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.

French intertidal and subtidal Macroalgae taxa data are collected during monitoring surveys on the English Channel / Bay of Biscay coasts.  Protocols are implemented in the Water Framework Directive. Data are transmitted in a Seadatanet format (CDI + ODV) to EMODnet Biology european database. 131 ODV files have been generated from period 01/01/2006 to 31/12/2021.

The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies. This dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution. It has been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. It covers the date range from 2002-2021. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions (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).

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.

The GEBCO_2022 Grid is a global continuous terrain model for ocean and land with a spatial resolution of 15 arc seconds. In regions outside of the Arctic Ocean area, the grid uses as a base Version 2.4 of the SRTM15_plus data set (Tozer, B. et al, 2019). This data set is a fusion of land topography with measured and estimated seafloor topography. Included on top of this base grid are gridded bathymetric data sets developed by the four Regional Centers of The Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2022 Grid represents all data within the 2022 compilation. The compilation of the GEBCO_2022 Grid was carried out at the Seabed 2030 Global Center, hosted at the National Oceanography Centre, UK, with the aim of producing a seamless global terrain model. Outside of Polar regions, the Regional Centers provide their data sets as sparse grids i.e. only grid cells that contain data are populated. These data sets were included on to the base using a remove-restore blending procedure. This is a two-stage process of computing the difference between the new data and the base grid and then gridding the difference and adding the difference back to the existing base grid. The aim is to achieve a smooth transition between the new and base data sets with the minimum of perturbation of the existing base data set. The data sets supplied in the form of complete grids (primarily areas north of 60N and south of 50S) were included using feather blending techniques from GlobalMapper software. The GEBCO_2022 Grid has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and the General Bathymetric Chart of the Oceans (GEBCO). It aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor by 2030 and make it available to all. Funded by the Nippon Foundation, the four Seabed 2030 Regional Centers include the Southern Ocean - hosted at the Alfred Wegener Institute, Germany; South and West Pacific Ocean - hosted at the National Institute of Water and Atmospheric Research, New Zealand; Atlantic and Indian Oceans - hosted at the Lamont-Doherty Earth Observatory, Columbia University, USA; Arctic and North Pacific Oceans - hosted at Stockholm University, Sweden and the Center for Coastal and Ocean Mapping at the University of New Hampshire, USA.

This dataset consists of metatranscriptomic sequencing reads corresponding to coastal micro-eukaryote communities sampled in Western Europe in 2018 and 2019.

The Level 4 merged microwave wind product is a complete set of hourly global 10-m wind maps on a 0.25x0.25 degree latitude-longitude grid, spanning 1 Jan 2010 through the end of 2020. The product combines background neutral equivalent wind fields from ERA5, daily surface current fields from CMEMS, and stress equivalent winds obtained from several microwave passive and active sensors to produce hourly surface current relative stress equivalent wind analyses. The satellite winds include those from recently launched L-band passive sensors capable of measuring extreme winds in tropical cyclones, with little or no degradation from precipitation. All satellite winds used in the analyses have been recalibrated using a large set of collocated satellite-SFMR wind data in storm-centric coordinates. To maximize the use of the satellite microwave data, winds within a 24-hour window centered on the analysis time have been incorporated into each analysis. To accomodate the large time window, satellite wind speeds are transformed into deviations from ERA5 background wind speeds interpolated to the measurement times, and then an optical flow-based morphing technique is applied to these wind speed increments to propagate them from measurement to analysis time. These morphed wind speed increments are then added to the background wind speed at the analysis time to yield a set of total wind speeds fields for each sensor at the analysis time. These individual sensor wind speed fields are then combined with the background 10-m wind direction to yield vorticity and divergence fields for the individual sensor winds. From these, merged vorticity and divergence fields are computed as a weighted average of the individual vorticity and divergence fields. The final vector wind field is then obtained directly from these merged vorticity and divergence fields. Note that one consequence of producing the analyses in terms of vorticity and divergence is that there are no discontinuities in the wind speed fields at the (morphed) swath edges. There are two important points to be noted: the background ERA5 wind speed fields have been rescaled to be globally consistent with the recalibrated AMSR2 wind speeds. This rescaling involves a large increase in the ERA5 background winds beyond about 17 m/s. For example, an ERA5 10 m wind speed of 30 m/s is transformed into a wind speed of 41 m/s, and a wind speed of 34 m/s is transformed into a wind speed of about 48 m/s. Besides the current version of the product is calibrated for use within tropical cyclones and is not appropriate for use elsewhere. This dataset was produced in the frame of ESA MAXSS project. The primary objective of the ESA Marine Atmosphere eXtreme Satellite Synergy (MAXSS) project is to provide guidance and innovative methodologies to maximize the synergetic use of available Earth Observation data (satellite, in situ) to improve understanding about the multi-scale dynamical characteristics of extreme air-sea interaction.

