SeaDataNet

SeaDataNet is a major pan-European infrastructure for managing and providing access to marine data sets, acquired by European organisations from research cruises and other observational activities in European coastal marine waters, regional seas and the global ocean. Founding partners are National Oceanographic Data Centres (NODCs), and major marine research institutes. The SeaDataNet network gradually expanded its network of data centres and infrastructure, during a series of dedicated EU RTD projects, and by engaging as core data management infrastructure and network in leading European Commission initiatives such as the European Marine Observation and Data network (EMODnet), Copernicus Marine Service (CMS), and the European Open Science Cloud (EOSC).SeaDataNet develops, governs and promotes common standards, vocabularies, software tools, and services for marine data management, which are widely adopted. A core service is the CDI data discovery and access service which provides online unified discovery and access to vast resources of data sets, managed by 115+ connected SeaDataNet data centres from 34 countries around European seas, both from research and monitoring organisations. Currently, it gives access to more than 3 Million data sets, originating from 1000+ organisations in Europe, covering physical, geological, chemical, biological and geophysical data, acquired in European waters and global oceans. Standard metadata and data formats are used, supported by an ever-increasing set of controlled vocabularies, resulting in rich and highly FAIR metadata and data sets. SeaDataNet provides core services in EMODnet Chemistry, Bathymetry, and Physics for bringing together and harmonizing large amounts of marine data sets, which are used by EMODnet groups for generating thematic data products.EMODnet Bathymetry is active since 2008 and maintains a Digital Terrain Model (DTM) for the European seas. This is published every 2 years, each time extending coverage, and improving quality and precision. The DTMs are produced from surveys and aggregated data sets that are referenced with metadata via the SeaDataNet Catalogue services. Bathymetric survey data sets are gathered and populated by national hydrographic services, marine research institutes, and companies in the SeaDataNet CDI Data Discovery & Access service. Currently, this amounts to more than 45.000 datasets from 78 data providers. A major selection of these datasets has been used for preparing the 2024 release of the EMODnet DTM for all European waters and Caribbean, which has been published on the EMODnet portal. Currently, work is ongoing for a new 2026 version. The EMODnet DTM has a grid resolution of 1/16 * 1/16 arc minutes (circa 115 * 115 m), covering all European seas. It is based upon circa 22.000+ in situ datasets. It can be downloaded in tiles and viewed as map layers in the EMODnet portal. The maps are derived from EMODnet Bathymetry OGC WMS, WMTS, and WFS services. The EMODnet Bathymetry products are very popular and in 2024 – 2025 more than 100.000 EMODnet DTM files were downloaded, and more than 60 million OGC service requests were registered over the 2 years. EMODnet Bathymetry is also managing the European contribution to the international Seabed 2030 project.

Ocean observations are acquired from a great variety of platforms at different spatial and temporal scales. Additionally, a great number of physical, chemical, geological and biological variables need to be measured to characterise and understand ocean dynamics and the ocean ecosystem. Developing robust infrastructures that facilitate the search and access to diverse oceanographic data becomes essential to be able to use ocean observations for increasing our knowledge of ocean dynamics and of the ocean ecosystem and for producing information, services and products to support the great diversity of marine stakeholders.In this communication, we will present recent developments of data infrastructures and data services at the the Spanish Institute of Oceanography (IEO-CSIC) in the framework of different national and international funded projects (DEMON, Oceans+, iFADO, DATAMARE...). IEO was founded in 1914 and focuses its activity on oceanography and marine research for different marine stakeholders and to provide scientific advice to the government to support the implementation of marine related policies. IEO is part of the National Oceanographic Data Centres (NODC) network, which adhere to FAIR principles (Findable, Accessible, Interoperable, and Reusable) to ensure standardized data management and global exchange of ocean data. In this contribution we will focus on our recent developments of standard catalogues using open-source tools like GeoNetwork and we will show how this approach has enabled IEO to organise and present a vast catalogue of oceanographic data and of data services that disseminate marine data sets following FAIR principles. Proper hierarchization ensure that users (individuals or data hubs) can navigate the IEO catalogue effectively, taking advantage of the search capabilities to access data for research and applications. Different examples of access to multidisciplinary data sets obtained with different ocean observing platforms will be provided. In addition, we will show how the ocean data stored at IEO can be catalogued to support the implementation of the EU Marine Strategy Framework Directive (MSFD).Finally, we will present the integrated marine data platform being developed by the EU funded Galician Marine Sciences Program by different academic and research actors in Galicia (CESGA, CSIC, MeteoGalicia, INTECMAR, CETMAR and universities of Santiago and Coruña) with the objective of demonstrating the improvement in the management of marine data using supercomputing technologies (High Performance Computing, HPC). We will show practical examples of how access and processing of multidisciplinary marine data sets can be performed in the infrastructure in a powerful, standardized, fast and simple way.

