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. **This version is now superseded by the version 4 with higher spatial and temporal resolution**

Particularly suited to the purpose of measuring the sensitivity of benthic communities to trawling, a trawl disturbance indicator (de Juan and Demestre, 2012, de Juan et al. 2009) was proposed based on benthic species biological traits to evaluate the sensibility of mega- and epifaunal community to fishing pressure known to have a physical impact on the seafloor (such as dredging and bottom trawling). The selected biological traits were chosen as they determine vulnerability to trawling: mobility, fragility, position on substrata, average size and feeding mode that can easily be related to the fragility, recoverability and vulnerability ecological concepts. The five categories retained are functional traits that were selected based on the knowledge of the response of benthic taxa to trawling disturbance (de Juan et al., 2009). They reflect respectively the possibility to avoid direct gear impact, to benefit from trawling for feeding, to escape gear, to get caught by the net and to resist trawling/dredging action, each of these characteristics being either advantageous or sensitive to trawling. To expand this approach to that proposed by Certain et al. (2015), the protection status of certain species was also indicated. To enable quantitative analysis, a score was assigned to each category: from low sensitivity (0) to high sensitivity (3). Biological traits of species have been defined, from the BIOTIC database (MARLIN, 2014) and from information given by Garcia (2010), Le Pape et al. (2007) and Brind’Amour et al. (2009). For missing traits, additional information from literature has been considered. The protection status of each taxa was also scored: Atlantic species listed in OSPAR List of Threatened and/or Declining Species and Habitats (https://www.ospar.org/work-areas/bdc/species-habitats/list-of-threatened-declining-species-habitats) and Mediterranean species listed in Vulnerable Marine Ecosystems (FAO, 2018 and Oceana, 2017) were scored 3 and other species were scored 1. The scores of 1085 taxa commonly found in bottom trawl by-catch in the southern North Sea, English Channel and north-western Mediterranean were described.

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.

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 2P (L2P) 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, separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2022021 (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).

Raw reads for the assembly of Gambusia holbrooki genome.

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.

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.

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

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.