In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (eg become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (eg a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’, spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is done for each of the other datasets, producing a dataset of matchups. About 35 million in situ datapoints were then matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of 287,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. This dataset was funded by ESA Satellite Oceanographic Datasets for Acidification (OceanSODA) project which aims at developing the use of satellite Earth Observation for studying and monitoring marine carbonate chemistry.

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.

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

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.

Raw reads for the assembly of Gambusia holbrooki genome.

Understanding the dynamics of species interactions for food (prey-predator, competition for resources) and the functioning of trophic networks (dependence on trophic pathways, food chain flows, etc.) has become a thriving ecological research field in recent decades. This empirical knowledge is then used to develop population and ecosystem modelling approaches to support ecosystem-based management. The TrophicCS data set offers spatialized trophic information on a large spatial scale (the entire Celtic Sea continental shelf and upper slope) for a wide range of species. It combines ingested prey (gut content analysis) and a more integrated indicator of food sources (stable isotope analysis). A total of 1337 samples of large epifaunal invertebrates (bivalve mollusks and decapod crustaceans), zooplankton, fish and cephalopods, corresponding to 114 species, were collected and analyzed for stable isotope analysis of their carbon and nitrogen content. Sample size varied between taxa (from 1 to 52), with an average of 11.72 individuals sampled per species, and water depths ranged from 57 to 516 m. The gut contents of 1026 fish belonging to ten commercially important species: black anglerfish (Lophius budegassa), white anglerfish (Lophius piscatorius), blue whiting (Micromesistius poutassou), cod (Gadus morhua), haddock (Melanogrammus aeglefinus), hake (Merluccius merluccius), megrim (Lepidorhombus whiffiagonis), plaice (Pleuronectes platessa), sole (Solea solea) and whiting (Merlangius merlangus) were analyzed. The stomach content data set contains the occurrence of prey in stomach, identified to the lowest taxonomic level possible. To consider potential ontogenetic diet changes, a large size range was sampled. The TrophicCS data set was used to improve understanding of trophic relationships and ecosystem functioning in the Celtic Sea. When you use the data in your publication, we request that you cite this data paper. If you use the present data set (TrophicCS) for the majority of the data analyzed in your study, you may wish to consider inviting at least one author of the core team of this data paper to become a collaborator /coauthor of your paper.

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.

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).

This dataset contains the pictures used for morphometric measurements, as well as the elemental compositon and production rates data, of planktonic Rhizaria. Specimens were collected in the bay of Villefranche-sur-Mer in May 2019 and during the P2107 cruise in the California Current in July-August 2021. Analyses of the data can be found at https://github.com/MnnLgt/Elemental_composition_Rhizaria.

Global wave hindcast (1961-2020) at 1° resolution using CMIP6 wind and sea-ice forcings for ALL (historical), GHG (historical greenhouse-gas-only), AER (historical Anthropogenic-aerosol-only), NAT (historical natural only) scenario.