The data sets presented here result from the long-term monitoring of individual growth patterns in anchovy and sardine in the Bay of Biscay, from 2000 to 2018. They derived from the PELGAS survey series (Doray et al., 2018), which monitors annually the Bay of Biscay pelagic ecosystem since 2000. The survey is performed in May during the peak spawning of anchovy and main spawning of sardine. Among the many data collected, anchovy and sardine populations are assessed by combining acoustic records with pelagic trawl hauls catches and ICES survey protocoles are used, as detailed in Doray et al. (2021). Briefly, fish acoustic backscatter are recorded along survey transect lines and pelagic trawl hauls undertaken opportunistically to identify echotraces to species and collect fish samples for acquiring biometric data. At each trawl haul and for each species, a random subsample of individuals is taken to establish the species’ length distributions. For anchovy and sardine, this subsample is further subsampled, spanning the whole length range, to take individual fish measurements. These amount to extracting otoliths and measuring individuals’ age, length, weight, sexual maturity and other parameters. Individual measurements are taken on fourty individuals of anchovy and sardine when the species are present in the catch. For each individual fish, the two otolith sagittae are extracted on board, mounted in leukit for age reading on board when permitting and/or on land in the laboratory. Growth patterns in the otoliths were analysed on land with a binocular stereomicroscope under reflected natural light. For anchovy, otoliths’ growth was measured for all individuals in all the hauls. For sardine, trawl hauls were selected and all individual otoliths were measured in each selected haul. The selection was made using the geographical stratification defined in Petitgas et al. (2018) based on the ecosystem spatial structure. An average of two to three hauls in each of the four strata were selected per year. The otoliths mounted in leukit were imaged and growth-at-age in the otoliths was measured with the software TNPC (Traitement numérique des pièces calcifiées: Mahé et al., 2009). Under the binocular microscope and natural light, the otoliths showed hyaline (aragonite-poor) rings corresponding to winter periods of low growth and between the rings, white opaque (aragonite-rich) portions corresponding to annual growth periods. The annual ring determination, the age assignment and the measurement of annual ring diameters followed ICES protocoles and guidelines for anchovy and sardine (ICES, 2010; 2011). The age was estimated as the number of hyaline rings. If the edge was hyaline, it was counted as a ring as a hyaline edge observed within the first half of the year is assumed to represent the last winter. The diameter of each annual ring was measured from middle of the hyaline ring on one side to the middle of the ring on the opposite side along the major elongated axis of the otolith and passing through its centre. The distance was expressed in mm after calibration of the stereomiscroscope and the pixel images. Such diameter corresponded to growth-at-age. Ages 0 to 4 were considered (diameters R1 to R5). The total diameter of the otolith was also measured. The data sets span 19 years, from 2000 to 2018 and comprise 20,186 and 8,624 individual fish analyzed at 535 and 235 trawl hauls for anchovy and sardine, respectively. These data sets were used by Boëns et al. (2021 and 2023) to analyse changes in growth patterns and growth-selective mortality at age in anchovy and sardine under environmental and fishing pressures. References: Doray, M., Boyra, G. and Van Der Kooij, J. (eds) (2021). ICES Survey Protocols – Manual for acoustic surveys coordinated under ICES Working Group on Acoustic and Egg Surveys for Small Pelagic Fish (WGACEGG). 1st Edition. ICES Techniques in Marine Environmental Sciences, 64. https://doi.org/10.17895/ices.pub.7462  Doray, M., Petitgas, P., Romagnan, J.-B., Huret, M., Duhamel, E., Dupuy, Ch., Spitz, J., Authier, M., Sanchez, F., Berger, L., Doremus, G., Bourriau, P., Grellier, P. and Masse, J. (2018). The PELGAS survey: ship-based integrated monitoring of the Bay of Biscay pelagic ecosystem. Progress In Oceanography, 166, 15-29. https://doi.org/10.1016/j.pocean.2017.09.015 ICES (2010). Report of the Workshop on Age reading of European anchovy (WKARA), 9-13 November 2009, Sicily, Italy. ICES CM 2009/ACOM: 43. 122 pp. https://doi.org/10.17895/ices.pub.19280525 ICES (2011). Report of the Workshop on Age Reading of European Atlantic Sardine (WKARAS), 14-18 February 2011, Lisbon, Portugal. ICES CM 2011/ACOM:42. 91 pp. https://doi.org/10.17895/ices.pub.19280855 Petitgas, P., Huret, M., Dupuy, Ch., Spitz, J., Authier, M., Romagnan, J.-B. and Doray, M. (2018). Ecosystem spatial structure revealed by integrated survey data. Progress In Oceanography, 166, 189-198. https://doi.org/10.1016/j.pocean.2017.09.012 Mahe, K., Bellail, R., Dufour, J.-L., Boiron-Leroy, A., Dimeet, J., Duhamel, E., Elleboode, R., Felix, J., Grellier, P., Huet, J., Labastie, J., Le Roy, D., Lizaud, O., Manten, M.-L., Martin, S., Metral, L., Nedelec, D., Verin, Y. and Badts, V. (2009). Synthèse française des procédures d'estimation d'âge / French summary of age estimation procedures. https://archimer.ifremer.fr/doc/00000/7294/ Boëns, A., Grellier, P., Lebigre, Ch. and Petitgas, P. (2021). Determinants of growth and selective mortality in anchovy and sardine in the Bay of Biscay. Fisheries Research, 239, 105947. https://doi.org/10.1016/j.fishres.2021.105947 Boëns, A., Ernande, B., Petitgas, P. and Lebigre, Ch. (2023). Different mechanisms underpin the decline in growth of anchovies and sardines of the Bay of Biscay. Evolutionary Applications, 16: 1393–1411. https://doi.org/10.1111/eva.13564  

