The Ocean Colour Climate Change Initiative project aims to: Develop and validate algorithms to meet the Ocean Colour GCOS ECV requirements for consistent, stable, error-characterized global satellite data products from multi-sensor data archives. Produce and validate, within an R&D context, the most complete and consistent possible time series of multi-sensor global satellite data products for climate research and modelling. Optimize the impact of MERIS data on climate data records. Generate complete specifications for an operational production system. Strengthen inter-disciplinary cooperation between international Earth observation, climate research and modelling communities, in pursuit of scientific excellence. The ESA OC CCI project is following a data reprocessing paradigm of regular re-processings utilising on-going research and developments in atmospheric correction, in-water algorithms, data merging techniques and bias correction. This requires flexibility and rapid turn-around of processing of extensive ocean colour datasets from a number of ESA and NASA missions to both trial new algorithms and methods and undertake the complete data set production. Read more about the Ocean Colour project on ESA's project website. https://climate.esa.int/en/projects/ocean-colour/.

In integrated multi-trophic aquaculture (IMTA), multiple aquatic species from different trophic levels are farmed together. Thus, waste from one species can be used as input (fertiliser and food) for another species. The EU-funded ASTRAL project will develop IMTA production chains for the Atlantic markets. Focusing on a regional challenge-based perspective, it will bring together labs in Ireland and Scotland (open offshore labs), South Africa (flow-through inshore) and Brazil (recirculation inshore) as well as Argentina (prospective IMTA lab). The aim is to increase circularity by as much as 60 % compared to monoculture baseline aquaculture and to boost revenue diversification for aquaculture producers. ASTRAL will share, integrate, and co-generate knowledge, technology and best practices fostering a collaborative ecosystem along the Atlantic.

The present data set concerne metabarcoding raw reads produced using 4 different PCR targeting polymerase or capside coding region of the genoyupe I and II of norovirus. Test samples of norovirus with serial dilutions in pure water and after a bio-accumulation in oysters. Sequencing was made after VirCapSeq-VERT approach.

The SDC_MED_DP2 product contains 55 sliding decadal temperature fields (1955-1964, 1956-1965, 1957-1966, …, 2009-2018) at 1/8° horizontal resolution obtained in the 0-2000m layer and two derived OHC annual anomaly estimates for the 0-700m and the 0-2000m layers. Sliding decades of annual Temperature fields were obtained from an integrated Mediterranean Sea dataset covering the time period 1955-2018, which combines data extracted from SeaDataNet infrastructure at the end of July 2019 (SDC_MED_DATA_TS_V2, https://doi.org/10.12770/3f8eaace-9f9b-4b1b-a7a4-9c55270e205a) and the Coriolis Ocean Dataset for Reanalysis (CORA 5.2, accessed in July 2020, https://archimer.ifremer.fr/doc/00595/70726/). The resulting annual OHC anomaly time series span the 1960-2014 period. The analysis was performed with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.6.1.

Itinéraires de randonnée et pistes cyclables du Département des Landes. Le Département des Landes propose 3 500 km d’itinéraires inscrits au Plan départemental des itinéraires de promenade et de randonnée (PDIPR) et près de 2 500 km d’itinéraires cyclables. Ces circuits sont entretenus et balisés avec des niveaux de difficultés mentionnés sur chaque parcours.

The SDC_MED_CLIM_TS_V2 product contains Temperature and Salinity Climatologies for Mediterranean Sea: monthly and seasonal fields for time periods 1955-2018, 1955-1984 and 1985-2018 and seasonal fields for 6 decades covering the time period 1955 to 2018. The climatic fields were computed from an integrated Mediterranean Sea data set that combines data extracted from SeaDataNet infrastructure (SDC_MED_DATA_TS_V2, https://doi.org/10.12770/2a2aa0c5-4054-4a62-a18b-3835b304fe64) and Coriolis Ocean Dataset for Reanalysis (CORA5.2) distributed by the Copernicus Marine Service (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b). The computation was done with the DIVAnd (Data-Interpolating Variational Analysis), version 2.4.0.

Level 3, four times a day, sub-skin Sea Surface Temperature derived from AVHRR on Metop satellites and VIIRS or AVHRR on NOAA and NPP satellites, over North Atlantic and European Seas and re-projected on a polar stereographic at 2 km resolution, in GHRSST compliant netCDF format. This catalogue entry presents NOAA-20 North Atlantic Regional Sea Surface Temperature. SST is retrieved from infrared channels using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements. The product format is compliant with the GHRSST Data Specification (GDS) version 2.Users are advised to use data only with quality levels 3,4 and 5.

This dataset is the coastal zone land surface region from Europe, derived from the coastline towards inland, as a series of 10 consecutive buffers of 1km width each. The coastline is defined by the extent of the Corine Land Cover 2018 (raster 100m) version 20 accounting layer. In this version all Corine Land Cover pixels with a value of 523, corresponding to sea and oceans, were considered as non-land surface and thus were excluded from the buffer zone.

Level 2 sub-skin Sea Surface Temperature derived from AVHRR on Metop, global and provided in full-resolution swath (1 km at nadir), in GHRSST compliant netCDF format. The satellite input data has successively come from Metop-A, Metop-B and Metop-C level 1 data processed at EUMETSAT. SST is retrieved from AVHRR infrared channels (3.7, 10.8 and 12.0 µm) using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements.The product format is compliant with the GHRSST Data Specification (GDS) version 2. Users are advised to use data only with quality levels 3,4 and 5.

This study gathers multi-year environmental sequencing datasets generated within the French ROME pilot observatory network. It includes eDNA metabarcoding and RNA-based analyses from water samples, oyster tissues, and viral fractions collected across four French estuarine ecosystems between 2020 and 2023, supporting integrated monitoring of coastal microbiomes and microbial hazards.