SeaDataNet is a pan-European infrastructure that manages and provides access to marine datasets collected by European organisations through research cruises and observational activities in coastal waters, regional seas, and the global ocean. It was founded by National Oceanographic Data Centres (NODCs) and major marine research institutes. The network has expanded through successive EU-funded RTD projects and by contributing to major European initiatives such as EMODnet, Copernicus Marine Service, ENVRI, and the European Open Science Cloud (EOSC).SeaDataNet develops and promotes widely adopted standards, vocabularies, software tools, and services that support FAIR marine data management. Its core service, the CDI (Common Data Index), provides unified online discovery and access to in situ marine observation data managed by more than 115 data centres in 34 countries. The service currently offers access to over 3 million datasets from more than 1,000 European organisations, covering physical, chemical, biological, geological, and geophysical data from European waters and the global ocean. The use of standard metadata, formats, and controlled vocabularies ensures rich, highly FAIR datasets.SeaDataNet also delivers core data services for EMODnet Chemistry, Bathymetry, and Physics, harmonising large volumes of marine data that support the production of thematic data products, including Essential Climate Variable (ECV) and Essential Ocean Variable (EOV) datasets.Environmental science increasingly relies on large, heterogeneous, and rapidly growing data collections that must be efficiently accessed, subsetted, and harmonised for use in models, digital twins, AI workflows, and Virtual Research Environments (VREs). The fully open-source Beacon software, developed by MARIS https://beacon.maris.nl/, addresses these challenges by enabling cloud-native, high-performance data lakes that are fast to deploy and access. Beacon supports parameter harmonisation using metadata annotations based on NERC vocabularies, ECV vocabularies, and the I-ADOPT methodology adopted in ENVRI-HUB Next.To improve the ease of access to subsets of the SeaDataNet CDI data collection, a Beacon instance containing all the open SeaDataNet data was set-up. This now allows users to obtain real-time access to data subsets in multiple data formats (NetCDF, Parquet, Zarr) and flexible querying from Jupyter Notebooks or a newly developed Beacon studio (user interface) for non-technical users. Within ENVRI-HUB Next, this SeaDataNet instance enables on-the-fly access to ECV-related subsets from millions of files via Jupyter Notebooks, ready for use in the Analytical Framework.The presentation focuses on this use case, the technical solution, and its potential applicability for other Research Infrastructures supporting EOSC use cases.