This product displays for Mercury, positions with values counts that have been measured per matrix for each year and are present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for every available year.

The drought index (DI) is computed using SMOS derived root zone soil moisture (RZSM) monthly fields. The concept is to consider 13 years of SMOS data (2010-2022). The RZSM monthly mean and max over the period is iused to compute a soil water deficit to remove seasonal variability. from teh soil water deficit a soil moisture drought index is computed .The range of values for SMDI lies between -4 to +4, with -4 representing extreme dry conditions and +4 representing extreme wet conditions.

This product displays for Naphthalene, positions with values counts that have been measured per matrix for each year and are present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for every available year.

A consistent dataset of bottom trawl survey data spanning 47 years in the Bay of Biscay was assembled. The dataset includes data from the current EVHOE survey from 1987 to 2019 and two previous surveys carried out in 1973 and 1976. The recent EVHOE time-series from 1997 is also available from DATRAS (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx). The catch in numbers and weight (kg) per haul of all Rajiformes species caught in these surveys is provided. Haul information is provided for all hauls, including those with no catch of Rajiformes. Areas of the sampling strata of the survey and spatial polygones of these strata are provided in separate files.

This visualization product displays beaches locations where non-MSFD monitoring surveys, research & cleaning operations have been applied to collate data on macrolitter (> 2.5 cm). Reference lists associated with these protocols have been indicated with different colors in the map. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata). More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays the density of floating micro-litter per net normalized per km² per year from research and monitoring protocols. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to research and monitoring protocols as MSFD monitoring. Densities were calculated for each net using the following calculation: Density (number of particles per km²) = Micro-litter count / (Sampling effort (km) * Net opening (cm) * 0.00001) When the number of microlitters or the net opening was not filled, the density could not be calculated. Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area.

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.

This dataset provides a World Ocean Atlas of Argo inferred statistics. The primary data are exclusively Argo profiles. The statistics are done using the whole time range covered by the Argo data, starting in July 1997. The atlas is provided with a 0.25° resolution in the horizontal and 63 depths from 0 m to 2,000 m in the vertical. The statistics include means of Conservative Temperature (CT), Absolute Salinity, compensated density, compressiblity factor and vertical isopycnal displacement (VID); standard deviations of CT, VID and the squared Brunt Vaisala frequency; skewness and kurtosis of VID; and Eddy Available Potential Energy (EAPE). The compensated density is the product of the in-situ density times the compressibility factor. It generalizes the virtual density used in Roullet et al. (2014). The compressibility factor is defined so as to remove the dependency with pressure of the in-situ density. The compensated density is used in the computation of the VID and the EAPE.

'''Short description:''' For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products: *0.5 degrees grid for the 50 km scatterometer L2 inputs, *0.25 degrees grid based on 25 km scatterometer swath observations, *and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for: *The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees; *The OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; *The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees In addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations. '''DOI (product) :''' https://doi.org/10.48670/moi-00182