Dataset summary Plankton and detritus are essential components of the Earth’s oceans influencing biogeochemical cycles and carbon sequestration. Climate change impacts their composition and marine ecosystems as a whole. To improve our understanding of these changes, standardized observation methods and integrated global datasets are needed to enhance the accuracy of ecological and climate models. Here, we present a global dataset for plankton and detritus obtained by two versions of the Underwater Vision Profiler 5 (UVP5). This release contains the images classified in 33 homogenized categories, as well as the metadata associated with them, reaching 3,114 profiles and ca. 8 million objects acquired between 2008-2018 at global scale. The geographical distribution of the dataset is unbalanced, with the Equatorial region (30° S - 30° N) being the most represented, followed by the high latitudes in the northern hemisphere and lastly the high latitudes in the Southern Hemisphere. Detritus is the most abundant category in terms of concentration (90%) and biovolume (95%), although its classification in different morphotypes is still not well established. Copepoda was the most abundant taxa within the plankton, with Trichodesmium colonies being the second most abundant. The two versions of UVP5 (SD and HD) have different imagers, resulting in a different effective size range to analyse plankton and detritus from the images (HD objects >600 µm, SD objects >1 mm) and morphological properties (grey levels, etc.) presenting similar patterns, although the ranges may differ. A large number of images of plankton and detritus will be collected in the future by the UVP5, and the public availability of this dataset will help it being utilized as a training set for machine learning and being improved by the scientific community. This will reduce uncertainty by classifying previously unclassified objects and expand the classification categories, ultimately enhancing biodiversity quantification. Data tables The data set is organised according to: - samples : Underwater Vision Profiler 5 profiles, taken at a given point in space and time. - objects : individual UVP images, taken at a given depth along the each profile, on which various morphological features were measured and that where then classified taxonomically in EcoTaxa. samples and objects have unique identifiers. The sample_id is used to link the different tables of the data set together. All files are Tab separated values, UTF8 encoded, gzip compressed. samples.tsv.gz - sample_id    <int>    unique sample identifier - sample_name    <text>    original sample identifier - project    <text>    EcoPart project title - lat, lon    <float>    location [decimal degrees] - datetime    <text>    date and time of start of profile [ISO 8601: YYYY-MM-DDTHH:MM:SSZ] - pixel_size    <float>    size of one pixel [mm] - uvp_model    <text>    version of the UVP: SD: standard definition, ZD: zoomed, HD: high definition samples_volume.tsv.gz Along a profile, the UVP takes many images, each of a fixed volume. The profiles are cut into 5 m depth bins in which the number of images taken is recorded and hence the imaged volume is known. This is necessary to compute concentrations. - sample_id    <int>    unique sample identifier - mid_depth_bin    <float>    middle of the depth bin (2.5 = from 0 to 5 m depth) [m] - water_volume_imaged    <float>    volume imaged = number of full images × unit volume [L] objects.tsv.gz - object_id    <int>     unique object identifier - object_name    <text>     original object identifier - sample_id    <int>     unique sample identifier - depth    <float>    depth at which the image was taken [m] - mid_depth_bin    <float>    corresponding depth bin [m]; to match with samples_volumes - taxon    <text>     original taxonomic name as in EcoTaxa; is not consistent across projects - lineage    <text>     taxonomic lineage corresponding to that name - classif_author    <text>     unique, anonymised identifier of the user who performed this classification - classif_datetime    <text>     date and time at which the classification was - group    <text>     broader taxonomic name, for which the identification is consistent over the whole dataset - group_lineage    <text>     taxonomic lineage corresponding to this broader group - area_mm2    <float>    measurements on the object, in real worl units (i.e. comparable across the whole dataset) … - major_mm    <float> - area    <float>    measurements on the objet, in [pixels] and therefore not directly comparable among the different UVP models and units - mean    <float> … - skeleton_area    <float> properties_per_bin.tsv.gz The information above allows to compute concentrations, biovolumes, and average grey level within a given depth bin. The code to do so is in `summarise_objects_properties.R`. - sample_id    <int>     unique sample identifier - depth_range    <text>     range of depth over which the concentration/biovolume are computed: (start,end], in [m] where `(` means not including, `]` means including - group    <text>     broad taxonomic group - concentration    <float>    concentration [ind/L] - biovolume    <float>    biovolume [mm3/L] - avg_grey    <float>    average grey level of particles [no unit; 0 is black, 255 is white] ODV_biovolumes.txt, ODV_concentrations.txt, ODV_grey_levels.txt This is the same information as above, formatted in a way that Ocean Data View https://odv.awi.de can read. In ODV, go to Import > ODV Spreadsheet and accept all default choices. Images The images are provided in a separate, much larger, zip file. They are stored with the format `sample_id/object_id.jpg`, where `sample_id` and `object_id` are the integer identifiers used in the data tables above.

