Physical and other data were collected from bottle and XBT casts from the AGS NO 2 and other platforms from the NW Pacific (limit-180). Data were collected by the Fisheries Agency of Japan (JFA) and other institutions from 07 January 1967 to 02 March 1984. Data were processed by NODC to the NODC standard Station Data II Output Format (SD2) and the Universal Bathythermograph Output Format (UBT). Full format descriptions are available from NODC at www.nodc.noaa.gov/.

The SD2 file format is used for physical-chemical oceanographic data recorded at discrete depth levels. Most of the observations were made using multi- bottle Nansen casts or other types of water samplers. A small amount (about 5%), were obtained using electronic CTD (conductivity-temperature-depth) or STD (salinity-temperature-depth) recorders. The CTD/STD data were reported to NODC at depth levels equivalent to Nansen cast data, however, and have been processed and stored the same as the Nansen data. Cruise information, position, date and time are reported for each station. Each station contains the measurements taken at observed depth levels, but also includes data values interpolated to a set of standard depth levels.

The UBT format contains temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Cruise information, position, date and time were reported for each observation. The data records are comprised of pairs of temperature-depth values. The XBT data files contain temperature values at originators defined depths. Standard XBTs can obtain profiles to depths of either 450 or 760 m. Special instruments permit measurements to be obtained to 1830 m.

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 dataset contains standardized, harmonized and validated data collections from beach litter (monitoring and other sources). Datasets concerning beach and 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. For beach litter, the harmonized datasets contain all unrestricted EMODnet Chemistry data on beach litter, including monitoring data, data from cleaning surveys and data from research. A relevant part of the monitoring data has been considered for assessment purposes by the European institutions and therefore is tagged as MSFD_monitoring. EMODnet beach litter data and databases are hosted and maintained by 'Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)' from Italy. Data are formatted following Guidelines and forms for gathering marine litter data, which can be found at: https://doi.org/10.6092/15c0d34c-a01a-4091-91ac-7c4f561ab508. The updated vocabularies of admitted values are available in https://nodc.ogs.it/marinelitter/vocab. The harmonized datasets can be downloaded as EMODnet Beach litter data format Version 7.0, which is a spreadsheet file composed of 4 sheets: beach metadata, survey metadata, animals and litter.

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 dataset contains standardized, harmonized and validated data collections from beach litter (monitoring and other sources). Datasets concerning beach and 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. For beach litter, the harmonized datasets contain all unrestricted EMODnet Chemistry data on beach litter, including monitoring data, data from cleaning surveys and data from research. A relevant part of the monitoring data has been considered for assessment purposes by the European institutions and therefore is tagged as MSFD_monitoring. EMODnet beach litter data and databases are hosted and maintained by 'Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)' from Italy. Data are formatted following Guidelines and forms for gathering marine litter data, which can be found at: https://doi.org/10.6092/15c0d34c-a01a-4091-91ac-7c4f561ab508 The updated vocabularies of admitted values are available at: https://nodc.ogs.it/marinelitter/vocab The harmonized datasets can be downloaded as EMODnet Beach litter data format Version 7.0, which is a spreadsheet file composed of 4 sheets: beach metadata, survey metadata, animals and litter. Local_CDI field in the survey metadata sheet allows to retrieve the original data.

Synthetic Aperture Radar (SAR), renowned for its all-weather monitoring capability and high-resolution imaging characteristics, plays a pivotal role in ocean resource exploration, environmental surveillance, and maritime security. It has become a fundamental technological support in marine science research and maritime management. However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. To address these challenges, this paper proposes AC-YOLO, a novel lightweight SAR ship detection model based on YOLO11. Specifically, we design a lightweight cross-scale feature fusion module that adaptively fuses multi-scale feature information, enhancing small target detection while reducing model complexity. Additionally, we construct a hybrid attention enhancement module, integrating convolutional operations with a self-attention mechanism to improve feature discrimination without compromising computational efficiency. Furthermore, we propose an optimized bounding box regression loss function, the Minimum Point Distance Intersection over the Union (MPDIoU), which establishes multi-dimensional geometric metrics to accurately characterize discrepancies in overlap area, center distance, and scale variation between predicted and ground truth boxes. Experimental results demonstrate that, compared with the baseline YOLO11 model, AC-YOLO reduces parameter count by 30.0% and computational load by 15.6% on the SSDD dataset, with an average precision (AP) improvement of 1.2%; on the HRSID dataset, the AP increases by 1.5%. This model effectively reconciles the trade-off between complexity and detection accuracy, providing a feasible solution for deployment on edge computing platforms. The source code for the AC-YOLO model is available at: https://github.com/He-ship-sar/ACYOLO.

