This dataset contains maps of 13 anthropogenic pressures (one pressure per map), modeled according to the methodology used by Holon et al. (2015), and updated with the latest available data on human activities in 2021. This dataset is available for visualization on the Medtrix cartographic platform (http://www.medtrix.fr, free access after registration). More details can be found on the methodology and modeling approach on the medtrix website (https://medtrix.fr/en/portfolio_page/impact-2/) and on the 2018 IMPACT update report (only available in French at the moment). The modeling and mapping was performed using R software V 4.0. Table 1 lists the modeled anthropogenic pressures, the modeling approach and the data used. The spatial resolution of the raster layers is 500 m, the coordinate reference system (CRS) of the raster layers is RGF93 / Lambert-93 (EPSG 2154). The values of each layer range from 0 (no pressure) to 1 (max modeled pressure over the study area), and is unitless. All pressures are modeled over the spatial extent of the French mediterranean coastal habitat map (www.medtrix.fr, “DONIA expert” project), except for professional fishing and marine traffic, that are modeled over the entire French Mediterranean sea. The ZIP archive contains a tif raster composed of 13 layers corresponding to the 13 modeled pressures. All data acquired are publicly accessible without any restriction (under CC-BY licence).  

This visualization product displays nets locations where research and monitoring protocols have been applied to collate data on microlitter. Mesh size used with these protocols have been indicated with different colors in the map. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to research and monitoring protocols as MSFD monitoring. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area.

Numerous reef-forming species have declined dramatically over the last century. Many of these declines have been insufficiently documented due to anecdotal or hard-to-access information. The Ross worm Sabellaria spinulosa (L.) is a tube-building polychaete that can form large mostly subtidal reefs, providing important ecosystem services such as coastal protection and habitat provision. It ranges from Scotland to Morocco and into the Mediterranean as far as the Adriatic, yet little is known about its distribution outside of the North & Wadden Seas, where it is protected under the OSPAR & HELCOM regional sea conventions respectively. As a result, online marine biodiversity information systems currently contain haphazardly distributed records of S. spinulosa. One of the objectives of the REEHAB project (http://www.honeycombworms.org) was to combine historical records with contemporary data to document changes in the distribution and abundance of the two Sabellaria species found in Europe, S. alveolata and S. spinulosa. Here we publish the result of the curation of 555 S. spinulosa sources, gathered from literature, targeted surveys, local conservation reports, museum specimens, personal communications by authors  their research teams, national biodiversity information systems (i.e. the UK National Biodiversity Network (NBN), www.nbn.org.uk) and validated citizen science observations (i.e. https://www.inaturalist.org). 56% of these records were not previously referenced in any online information system. Additionally, historic samples from Gustave Gilson were scanned for S. spinulosa information and manually entered.   The original taxonomic identification of the 40,261 S. spinulosa records has been kept. Some identification errors may however be present, particularly in the English Channel and Mediterranean where intertidal and shallow subtidal records can be mistaken for Sabellaria alveolata. A further 229 observations (16 sources) are recorded as ‘Sabellaria spp.’ as the available information did not provide an identification down to species level. Many sources reported abundances based on the semi-quantitative SACFOR scale whilst others simply noted its presence, and others still verified both its absence and presence. The result is a curated and comprehensive dataset spanning over two centuries on the past and present global distribution and abundance of S. spinulosa. Sabellaria spinulosa records projected onto a 50km grid. When SACFOR scale abundance scores were given to occurrence records, the highest abundance value per grid cell was retained.

The raster corresponds to the predicted Mediterranean bioregions of megabenthic communities.