AQUARIUS is an ongoing Horizon Europe funded project. An impressive range of 57 research infrastructure services is made available by Transnational Access (TA) Calls to include research vessels, mobile marine observation platforms, fixed marine facilities, experimental research facilities, river & basin supersites, aircraft, drones, satellite services, and sophisticated data infrastructures.As a result of the TA projects, many new data sets in a large variety of data types are being collected by TA teams, using and combining multiple and different observation installations. A major aim of AQUARIUS is supporting the EU Mission to Restore our Ocean and waters by 2030, and other marine initiatives, including contributing to the European Digital Twin of the Ocean and the UN Decade for Ocean Sciences.There is a strong effort in AQUARIUS to get the maximum return of investment from the TA activities. An open data policy has been adopted, implemented with a dedicated Data Management approach, to ensure that all gathered metadata and data are managed in line with the FAIR principles. They should become part of the repositories managed and operated by leading European data management infrastructures, such as SeaDataNet, EurOBIS, ELIXIR-ENA, ICOS-Ocean, and Copernicus INSTAC, for quality assurance, long term stewardship, and wide access and use. These infrastructures in turn are feeding into EMODnet, Copernicus Marine, Blue-Cloud (EOSC), Digital Twin of the Ocean (DTO) developments, and globally to e.g. GEOSS, and the UN-IOC Ocean Decade programme.To achieve a maximum result, the TA scientific teams are being supported by data centres, experienced in marine data management, and well connected to the European data management infrastructures. Most of them are National Oceanographic Data Centres (NODCs). They provide training and coach the TA teams during the AQUARIUS data management flow scheme. This includes steps from planning to training to deployment to publishing, and a number of instruments. One of those is the AQUARIUS TA Data Summary Log App which is used by PIs of TA projects to keep an overview and index of the data collection events. It produces a list for the data centres to know what data to expect from where and who and as a checklist for the next steps. The AQUARIUS TA Data Summary Log contains only metadata and no data. As follow-up, the TA teams and assigned data centres will work on elaborating the collected data to prevailing standards and inclusion in the European repositories. That progress is made visible through the AQUARIUS Dataflow Dashboard (ADD), integrated in the AQUARIUS website. It follows the progress from planning stage through to publishing of results for each awarded TA project. The ultimate goal is to give discovery and public access to research data sets as collected and processed and data products as generated by the TA research teams as part of the AQUARIUS TA projects.The presentation will provide more background information on the AQUARIUS project and will highlight more details about the data management approach.

Data from the marine environment are a valuable asset for marine industries, decision-making bodies and scientific research, but in Europe marine data are stored in a wide range of national, regional and international databases and repositories using different formats and standards which makes it difficult to find, assemble and use them efficiently. The European Marine Observation and Data Network (EMODnet) is a network of organisations set up in 2007 by the European Commission in the framework of EU’s Integrated Maritime policy to address the fragmented marine data collection, storage and access in Europe. This paper introduces EMODnet in the context of EU’s Marine Knowledge 2020 Strategy and highlights some of the main features of the EMODnet Data Portals, as well as those being developed by the EMODnet central portal. Finally, the paper zooms in on the features and endeavours of one specific EMODnet activity: the EMODnet portal with access to near-real time and historical data of physical parameters.

Many regions including Europe, the USA, Australia and Canada are making significant progress in facilitating the discovery, access and long-term stewardship of marine data through the development, implementation and operation of national, regional or international distributed ocean observing and data management infrastructures such as SeaDataNet, EMODnet, IOOS, R2R, and IMOS. The Ocean Data Interoperability Platform (ODIP) project, supported by the EU Framework Programme 7 (FP7), National Science Foundation (USA) and Australian government, was initiated 1st October 2012 and continues as ODIP II for another 3 years with EU HORIZON 2020 funding. ODIP includes many of the major organizations engaged in ocean data management in Europe, US, Australia and Canada. ODIP is also supported by the IOC-IODE, closely linking this activity with its Ocean Data Portal (ODP) and Ocean Data Standards Best Practices (ODSBP) projects.

Marine and coastal data management is crucial for scientific research, economic activities, and environmental monitoring. Marine data is collected from different sources using various tools and methodologies. There are several methods used for formatting marine data, but to put them in a standardized form takes time, effort and knowledge. To overcome the technical difficulties involved in submitting data, a group of 41 international organizations, led by HCMR (Greece) and MARIS (The Netherlands), is active in the EMODnet Data Ingestion project. Their goal is to simplify the process of data acquisition and transformation, ensuring data becomes interoperable and increasing the availability of marine data. By providing a structured platform for the submission, management, and dissemination of marine datasets, the project fosters an environment where data can be easily accessed, shared, and utilized by a wide range of stakeholders, including researchers, policymakers, and public. This paper presents EMODnet Data Ingestion services within the framework of the broader EMODnet. Examples of data sharing and important aspects of the EMODnet Data portal are delivered.