This data was collected by the National Oceanographic Research Institute (NORI) of the Ministry of Oceans and Fisheries to provide the results of AI-based CCTV footage analysis for sea fog detection at 10 major trading ports. Data can be retrieved using parameters such as observatory code and observation date. The data primarily consists of observatory name, observation date, fog detection index, and score values. This data can be used to establish a maritime traffic safety management system, enhance port operation efficiency, assess safety upon vessel entry and departure, and produce marine weather-related research and statistics.

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels and other oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until October 2021 whenever possible. We have archived the dataset into two parts. This first part contains the 4527 records that are can be used for both research and consultancy purposes. The higher-frequency sea-level dataset in this first part of GESLA-3 was obtained from 33 international and national data providers, specifically: University of Hawaii Sea level Center, National Oceanic and Atmospheric Administration, Marine Environmental Data Section, United States Geological Survey, Bureau of Meteorology, Rijkswaterstaat, Japan Oceanographic Data Center, Japan Meteorological Agency, Swedish Meteorological and Hydrological Institute, Réseaux de référence des observations marégraphiques (Reference networks for tidal observations), British Oceanographic Data Centre, California Department of Water Resources, Japan Oceanographic Data Center, Japan Coast Guard, Norwegian Hydrographic Service, Japan Oceanographic Data Center, Geospatial Information Authority of Japan, Wasserstraßen-und Schifffahrtsverwaltung des Bundes (Federal Waterway and Shipping Administration), Japan Oceanographic Data Center, Ports and Harbours Bureau, South Florida Water Management District, Instituto Superiore per la Protezione e la Ricerca Ambientale (Higher Institute for Environmental Protection and Research), Instituto Español de Oceanografía (Spanish Istitute of Oceanography), Data archaeology exercise, National Autonomous University of Mexico, Finnish Meteorological Institute, Danish Meteorological Institute, Bundesanstalt Für Gewässerkunde(Federal Institute of Hydrology), Marine Institute (Coastal sites), Coastal Channel Observatory, National Oceanography Centre, North West Florida Water Management Department, European Sea-Level Service, Icelandic Coast Guard Hydrographic and Maritime Safety Department, North Carolina Department of Emergency Management, Marine Institute (River Sites) and the Global Sea Level Observing System. These data are made available under the creative commons CC-BY 4.0 license.

This visualization product displays the number of non-MSFD monitoring surveys, research & cleaning operations and the associated temporal coverage per beach.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter 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 and reference lists used on a European scale.

Preliminary processings were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates.

More information is available in the attached documents.

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.

The marine buoys market was valued at $2.8 billion in 2025 and is projected to reach $4.6 billion by 2033, growing at a 6.7% CAGR driven by maritime safety regulations, aquaculture expansion, and ocean monitoring.

This data was collected by the National Oceanographic Research Institute (NORI) of the Ministry of Oceans and Fisheries to provide facility safety information. Its primary content consists of marine space-related information reclassified based on facility safety information based on four key national open data types: maritime zone maps, fishing ground maps, yacht fishing maps, and small boat port maps. This data can be used as a basis for analyzing the safety management status of marine facilities, establishing port and fishing ground safety policies, referencing maritime traffic data for leisure activities and vessel operations, and establishing efficient utilization plans for marine space.

The International Programme for Antarctic Buoys (IPAB) is run by the World Climate Research Programme (WCRP). IPAB is a self-sustaining project of the WCRP, and provides a link between institutions with Antarctic and Southern Ocean interests. IPAB was formally established, following a one year pilot phase, at a meeting in Helsinki, Finland in June 1994. IPAB aims to establish and maintain a network of drifting buoys in the Antarctic sea-ice zone, which monitor ice motion, pressure and temperature. In 1997, 16 organisations, representing 11 countries, were involved in the IPAB programme, including: Alfred Wegener Institute, Antarctic CRC, Australian Antarctic Division, British Antarctic Survey, Commonwealth Bureau of Meteorology, INPE -National Institute for Space Research, Institute for Marine Research and University of Helsinki, Hydrographic Department, Maritime Safety Agency, National Ice Center, National Institute of Polar Research, Programma Nazionale di Ricerche in Antardtide, Scott Polar Research Institute, Service Argos, South African Weather Bureau, United Kingdom Meteorological Office, and World Data Center A Glaciology. Tables of data availability, information, experiment details, literature, and data sets are available from the IPAB home page. Links are also available to databases held by other organisations, and links to Arctic and Indian Ocean buoy databases.

