The upper ocean pycnocline (UOP) monthly climatology is based on the ISAS20 ARGO dataset containing Argo and Deep-Argo temperature and salinity profiles on the period 2002-2020. Regardless of the season, the UOP is defined as the shallowest significant stratification peak captured by the method described in Sérazin et al. (2022), whose detection threshold is proportional to the standard deviation of the stratification profile. The three main characteristics of the UOP are provided -- intensity, depth and thickness -- along with hydrographic variables at the upper and lower edges of the pycnocline, the Turner angle and density ratio at the depth of the UOP. A stratification index (SI) that evaluates the amount of buoyancy required to destratify the upper ocean down to a certain depth, is also included. When evaluated at the bottom of the UOP, this gives the upper ocean stratification index (UOSI) as discussed in Sérazin et al. (2022). Three mixed layer depth variables are also included in this dataset, including the one using the classic density threshold of 0.03 kg.m-3, along with the minimum of these MLD variables. Several statistics of the UOP characteristics and the associated quantities are available in 2°×2° bins for each month of the year, whose results were smoothed using a diffusive gaussian filter with a 500 km scale. UOP characteristics are also available for each profile, with all the profiles sorted in one file per month.

This dataset provides detections of fronts derived from high resolution remote sensing SST observations by SEVIRI L3C from OSISAF over Western Europe region. The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by OceanDataLab and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

In order to better characterize the genetic diversity of Cetaceans and especially the common Dolphin from the Bay of Biscay, sequences from the mitochondrial Cytochrome B region were obtained from water samples acquired close to groups of dolphins.

The CDR-derived Wet Tropospheric Correction (WTC) Product V2 is generated from the Level-2+ along-track altimetry products version 2024 (L2P 2024) distributed by AVISO+ (www.aviso.altimetry.fr). It provides a long-term, homogenized estimation of the wet tropospheric correction based on Climate Data Records (CDRs) of atmospheric water vapour combined with high frequencies MWR data. Two independent CDRs datasets are used: - REMSS V7R2 (coverage until 2022) https://www.remss.com/measurements/atmospheric-water-vapor/tpw-1-deg-product/ - HOAPS V5 precursor CDR from EUMETSAT CM SAF (coverage until 2020) HOAPS V4/V5 data available via https://wui.cmsaf.eu Note: the HOAPS V5 precursor is not yet an official CM SAF product; full validation and public release are pending. The MWR/CDR WTC V2 estimates is derived using spatially varying but temporally constant polynomial coefficients (ai). 1. WTC V2 – Along-track L2P Product Data format: The WTC V2 product is delivered in Level-2+ (L2P) format, along the satellite ground track. Each mission is distributed as a compressed archive (.tar.gz) containing one NetCDF4 CF-1.8 file per mission cycle. Archive naming convention: <mission>_WTC_from_WV_CDR_<version>.tar.gz mission: TP (TOPEX/Poseidon), J1, J2, J3 version: product version (currently V2) File naming convention inside archives: <mission>_C<cycle>.nc cycle: 4-digit cycle index (e.g., C0001) Each NetCDF file contains: 1/ Along-track WTC estimate; 2/ Ancillary information; 3/ Space–time coordinates 2. WTC CDR Uncertainties – Gridded Product: A complementary product is provided, delivering regional trend estimates and associated uncertainties from the WTC Climate Data Record. The uncertainty product is distributed as a single NetCDF4 file: wtc_trend_uncertainties.nc . This file contains global gridded fields of WTC CDR trend and uncertainty parameters. Product content: This is the first dedicated version providing both: WTC CDR (HOAPS) linear trends, and Uncertainty estimates on these trends. Uncertainties are expressed as 1-sigma confidence intervals, and propagated using the methodology described in Section 2.3 of the Product User Manual. The product includes: - Total uncertainty on the WTC trend, propagated from all identified uncertainty sources in the WTC–TCWV regression. - Individual contributions of uncertainty sources (Uncertainties on regression coefficients: a0, a1 and their standard deviations; Uncertainties inherited from the HOAPS TCWV CDR) These fields enable users to assess the relative importance of each uncertainty component and recompute uncertainty propagation with alternative methods. Included regression input variables: To ensure transparency and reproducibility, the product provides: 1/ regression coefficients a0, a1; 2/ their associated uncertainties (std of a0, std of a1); 3/additional diagnostic fields required to recompute uncertainties if needed.