<p>The demand for tailored climate data by different users is growing worldwide together with the awareness of the challenges posed to society and the environment by climate change. The extreme weather events intensification, sea-level rise, and coastal erosion are urgent challenges to be addressed by European coastal cities. The overreaching scope of the H2020 SCORE (Smart Control of the Climate Resilience in European Coastal Cities) project is to develop a framework for the definition and uptake of integrated Ecosystem-Based Approaches (EBA) and smart digital tools by establishing a network of 10 coastal city' living labs' (CCLLs) to increase the climate resilience of European coastal cities. To achieve this, the first steps are focused on i) the identification and selection of reference datasets for the historical baseline characterization and the projections for the next decades, ii) the downscaling of climate projections in order to produce a dataset of environmental parameters with the suitable temporal and spatial resolution for the project CCLLs' application needs, and iii) the development of statistical tools for data analysis, modeling and testing to assess the occurrence of major coastal hazards and the future evolution trends of the coastline. For this purpose, open, free, and reliable climate data are needed.</p><p>Based on some essential requirements, a procedure to select, from the main European climate services, fit-for-purpose climate and marine data of interest for SCORE users has been set up. Moreover, a step-by-step procedure on how to access and handle these data has been provided.  One of the main issues encountered while exploring the vast range of different climate services and datasets available consists in the articulation of the users' needs. In particular, the best coverage in terms of variables of interest for modeling and Spatio-temporal resolution must be ensured, while guaranteeing a good users' experience in terms of easy accessibility and the provision of information on (meta)data quality, standards, and conventions. The fragmentation of marine data repositories during the previous decades, along with their limited historical temporal coverage are other main challenges encountered. In addition, notwithstanding the availability of datasets downscaled from global to regional models, the spatio-temporal resolution of most part of datasets requires undertaking some statistical or physical downscaling procedures for their use in local impact studies.</p><p>We thus construct our analysis on those databases and services which are officially available through and/or supported by EU institutions like, e.g., the Copernicus Climate Change Service (C3S) and the Climate Data Store (CDS), the Copernicus Marine Environment Monitoring Service (CMEMS) portal or the European Marine Observation and Data Network (EMODnet) initiative.    The various criteria defined to select the most appropriate climate services and related datasets for the SCORE activities will be presented as well as a few case studies as examples of possible climate information communication strategies, to help the end-user practically understand and tackle the challenges when interacting with the dataset-interface and in data handling procedures.</p>

With the growing complexity and frequency of oceanic sampling, more efficient systems are needed to manage the information. Developing robust infrastructures that facilitate the search and access to oceanographic data becomes essential to ensure effective use of information — meaning its integration, interoperability, and reuse across research and operational contexts — as well as to promote collaboration and interoperability among organizations. In this context, the concept of geoportals as human-machine interfaces emerges as one of the key solutions for accessing and disseminating spatial data. The Spanish Institute of Oceanography (IEO-CSIC), as part of the National Oceanographic Data Centres (NODC) network, has adopted open-source solutions such as GeoNetwork to develop a standardized metadata repository that adheres to FAIR principles (Findable, Accessible, Interoperable, and Reusable). This approach has enabled IEO to organize and present an extensive catalogue of multidisciplinary oceanographic data, supporting data discovery, integration, and global exchange. The implementation of proper hierarchization mechanisms has improved search capabilities, allowing users (individuals or data hubs) to efficiently access marine datasets for research and applications. Furthermore, the cataloguing of ocean data at IEO plays a crucial role in supporting the implementation of the EU Marine Strategy Framework Directive (MSFD), demonstrating the impact of structured data management on environmental policies and ocean governance.