Reef-building species are recognized as having an important ecological role and as generally enhancing the diversity of benthic organisms in marine habitats.  However, although these ecosystem engineers have a facilitating role for some species, they may exclude or compete with others. The honeycomb worm Sabellaria alveolata (Linnaeus, 1767) is an important foundation species, commonly found from northwest Ireland to northern Mauritania (Curd et al., 2020), whose reef structures increase the physical complexity of the marine benthos, supporting high levels of biodiversity. Local patterns and regional differences in taxonomic and functional diversity were examined in honeycomb worm reefs from ten sites along the northeastern Atlantic to explore variation in diversity across biogeographic regions and the potential effects of environmental drivers. To characterize the functional diversity at each site, a biological trait analysis (BTA) was conducted (Statzner et al., 1994). Here we present the functional trait database used for the benthic macrofauna found to live in association with honeycomb worm reefs. Eight biological traits (divided into 32 modalities) were selected (Table 1), providing information linked to the ecological functions performed by the associated macrofauna. The selected traits provide information on: (i) resource use and availability (by the trophic group of species, e.g. Thrush et al. 2006); (ii) secondary production and the amount of energy and organic matter (OM) produced based on the life cycle of the organisms (including longevity, maximum size and mode of reproduction, e.g. (Cusson and Bourget, 2005; Thrush et al., 2006) and; (iii) the behavior of the species in general [i.e. how these species occupy the environment and contribute to biogeochemical fluxes through habitat, movement, and bioturbation activity at different bathymetric levels, e.g. (Solan et al., 2004; Thrush et al., 2006; Queirós et al., 2013). Species were scored for each trait modality based on their affinity using a fuzzy coding approach (Chevenet et al., 1994), where multiple modalities can be attributed to a species if appropriate, and allowed for the incorporation of intraspecific variability in trait expression. The information concerning polychaetes was derived primarily from Fauchald et al (1979) and Jumars et al (2015). Information on other taxonomic groups was obtained either from databases of biological traits (www.marlin.ac.uk/biotic) or publications (Naylor, 1972; King, 1974; Caine, 1977; Lincoln, 1979; Holdich and Jones, 1983; Smaldon et al., 1993; Ingle, 1996; San Martín, 2003; Southward, 2008; Gil, 2011; Leblanc et al., 2011; Rumbold et al., 2012; San Martín and Worsfold, 2015; Jones et al., 2018). Map indicating the locations of the 10 study sites in the UK, France and Portugal within the four biogeographic provinces defined by Dinter (2001). (All sites were sampled in 8 different stations, except for UK4 where 5 stations were sampled).

The Programme Ocean Multidisciplinaire Meso Echelle (POMME) was designed to describe and quantify the role of mesoscale processes in the subduction of mode waters in the Northeast Atlantic. Intensive situ measurements were maintained during 1 year (September 2000 - October 2001), over a 8 degrees square area centered on 18 degrees W, 42 degrees N. In order to synthesized the in-situ physical observations, and merge them with satellite altimetry and surface fluxes datasets, a simplified Kalman filter has been designed. Daily fields of temperature, salinity, and stream function were produced on a regular grid over a full seasonal cycle. We propose here the gridded fields (KA_ files) and the in-situ datasets used by the analysis (Data_ files).

The SWISCA Level 2S (L2S) product provides along-track colocation of SWIM wave measuring instrument onto SCAT scatterometer grid, over the global ocean. SWIM and SCAT are both instruments onboard CFOSAT. CFOSAT (Chinese French Ocean SATellite) is a french-chinese mission launched in 2018, whose aim is to provide wind (SCAT instrument) and wave (SWIM instrument) measurements over the sea surface. The SWISCA L2S product is broken down into three different subproducts: - L2A containing the sigma0 of both SWIM and SCAT - L2B containing the wave parameters measured by SWIM and wind vectors measured by SCAT - AUX containing additional ancillary fields such as sea ice concentration (from CERSAT/SSMI), ocean currents (from CMEMS/GlobCurrent), SST and Wind (from ECMWF), rain rate (from IMERG), and WaveWatch3 wave spectra. All SWIM and ancillary observations are resampled onto SCAT scatterometer's geometry (wind vector cells, WVC). The SWISCA level 2S product is generated in delayed mode, a few days after acquisition. It is intended to foster cross analysis of SWIM and SCAT observations, and their combination to improve the retrieval of both wind and wave parameters. The SWISCA L2S product is generated and distributed by Ifremer / CERSAT in the frame of the Ifremer Wind and Wave Operation Center (IWWOC) co-funded by Ifremer and CNES and dedicated to the processing of the delayed mode data of CFOSAT mission.