We developed a panel of single nucleotide polymorphism (SNP) markers for thornback ray Raja clavata using a RADSeq protocole. Demultiplexed sequences were aligned to the genome of Leucoraja erinacea which was used as reference genome. From an initial set of 389 483 putative SNPs, 7741 SNPs with the largest minor allele frequency were selected for implementation on an Infinium® XT iSelect-96 SNP-array implemented by LABOGENA DNA. For the array, SNPs [T/C] and [T/G] were replaced by those from the complementary strand [A/G] and [A/C] respectively. For some SNPs, a second SNP was found in the 50 nucleotide bases flanking sequence. In these cases, two SNP probes were developed with each of the two alleles of the second SNP. A SNP probe naming convention was adopted to identify these pairs of probes corresponding to the same SNP locus: “MAJ” or “MIN” followed by the corresponding base was included in the probe name. For some of these pairs, only one of the two markers could be developed, resulting in a total set of 9120 SNP probes, including 6360 single SNP probes, 10 MAJ or MIN probes for which a single probe was successfully developed, and 1375 pairs of probes with MAJ and MIN versions. The 9120 SNP genotypes were then scored using the clustering algorithm implemented in the Illumina® GenomeStudio Genotyping Analysis Module v2.0.3 for 7726 individual samples, including duplicates, mostly from the Bay of Biscay but also from the Mediterranean Sea and West Iberia. Overall, 1643 SNPs failed to be genotyped in all individuals, for 319 markers the minor allele was not found and 7158 markers (including 1974 for 987 MIN-MAJ pairs) produced bi-allelic genotypes. The majority of these SNPs had a minor allele frequency between 0.1 and 0.5. The MIN-MAJ probes can be used for quality checking the genotyping results

Phenotypic plasticity, the ability of a single genotype to produce multiple phenotypes, is important for survival when species are faced with novel conditions. Theory predicts that range-edge populations will have greater phenotypic plasticity than core populations, but empirical examples from the wild are rare. The honeycomb worm, Sabellaria alveolata (L.), constructs the largest biogenic reefs in Europe, which support high biodiversity and numerous ecological functions. In order to assess the presence, causes and consequences of intraspecific variation in developmental plasticity and thermal adaptation in the honeycomb worm, we carried out common-garden experiments using the larvae of individuals sampled from along a latitudinal gradient covering the entire range of the species. We exposed larvae to three temperature treatments and measured phenotypic traits throughout development. We found phenotypic plasticity in larval growth rate but local adaptation in terms of larval period. The northern and southern range-edge populations of S. alveolata showed phenotypic plasticity for growth rate: growth rate increased as temperature treatment increased. In contrast, the core range populations showed no evidence of phenotypic plasticity. We present a rare case of range-edge plasticity at both the northern and southern range limit of species, likely caused by evolution of phenotypic plasticity during range expansion and its maintenance in highly heterogeneous environments. This dataset presents the raw image data collected for larval stages of Sabellaria alveolata from 5 populations across Europe and Northern Africa, exposed to 15, 20 and 25 C. Included are also opercular crown measurements used to estimate de size classes of individuals present in each population.  All measurements made with the images collected are presented in an Excel spreadsheet, also available here.

This data set is related to the article "Improving the robustness of dissipation rate estimates from microstructure shear data processing in ocean turbulence" (submitted to JTech, AMS). It provides the raw data files (with extension .p) from a vertical microstructure profiler VMP-6000 used for the published study. Raw data files are provided since the study precisely report on the data processing of raw microstructure horizontal velocity shear data to get dissipation rates of turbulent kinetic energy using the manufacturer software. The software (ODAS) can be downloaded from the manufacturer website at: https://rocklandscientific.com. The profiles were collected in the Mediterranean Sea in 2013 (French VAD cruise) and 2014 (Italian MEDOCC cruise), and in the North Atlantic Ocean in 2021 (MoMAR cruise). More details on the profiles are given in the related publication.

French benthic invertebrates composition and abundance taxa data are collected during monitoring surveys on the English Channel / Bay of Biscay coasts and Mediterranean coast (Quadrige program code : REBENT_FAU, RSL_FAU). Protocols are implemented in the Water Framework Directive.  Data are transmitted in a Seadatanet format (CDI + ODV) to EMODnet Biology european database. 498 ODV files have been generated from period 01/01/2003 to 31/12/2021.

The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from Sentinel-1 (referred to as SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP data) with a particular focus for use in climate studies. This dataset contains the Sentinel-1 SAR Remote Sensing Integrated Sea State Parameter product (v1.0), which forms part of the ESA Sea State CCI version 3.0 release. This product provides along-track primary significant wave height measurements and secondary sea state parameters, calibrated with CMEMS model data and reference in situ measurements at 20km resolution every 100km, processed using the Pleskachevsky et. al., 2021 emprical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP v3 dataset come from the Sentinel-1 satellite missions spanning from 2014 to 2021 (Sentinel-1 A, Sentinel-1 B).

The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 4 (L4) data) with a particular focus for use in climate studies. This dataset contains the Version 3 Remote Sensing Significant Wave Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2021 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).