://doi.org/10.48670/mds-00367

WMS/WFS services for marine chemical datasets used in EMODNet Chemistry and provided by SeaDataNet. The data distribution is managed by the Common Data Index (CDI) Data Discovery and Access service

Dataset summary Plankton and detritus are essential components of the Earth’s oceans influencing biogeochemical cycles and carbon sequestration. Climate change impacts their composition and marine ecosystems as a whole. To improve our understanding of these changes, standardized observation methods and integrated global datasets are needed to enhance the accuracy of ecological and climate models. Here, we present a global dataset for plankton and detritus obtained by two versions of the Underwater Vision Profiler 5 (UVP5). This release contains the images classified in 33 homogenized categories, as well as the metadata associated with them, reaching 3,114 profiles and ca. 8 million objects acquired between 2008-2018 at global scale. The geographical distribution of the dataset is unbalanced, with the Equatorial region (30° S - 30° N) being the most represented, followed by the high latitudes in the northern hemisphere and lastly the high latitudes in the Southern Hemisphere. Detritus is the most abundant category in terms of concentration (90%) and biovolume (95%), although its classification in different morphotypes is still not well established. Copepoda was the most abundant taxa within the plankton, with Trichodesmium colonies being the second most abundant. The two versions of UVP5 (SD and HD) have different imagers, resulting in a different effective size range to analyse plankton and detritus from the images (HD objects >600 µm, SD objects >1 mm) and morphological properties (grey levels, etc.) presenting similar patterns, although the ranges may differ. A large number of images of plankton and detritus will be collected in the future by the UVP5, and the public availability of this dataset will help it being utilized as a training set for machine learning and being improved by the scientific community. This will reduce uncertainty by classifying previously unclassified objects and expand the classification categories, ultimately enhancing biodiversity quantification. Data tables The data set is organised according to: - samples : Underwater Vision Profiler 5 profiles, taken at a given point in space and time. - objects : individual UVP images, taken at a given depth along the each profile, on which various morphological features were measured and that where then classified taxonomically in EcoTaxa. samples and objects have unique identifiers. The sample_id is used to link the different tables of the data set together. All files are Tab separated values, UTF8 encoded, gzip compressed. samples.tsv.gz - sample_id    <int>    unique sample identifier - sample_name    <text>    original sample identifier - project    <text>    EcoPart project title - lat, lon    <float>    location [decimal degrees] - datetime    <text>    date and time of start of profile [ISO 8601: YYYY-MM-DDTHH:MM:SSZ] - pixel_size    <float>    size of one pixel [mm] - uvp_model    <text>    version of the UVP: SD: standard definition, ZD: zoomed, HD: high definition samples_volume.tsv.gz Along a profile, the UVP takes many images, each of a fixed volume. The profiles are cut into 5 m depth bins in which the number of images taken is recorded and hence the imaged volume is known. This is necessary to compute concentrations. - sample_id    <int>    unique sample identifier - mid_depth_bin    <float>    middle of the depth bin (2.5 = from 0 to 5 m depth) [m] - water_volume_imaged    <float>    volume imaged = number of full images × unit volume [L] objects.tsv.gz - object_id    <int>     unique object identifier - object_name    <text>     original object identifier - sample_id    <int>     unique sample identifier - depth    <float>    depth at which the image was taken [m] - mid_depth_bin    <float>    corresponding depth bin [m]; to match with samples_volumes - taxon    <text>     original taxonomic name as in EcoTaxa; is not consistent across projects - lineage    <text>     taxonomic lineage corresponding to that name - classif_author    <text>     unique, anonymised identifier of the user who performed this classification - classif_datetime    <text>     date and time at which the classification was - group    <text>     broader taxonomic name, for which the identification is consistent over the whole dataset - group_lineage    <text>     taxonomic lineage corresponding to this broader group - area_mm2    <float>    measurements on the object, in real worl units (i.e. comparable across the whole dataset) … - major_mm    <float> - area    <float>    measurements on the objet, in [pixels] and therefore not directly comparable among the different UVP models and units - mean    <float> … - skeleton_area    <float> properties_per_bin.tsv.gz The information above allows to compute concentrations, biovolumes, and average grey level within a given depth bin. The code to do so is in `summarise_objects_properties.R`. - sample_id    <int>     unique sample identifier - depth_range    <text>     range of depth over which the concentration/biovolume are computed: (start,end], in [m] where `(` means not including, `]` means including - group    <text>     broad taxonomic group - concentration    <float>    concentration [ind/L] - biovolume    <float>    biovolume [mm3/L] - avg_grey    <float>    average grey level of particles [no unit; 0 is black, 255 is white] ODV_biovolumes.txt, ODV_concentrations.txt, ODV_grey_levels.txt This is the same information as above, formatted in a way that Ocean Data View https://odv.awi.de can read. In ODV, go to Import > ODV Spreadsheet and accept all default choices. Images The images are provided in a separate, much larger, zip file. They are stored with the format `sample_id/object_id.jpg`, where `sample_id` and `object_id` are the integer identifiers used in the data tables above.