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.

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 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).

This dataset provides surface Stokes drift as retrieved from the wave energy spectrum computed by the spectral wave model WAVEWATCH-III (r), under NOAA license, discretized in wave numbers and directions and the water depth at each location. It is estimated at the sea surface and expressed in m.s-1. WAVEWATCH-III (r) model solves the random phase spectral action density balance equation for wavenumber-direction spectra. Please refer to the WAVEWATCH-III User Manual for fully detailed description of the wave model equations and numerical approaches. 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 Ifremer / LOPS and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

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.

The SARWAVE project is developing a new sea state processor from SAR images to be applied over open ocean, sea ice, and coastal areas, and exploring potential synergy with other microwave and optical EO products.

We genotyped 1680 thornback ray Raja clavata sampled in the Bay of Biscay using a DNA chip described in Le Cam et al. (2019). After quality control 4604 SNPs were retained for identifying potential sex-linked SNPs using three methods: i) identification of excess of heterozygotes in one sex, ii) FST outlier analysis between the two sexes and iii) neuronal net modelling. Genotype coding: 0 homozygous for major allele, 1 heterozygous, 2 homozygous for minor allele. Flanking DNA sequences of SNPs identified with methods i) and ii) are also provided.  

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.

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.