This dataset is associated with the publication by Nicole Couto, Jennifer MacKinnon, Harper Simmons, and Matthew Alford, "Near-bottom baroclinic and submesoscale instability at the Chukchi shelfbreak." Four FastCTD sections and one Modular Microstructure Profiler (MMP) were collected crossing the Chukchi shelf break in September 2018 from the R/V Sikuliaq. Current velocities were measured simultaneously from two hull-mounted ADCPS: an RDI Workhorse 300 kHz and an RDI Ocean Surveyor 75 kHz. Important Note: This submission has been initially submitted to SEA scieNtific Open data Edition (SEANOE) publication service and received the recorded DOI. The metadata elements have been further processed (refined) in EMODnet Ingestion Service in order to conform with the Data Submission Service specifications.

The Copernicus High Resolution Water and Wetness (WAW) 2015 layer is a thematic product showing the occurrence of water and wet surfaces over the period from 2009 to 2015 for the EEA38 area and the United Kingdom . This metadata corresponds to the aggregation of the 20m classified product into a 100m raster. The production of the High Resolution Water and Wetness layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. Two WAW products are available: - The main Water and Wetness (WAW) product, with defined classes of (1) permanent water, (2) temporary water, (3) permanent wetness and (4) temporary wetness. - The additional expert product: Water and Wetness Probability Index (WWPI). The products show the occurrence of water and indicate the degree of wetness in a physical sense, assessed independently of the actual vegetation cover and are thus not limited to a specific land cover class and their relative frequencies. Data is provided as a mosaic of the full area, and as tiles with a side length of 1000 km x 1000 km. In 2020, due to methodological improvements, the temporary wet class has been reprocessed during the update for the 2018 reference year.

This product displays for Benzo(a)pyrene, positions with values counts that have been measured per matrix for each year and are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for every available year.

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. 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 eutrophication and acidity, and covers the Greater North Sea and Celtic Seas. Data were aggregated and quality controlled by 'Aarhus University, Department of Bioscience, Marine Ecology Roskilde' in Denmark. ITS-90 water temperature and water body salinity variables have also been included ('as are') to complete the eutrophication and acidity data. If you use these variables for calculations, please refer to SeaDataNet for the quality flags: https://www.seadatanet.org/Products/Aggregated-datasets . Regional datasets concerning eutrophication and acidity are automatically harvested, and the resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.13120/8xm0-5m67 ). Parameter names are based on P35 vocabulary, which relates to EMODnet Chemistry aggregated parameter names and is available at: https://vocab.nerc.ac.uk/search_nvs/P35/ . When not present in original data, water body nitrate plus nitrite was calculated by summing all nitrate and nitrite parameters. The same procedure was applied for water body dissolved inorganic nitrogen (DIN), which was calculated by summing all nitrate, nitrite, and ammonium parameters. Concentrations per unit mass were converted to a unit volume using a constant density of 1.025 kg/L. The aggregated dataset can also be downloaded as an ODV collection and spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported to ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV ).

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

This visualization product displays the spatial distribution of seafloor litter density 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 fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this 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 is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density (number of items per km²) = ∑Number of items / Swept area (km²) Then a grid with 30km x 30km cells is used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. More information on data processing and calculation are detailed in the document attached. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area. This work is based on the work presented in the following scientific article: O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.

This product displays for Triphenyltin, positions with values counts that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for all available years.

Abstract: Happywhale.com is a resource to help you know whales as individuals, and to benefit conservation science with rich data about individual whales. Original provider: Happywhale Dataset credits: Happywhale and contributors Supplemental information: Sightings and images were submitted to Happywhale by contributors. A portion of the Happywhale data were transferred to OBIS-SEAMAP upon the agreement between Happywhale and OBIS-SEAMAP. There may be duplicate records among Happywhale datasets and other OBIS-SEAMAP datasets. The precision of date/time vary per record. Some records have date accuracy up to year only. This dataset includes sightings and photos from the following 50 contributors in alphabetic order: Adelie Xiaohang Li; Adrian Boyle; Alex Cowan; Andrew Emmerson; Ann; Annette Bombosch; Barbara Messner; Bart Van Gelder; Cees Tineke; Christian Engelke; Conor Ryan; Doug Cheeseman; Doug Gould; Elke; Greta Henderson; Hadleigh Measham; Hannah Brightley; Hans Verdaat; Heidi Krajewsky; Hondius; Jamie Coleman; Jeff Higgott; Jeff Reynolds; Jérôme JACOB; Joel Moore; Joy van der Beek; Katharina Stoll; Keely Crowder; Kerstin Langenberger; Léa Zinsli; Loes de Heus; Marian Herz; Marijke Nita de Boer; Marilia Olio; Marit Pedersen; Mary Want; Menno Schaefer; Nacho Oria; Olivier Blaud; Petra Glardon; Philip Stone; Philip van Dueren; Phil Schultz; Pippa Low; Rémi Bigonneau; Sara Jenner; Sophie Ballagh; Steffo Polar; Tobias Brehm; Ulf Velander

Happywhale.com is a resource to help you know whales as individuals, and to benefit conservation science with rich data about individual whales. Original provider: Happywhale Dataset credits: Happywhale and contributors Supplemental information: Sightings and images were submitted to Happywhale by contributors. A portion of the Happywhale data were transferred to OBIS-SEAMAP upon the agreement between Happywhale and OBIS-SEAMAP. There may be duplicate records among Happywhale datasets and other OBIS-SEAMAP datasets. The precision of date/time vary per record. Some records have date accuracy up to year only. This dataset includes sightings and photos from the following 2 contributors in alphabetic order: Marilia Olio; Per Nikolaj Bukh

EMODnet Physics - Arctic Sea Ice Extend [2009 - ongoing] - Annual Max and Min contours. This product uses the Copernicus Marine Service product id. SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001