The data are available via several provided URLs. Further information and the data can be obtained from the IPAB home page URL. The data and documentation are also available directly from the NSIDC website. Finally, an older copy of the data are also held locally on the Australian Antarctic Data Centre's servers.

The documentation held at the NSIDC website provides important information on interpreting the dataset. A static copy of this document is included with the local copy of the dataset held on the Australian Antarctic Data Centre's servers.

Data from January 1995 to July 1998 only has been made available on the NSIDC website (and correspondingly on the AADC's servers). More data should be available soon.

This work was also completed as part of ASAC projects 732, 742 and 2678.

The fields in this dataset are: Buoy Number Year Time Longitude Latitude ARGOS Positional Accuracy Sea Ice Flag Air Pressure Air Temperature Water Temperature Velocity

Ocean Monitoring AI Market Size and Growth Outlook

Across all regions, the Ocean Monitoring AI market size reached USD 1.37 billion in 2024, driven by rapid advancements in artificial intelligence and the increasing need for real-time ocean data analytics. The market is projected to grow at a robust CAGR of 18.2% from 2025 to 2033, with the forecasted market size expected to reach USD 5.18 billion by 2033. This remarkable growth is primarily attributed to the rising adoption of AI-powered solutions for marine environmental monitoring, fisheries management, and maritime security, as well as substantial investments by governments and private organizations in marine research and sustainability initiatives.

A key growth factor propelling the Ocean Monitoring AI market is the escalating demand for advanced technologies capable of providing actionable insights into complex oceanic systems. Traditional ocean monitoring methods often struggle with the vastness and dynamic nature of marine environments, leading to gaps in data collection and interpretation. AI-powered software and hardware solutions have revolutionized this space by enabling real-time data processing, anomaly detection, and predictive analytics. These capabilities are crucial for timely responses to environmental threats, such as oil spills or harmful algal blooms, and for supporting sustainable fisheries management. Furthermore, the integration of AI with IoT sensors, autonomous underwater vehicles (AUVs), and satellite imaging has enhanced the accuracy, frequency, and spatial coverage of ocean monitoring operations, making it possible to address both local and global marine challenges more effectively.

The increasing focus on climate change studies and the need for robust marine environmental monitoring is another significant growth driver for the Ocean Monitoring AI market. Governments, research institutions, and environmental organizations are leveraging AI to analyze large-scale oceanographic datasets, monitor changes in sea temperature, salinity, and currents, and predict long-term climate trends. These AI-driven insights are essential for formulating policies aimed at mitigating the impacts of climate change on marine ecosystems and coastal communities. Additionally, the growing incidence of illegal fishing, maritime piracy, and other security threats has prompted investments in AI-based maritime surveillance systems, further expanding the application scope of Ocean Monitoring AI technologies. The convergence of big data analytics, machine learning, and remote sensing is expected to play a pivotal role in advancing ocean science and ensuring the sustainable use of ocean resources.

Private sector involvement and public-private partnerships have also contributed significantly to the growth of the Ocean Monitoring AI market. Companies operating in the oil & gas, shipping & logistics, and fisheries sectors are increasingly adopting AI-driven solutions to optimize operations, ensure regulatory compliance, and minimize environmental risks. For instance, AI-powered oil spill detection systems enable early identification and rapid response, reducing cleanup costs and environmental damage. Similarly, shipping companies utilize AI-based route optimization and cargo tracking to enhance efficiency and reduce fuel consumption. The availability of cloud-based deployment options has democratized access to sophisticated ocean monitoring tools, enabling even small and medium-sized enterprises (SMEs) to participate in marine conservation and resource management efforts. As the market matures, continuous innovation and collaboration among stakeholders will be essential to address emerging challenges and unlock new opportunities in ocean monitoring.