A world deep displacement dataset comprising more than 1600 000 Argo floats deep displacements, has been produced from the global Argo float database (GDAC). ANDRO dataset was completed over the period 2000-2009, then was partially but yearly updated since 2010. ANDRO actual contents and format is described in the user guide, which must be carefully read before using ANDRO (ANDRO format is also described in Ollitrault M. et al (2013)). One important feature of ANDRO is that the pressures measured during float drifts at depth, and suitably averaged are preserved in ANDRO (see Figure 2). To reach this goal, it was necessary to reprocess most of the Argo raw data, because of the many different decoding versions (roughly 100) not always applied by the DACs to the displacement data because they were mainly interested in the p,t,S profiles. The result of our work was the production of comprehensive files, named DEP (for déplacements in French), containing all the possibly retrievable float data. For detailed information and status of the last released ANDRO product, please visit the dedicated Argo France web page: https://www.umr-lops.fr/SNO-Argo/Products/ANDRO-Argo-floats-displacements-Atlas

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity, contaminants and marine litter. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on floating micro-litter. This dataset is the result of a validation and harmonisation process of the datasets concerning floating micro-litter present in EMODnet Chemistry. The datasets concerning micro-litter are automatically harvested and the resulting collections are harmonised and validated using ODV Software and following a common methodology for all sea regions. Parameter names are based on P01 vocabulary, which relates to BODC Parameter Usage Vocabulary and is available at: https://vocab.nerc.ac.uk/search_nvs/P01/ This process was performed by ‘Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)’ from Italy. Harmonisation means that: (1) unit conversion is carried out to express variables with a limited set of measurement units and (2) merging of variables described by different “local names”, but corresponding exactly to the same concepts in BODC P01 vocabulary. The harmonised dataset can be downloaded as ODV collection that can be opened with ODV software for visualization (More information can be found at: https://www.seadatanet.org/Software/ODV ). The same dataset is offered as spreadsheet (txt format, tab separated values) where the values of the categories for the following reported parameters (type, shape, size, color, transparency and material) have been uniformed as labelled in the SeaDataNet H01, H02, H03, H04, H05, H06 vocabularies (https://vocab.seadatanet.org/search ). This format is more adapted to worksheet applications (e.g. LibreOffice Calc).

The Mediterranean Sea is a natural laboratory to address questions about the formation and evolution of continental margins and the relationship between surface and deep processes. Different regional to local events have influenced the Neogene stratigraphic evolution of the Valencia and Menorca basins. The evaporites deposited during the Messinian Salinity Crisis (MSC) strongly impact its sedimentary and geomorphological evolution. Here we present a compilation of the main regional seismic stratigraphic markers from the continental platform to the deep sea. We provide in xyz format (z in second twt) the original picking files, (not interpolated) and interpolated grid of: i) the top of the Mesozoic formation, the base of the Neogene formations including the early Miocene volcanic features, ii) the top Burdigalian, Langhian, and Serravallian seismic horizons, iii) the seismic horizons related to the Messinian Salinity Crisis, iv) the Pliocene and Pleistocene seismic horizons v) the depth of the Seafloor. The available reflection seismic dataset results from the compilation and processing of vintage seismic profiles of previous works and from the Instituto Geologico y Minero de Espana (IGME). This compilation is currently the first available in literature and provides a useful contribution to the scientific community working on sedimentary, tectonics and geodynamics within the Western Mediterranean basins.

EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification, contaminants and litter. The chosen parameters are relevant for the Marine Strategy Framework Directive (MSFD), in particular for descriptors 5, 8, 9 and 10. The datasets contain standardized, harmonized and validated data collections from seafloor litter. Datasets concerning seafloor litter data are loaded in a central database after a semi-automated validation phase. Once loaded, a data assessment is performed in order to check data consistency and potential errors are corrected thanks to a feedback loop with data originators. EMODnet seafloor litter data and database are hosted and maintained by ‘Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)’ from Italy. For seafloor litter, the harmonized datasets contain all unrestricted EMODnet Chemistry data on seafloor litter data. Data are formatted following Guidelines and forms for gathering marine litter data, which can be found at: https://dx.doi.org/10.6092/15c0d34c-a01a-4091-91ac-7c4f561ab508 The updated vocabularies of admitted values are available at: https://vocab.seadatanet.org/search https://vocab.ices.dk/ The harmonized datasets can be downloaded as EMODnet Sea-floor litter data format version 1.0, which is a csv file, tab separated values.

In October 2019 we chose 15 sites from the 2019 EVHOE survey for environmental DNA (eDNA) sampling. The French international EVHOE bottom trawl survey is carried out annually during autumn in the BoB to monitor demersal fish resources. At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11.25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 l min–1 ), a VigiDNA 0.20 μm filtration capsule (SPYGEN, le Bourget du Lac, France), and disposable sterile tubing. Each filtration device filtered the water contained in two plastic bags (22.5 l), which represent two replicates per sampling site. We followed a rigorous protocol to avoid contamination during fieldwork, using disposable gloves and single-use filtration equipment and plastic bags to process each water sample. At the end of each filtration, we emptied the water inside the capsule that we replaced by 80 ml of CL1 conservation buffer and stored the samples at room temperature following the specifications of the manufacturer (SPYGEN, Le Bourget du Lac, France). We processed the eDNA capsules at SPYGEN, following the protocol proposed by Polanco-Fernández et al., (2020). Half of the extracted DNA was processed by Sinsoma using newly developped ddPCR assays for European seabass (Dicentrachus labrax), European hake (Merluccius merluccius) and blackspot seabream (Pagellus bogaraveo).  The other half of the extracted DNA was analysed using metabarcoding with teleo primer. The raw metabarcoding data set is available at https://www.doi.org/10.16904/envidat.442 Bottom trawling using a GOV trawl was carried out before or after water sampling. The catch was sorted by species and catches in numbers and weight were recorded. No blackspot seabream individuals were caught.   Data content: * ddPCR/: contains the ddPCR counts and DNA concentrations for each sample and species. * SampleInfo/: contains the filter volume for each eDNA sample. * StationInfo/: contains metadata related to the data collected in the field for each filter. * Metabarcoding/: contains metabarcoding results for teleoprimer. * Trawldata/: contains catch data in numbers and weight (kg).      

Long-term time series of coliform bacteria concentration (fecal coliform or Escherichia coli) in shellfish in four submarine areas (North Sea/Channel, Britany, Atlantic, Mediterranean).

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.

This visualization product displays the density of seafloor litter per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys. In cases where the wingspread and/or the number of items were/was unknown, it was not possible to use the data because these fields are needed to calculate the density. Data collected before 2011 are concerned by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using the following formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area was calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities were calculated on each trawl and year using the following computation: Density (number of items per km²) = ∑Number of items / Swept area (km²) Percentiles 50, 75, 95 & 99 were calculated taking into account data for all years. More information on data processing and calculation are detailed in the attached document. 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 Marine Litter Database for this area.

Serveur wms public de l'Ifremer - projet REPAMO