Plankton was sampled with various nets, from bottom or 500m depth to the surface, in many oceans of the world. Samples were imaged with a ZooScan. The full images were processed with ZooProcess which generated regions of interest (ROIs) around each individual object and a set of associated features measured on the object (see Gorsky et al 2010 for more information). The same objects were re-processed to compute features with the scikit-image toolbox http://scikit-image.org. The 1,451,745 resulting objects were sorted by a limited number of operators, following a common taxonomic guide, into 98 taxa, using the web application EcoTaxa http://ecotaxa.obs-vlfr.fr. For the purpose of training machine learning classifiers, the images in each class were split into training, validation, and test sets, with proportions 70%, 15% and 15%. The folder ZooScanNet_data.tar contains : taxa.csv.gz Table of the classification of each object in the dataset, with columns : - objid: unique object identifier in EcoTaxa (integer number) - taxon_level1: taxonomic name corresponding to the level 1 classification - lineage_level1: taxonomic lineage corresponding to the level 1 classification - taxon_level2: name of the taxon corresponding to the level 2 classification  - plankton: if the object is a plankton or not (boolean) - set: class of the image corresponding to the taxon (train : training, val : validation, or test) - img_path: local path of the image corresponding to the taxon (of level 1), named according to the object id features_native.csv.gz Table of metadata of each object including the different features processed by ZooProcess. All features are computed on the object only, not the background. All area/length measures are in pixels. All grey levels are in encoded in 8 bits (0=black, 255=white). With columns: - objid: unique object identifier in EcoTaxa (integer number) And 48 features: - area - mean - stddev - mode - min/max - perim. - width,height  - major,minor - circ. - feret - intden - median - skew,kurt - %area - area_exc - fractal - skelarea - slope - histcum1,2,3 - nb1,2,3 - symetrieh,symetriev - symetriehc,symetrievc - convperim,convarea - fcons - thickr:  - esd - elongation - range - centroids - sr - perimareaexc - feretareaexc - perimferet/perimmajor - circex - cdexc See the “ZooScan” sheet - OBJECT metadata, annotation and measurements - , at https://doi.org/10.5281/zenodo.14704250 for definitions. features_skimage.csv.gz Table of morphological features recomputed with skimage.measure.regionprops on the ROIs produced by ZooProcess. See http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops for documentation. inventory.tsv Tree view of the taxonomy and number of images in each taxon, displayed as text. With columns : - lineage_level1: taxonomic lineage corresponding to the level 1 classification - taxon_level1: name of the taxon corresponding to the level 1 classification - n: number of objects in each taxon class          2. Second folder ZooScanNet_imgs.tar contains : imgs Directory containing images of each object, named according to the object id objid and sorted in subdirectories according to their taxon.         3. And : map.png Map of the sampling locations, to give an idea of the diversity sampled in this dataset.  

Rapid changes in ocean circulation and climate have been observed in marine-sediment and ice cores over the last glacial period and deglaciation, highlighting the non-linear character of the climate system and underlining the possibility of rapid climate shifts in response to anthropogenic greenhouse gas forcing. To date, these rapid changes in climate and ocean circulation are still not fully explained. One obstacle hindering progress in our understanding of the interactions between past ocean circulation and climate changes is the difficulty of accurately dating marine cores. Here, we present a set of 92 marine sediment cores from the Atlantic Ocean for which we have established age-depth models that are consistent with the Greenland GICC05 ice core chronology, and computed the associated dating uncertainties, using a new deposition modeling technique. This is the first set of consistently dated marine sediment cores enabling paleoclimate scientists to evaluate leads/lags between circulation and climate changes over vast regions of the Atlantic Ocean. Moreover, this data set is of direct use in paleoclimate modeling studies.

This visualization product displays the density of floating micro-litter per net normalized per m³ per year from specific protocols different from research and monitoring protocols. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to a very specific protocol such as the Volvo Ocean Race (VOR) or Oceaneye. Densities were calculated for each net using the following calculation: Density (number of particles per m³) = Micro-litter count / Sampling effort (m³) When the number of micro-litters was not filled, it was not possible to calculate the density. Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area.

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). '''CMEMS KEY FINDINGS''' Since the year 2005 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.0 ± 0.2 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00239

EVHOE (« Evaluation Halieutique de l’Ouest Européen ») surveys provide observational data on bentho-demersal communities on the continental shelves of the Bay of Biscay and the Celtic Sea for more than 30 years. The surveys operate a standardized bottom trawling gear and are conducted from 15 to 600 m depth, usually in the fourth quarter of the year, starting at the end of October. The main objectives are the monitoring of 22 commercial stocks of fish species and 10 cephalopods from the North-East Atlantic. The dataset also provide a description of regional diversity, including 250 taxa of fish, 45 taxa of cephalopods and others “commercial” invertebrates and, from 2008, more than 350 other taxa of benthic invertebrates. The acquisition of this dataset, organised by IFREMER, is steered by the IBTS working group organised within the framework of ICES. It is being funded by the European DCMAP programme, in coordination with the French Directorate-General for Maritime Affairs, Fisheries and Aquaculture (DGAMPA). This dataset is of great interest for the long-term monitoring of the continental shelves of the Bay of Biscay and the Celtic Sea. Moreover, on a larger scale, by being integrated into a European network of bottom trawl surveys, these data play an essential role in studying the evolution of ecosystems from continental shelves to the scale of the eastern North Atlantic. From April 2025, the proposed data have been updated in the latest standard format recognised by IFREMER (‘ELFIC’ format). The 5 data tables are compiled in a .zip file which also contains a document detailing the content of each table and their respective data fields.

This visualization product displays the density of floating micro-litter per net normalized in grams per km² per year from specific protocols different from research and monitoring protocols. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to a very specific protocol such as the Volvo Ocean Race (VOR) or Oceaneye. Densities were calculated for each net using the following calculation: Density (weight of particles per km²) = Micro-litter weight / (Sampling effort (km) * Net opening (cm) * 0.00001) When the weight of microlitters or the net opening was not filled, it was not possible to calculate the density. Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area.