Regionally, North America and Europe currently dominate the Ocean Monitoring AI market, owing to their strong technological infrastructure, significant investments in marine research, and stringent environmental regulations. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing government initiatives, expanding maritime activities, and the rising adoption of digital technologies in countries such as China, Japan, and Australia. Latin America and the Middle East & Africa are also emerging as important markets, supported by growing awareness of marine conservation and the need to protect valuable coastal and offshore resources. Overall, the global Ocean Monitoring AI market is poised for sustained expansion, underpi

This visualization product displays marine macro-litter (> 2.5cm) material categories percentage per beach per year from non-MSFD monitoring surveys, research & cleaning operations.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter 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 and reference lists used on a European scale.

Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Exclusion of surveys without associated length; - Some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Exclusion of the "feaces" category: it concerns more exactly the items of dog excrements in bags of the OSPAR (item code: 121) and ITA (item code: IT59) reference lists; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of items (normalized by 100 m) = Number of litter per items x (100 / survey length) Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey.

To calculate percentages for each material category, formula applied is: Material (%) = (∑number of items (normalized at 100 m) of each material category)*100 / (∑number of items (normalized at 100 m) of all categories)

The material categories differ between reference lists (OSPAR, TSG_ML, UNEP, UNEP_MARLIN). In order to apply a common procedure for all the surveys, the material categories have been harmonized.

More information is available in the attached documents.

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.

Synthetic Aperture Radar (SAR), renowned for its all-weather monitoring capability and high-resolution imaging characteristics, plays a pivotal role in ocean resource exploration, environmental surveillance, and maritime security. It has become a fundamental technological support in marine science research and maritime management. However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. To address these challenges, this paper proposes AC-YOLO, a novel lightweight SAR ship detection model based on YOLO11. Specifically, we design a lightweight cross-scale feature fusion module that adaptively fuses multi-scale feature information, enhancing small target detection while reducing model complexity. Additionally, we construct a hybrid attention enhancement module, integrating convolutional operations with a self-attention mechanism to improve feature discrimination without compromising computational efficiency. Furthermore, we propose an optimized bounding box regression loss function, the Minimum Point Distance Intersection over the Union (MPDIoU), which establishes multi-dimensional geometric metrics to accurately characterize discrepancies in overlap area, center distance, and scale variation between predicted and ground truth boxes. Experimental results demonstrate that, compared with the baseline YOLO11 model, AC-YOLO reduces parameter count by 30.0% and computational load by 15.6% on the SSDD dataset, with an average precision (AP) improvement of 1.2%; on the HRSID dataset, the AP increases by 1.5%. This model effectively reconciles the trade-off between complexity and detection accuracy, providing a feasible solution for deployment on edge computing platforms. The source code for the AC-YOLO model is available at: https://github.com/He-ship-sar/ACYOLO.

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 dataset contains standardized, harmonized and validated data collections from beach litter (monitoring and other sources). Datasets concerning beach and 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. For beach litter, the harmonized datasets contain all unrestricted or SeaDataNet License EMODnet Chemistry data on beach litter data, including 12390 CDI records, in which 11152 refer to monitoring data, 1208 to data from cleaning surveys and 29 to data from research. A relevant part of the monitoring data has been considered for assessment purposes by the European institutions and therefore is tagged as MSFD_monitoring. The temporal range for monitoring data is from 2001-01-01 to 2020-08-26 and includes data from 582 beaches. For data from cleaning, the temporal range is from 2013-03-12 to 2020-04-22 and includes data from 831 beaches. For data from research, the temporal range is from 2014-04-07 to 2016-10-12 and includes data from 21 beaches. EMODnet beach litter data and databases are hosted and maintained by 'Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)' from Italy. Data are formatted following Guidelines and forms for gathering marine litter data, which can be found at: https://doi.org/10.6092/15c0d34c-a01a-4091-91ac-7c4f561ab508. The updated vocabularies of admitted values are available in https://nodc.ogs.it/marinelitter/vocab. The harmonized datasets can be downloaded as EMODnet Beach litter data format Version 1.0, which is a spreadsheet file composed of 4 sheets: beach metadata, survey metadata, animals and litter. The original datasets can be searched and downloaded from EMODnet Chemistry Chemistry CDI Data and Discovery Access Service: https://emodnet-chemistry.maris.nl/search

This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations without UNEP-MARLIN data.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter 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 and reference lists used on a European scale.

Preliminary processings were necessary to harmonize all the data:

• Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring;

• Selection of surveys from non-MSFD monitoring, cleaning and research operations;

• Exclusion of beaches without coordinates;

• Selection of cigarette related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata);

• Exclusion of surveys without associated length;

• Exclusion of surveys referring to the UNEP-MARLIN list: the UNEP-MARLIN protocol differs from the other types of monitoring in that cigarette butts are surveyed in a 10m square. To avoid comparing abundances from very different protocols, the choice has been made to distinguish in two maps the cigarette related items results associated with the UNEP-MARLIN list from the others;

• Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula:

Number of cigarette related items of the survey (normalized by 100 m) = Number of cigarette related items of the survey x (100 / survey length)

Then, this normalized number of cigarette related items is summed to obtain the total normalized number of cigarette related items for each survey. Finally, the median abundance of cigarette related items for each beach and year is calculated from these normalized abundances of cigarette related items per survey.

Percentiles 50, 75, 95 & 99 have been calculated taking into account cigarette related items from other sources data (excluding UNEP-MARLIN protocol) for all years.

More information is available in the attached documents.

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.

The Copernicus Marine Service is the marine component of the European Union’s Copernicus Programme. It provides free, open-access, and science-based information on the state of the Ocean at both global and regional scales. This includes data on the physical Ocean (Blue Ocean), sea ice (White Ocean), and Ocean biogeochemistry (Green Ocean).Funded by the European Commission and implemented by Mercator Ocean International, the service supports the implementation of EU policies and International legal Commitments related to Ocean Governance. It also aims to meet the growing demand for reliable Ocean knowledge across society, and to foster sustainable development in the Ocean sectors by providing state-of-the-art Ocean data and forecasts.The Copernicus Marine Service contributes to a wide range of applications, including marine protection, climate change monitoring, pollution tracking, maritime safety, sustainable use of marine resources, and the development of renewable Ocean energy. It is also an asset for scientific research and innovation, and plays a key role in raising public awareness of Ocean-related issues by making complex Ocean information accessible to citizens all around the world.The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12° horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).It is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.DOI (product): https://doi.org/10.48670/moi-00021

The Copernicus Marine Service is the marine component of the European Union’s Copernicus Programme. It provides free, open-access, and science-based information on the state of the Ocean at both global and regional scales. This includes data on the physical Ocean (Blue Ocean), sea ice (White Ocean), and Ocean biogeochemistry (Green Ocean).Funded by the European Commission and implemented by Mercator Ocean International, the service supports the implementation of EU policies and International legal Commitments related to Ocean Governance. It also aims to meet the growing demand for reliable Ocean knowledge across society, and to foster sustainable development in the Ocean sectors by providing state-of-the-art Ocean data and forecasts.The Copernicus Marine Service contributes to a wide range of applications, including marine protection, climate change monitoring, pollution tracking, maritime safety, sustainable use of marine resources, and the development of renewable Ocean energy. It is also an asset for scientific research and innovation, and plays a key role in raising public awareness of Ocean-related issues by making complex Ocean information accessible to citizens all around the world.The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12° horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).It is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.DOI (product): https://doi.org/10.48670/moi-00021

This data was collected by the National Oceanographic Research Institute of the Ministry of Oceans and Fisheries to provide leisure and tourism information. Its primary content is marine spatial information reclassified based on leisure and tourism information based on four key national open data types: maritime zone maps, fishing ground information maps, yacht fishing information maps, and small boat port guide maps. This data can be utilized for policymaking, field work, and the production of zone-specific user guidance materials, including marine leisure tourism business planning, field operations, marine zone management, fishing ground and port facility development, analysis of maritime zone-specific usage, and safety facility deployment.

This visualization product displays beaches locations where non-MSFD monitoring surveys, research & cleaning operations have been applied to collate data on macrolitter (> 2.5 cm). Reference lists associated with these protocols have been indicated with different colors in the map.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter 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 and reference lists used on a European scale.

Preliminary processings were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata).

More information is available in the attached documents.

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.

This visualization product displays the single use plastics (SUP) related items abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter 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 and reference lists used on a European scale.

Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Selection of SUP related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines for Macro Litter on Coastlines from JRC (this document is attached to this metadata); - Exclusion of surveys without associated length; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of SUP related items of the survey (normalized by 100 m) = Number of SUP related items of the survey x (100 / survey length) Then, this normalized number of SUP related items is summed to obtain the total normalized number of SUP related items for each survey. Finally, the median abundance of SUP related items for each beach and year is calculated from these normalized abundances of SUP related items per survey.

Percentiles 50, 75, 95 & 99 have been calculated taking into account SUP related items from other sources data for all years.

More information is available in the attached documents.

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.