Common-azimuth imaging can significantly reduce the cost of full-volume 3-D prestack depth imaging of marine data sets. The common-azimuth imaging procedure comprises of two steps: first, transformation of the prestack data to common azimuth data by azimuth moveout (AMO); second, imaging the transformed data by common-azimuth migration. Both these steps are computationally efficient. AMO is a partial migration operator and thus it has narrow spatial aperture. Common-azimuth migration is based on downward continuation of the wavefield; therefore, its computational cost increases only as the square of the image depth. In contrast, the cost of conventional Kirchhoff migration is proportional to the cube of the image depth. Because it is a wavefield-continuation method, common-azimuth migration does not require the computation of asymptotic Green functions. Therefore, common-azimuth imaging is likely to overcome some of the accuracy problems encountered by Kirchhoff migration in the presence of complex wave-propagation phenomena. The proposed common-azimuth imaging procedure successfully depth imaged a marine data set recorded in the North Sea. These positive results suggest the application of common-azimuth imaging to velocity estimation based on wavefield focusing. Introduction Computational cost is a main obstacle to the widespread use 3D prestack depth migration for full-volume imaging. Currently, 3-D prestack migration is almost exclusively performed by Kirchhoff methods because they effectively handle the irregular and sparse geometries of 3-D prestack data. However, the computational cost of full-volume imaging by Kirchhoff migration increases by a factor proportional to the cube of the depth extent of the image, making the imaging of deeper targets extremely expensive. In contrast, the cost of migration methods based on the downward continuation of the wavefield increases only with the square of depth. But 3-D prestack data cannot be efficiently downward-continued by standard methods because of their irregular and sparse sampling. Here I present a procedure for full-volume prestack imaging of marine data sets that takes advantage of the limited azimuthal range of 3-D marine data to reduce significantly the computational cost. The procedure comprises two steps. The first step transforms the recorded data into effective common-azimuth data, where the common-azimuth is the direction of the acquisition sail line. The second step images the synthesized common-azimuth data set by prestack migration based of the downward continuation of the wavefield. To transform marine data into effective common-azimuth data, I "rotate" the prestack data using a partial-prestack migration operator called azimuth moveout3 (AMO). The AMO operator is defined in the time-space domain, and it can be applied as an integral operator to 3-D marine data with geometry irregularities caused by cable feather and multistreamer recording. The spatial aperture, and consequently the computational cost, of the AMO operator is approximately proportional to the azimuth rotation between the input offset vector and the output offset vector, and it is small when the azimuth rotation is small. Since the azimuths of most of the traces recorded during a marine acquisition are close to the nominal common azimuth, the cost of the AMO transformation is low. Common-azimuth migration is based on a downward-continuation operator derived from the full 3-D prestack operator2.

Since June 2004, two ocean data archivists from the National Oceanic and Atmospheric Administration (NOAA) National Oceanographic Data Center (NODC) have participated as on-board data managers for two cruises sponsored by the NOAA Office of Ocean Exploration (OE). These two offices are part of the OE Integrated Product Team (IPT), which is developing an end-to-end OE data management system. Two components of this system are 1) gathering information during OE cruises about the on-board science activities and the collected data; and 2) archiving these data with their accompanying science and cruise information. The NODC archivists' participation on the OE cruises gave them a first-hand perspective and insight on these two ends of the data management spectrum. Based on these experiences, three recommendations for an end-to-end data management system are presented.

My work developed a kernel density estimator that well resolves typical structures of probability densities, which was demonstrated on a newly compiled marine data set of organic carbon-13 isotope ratios (δ13CPOC). All work was conducted within the emerging field of marine data science. I identified classical data science, a general understanding of ocean science, communication skills, and confidence as requirements for marine data scientists. In the beginning of my work, the existing δ13CPOC data consisted of about 500 data points in the global ocean. I expanded the existing data set to 4732 data points in a first version, and additionally to 6952 in a second. Both are published at PANGAEA along with meta information such as measurement location, time, and method, and interpolations. I have published a description of the temporal and geographic distribution of the first version at Earth System Science Data. I designed the development of the kernel density estimator algorithm on the existing concept of computing it as a solution of the diffusion equation. My algorithm uses finite differences in space and equidistant time steps with an implicit Euler method, and approximates the optimal smoothing parameter by two pilot steps. Compared to other well-known kernel density estimators, my algorithm produces reliable approximations of multimodal and boundary-close distributions on artificial and real marine data and is robust to noise. My implementation is published as a Python package on Zenodo, its description is submitted to Geoscientific Model Development. I was able to show that my kernel density estimator reliably evalu- ates ocean data and thus lays a foundation for calibrating Earth system models. At the same time, I was able to contribute to the definition and establishment of the field of Marine Data Science.

: The feasibility of global ocean weather prediction was just emerging as the Global Ocean Data Assimilation Experiment (GODAE) began in 1997. Ocean weather includes phenomena such as meandering currents and fronts, the surface mixed layer and sea surface temperature (SST), waves, upwelling of cold water, all influencing ocean variables such as temperature (T), salinity (S), currents, and sea surface height (SSH). Adequate real-time data input, computing power, numerical ocean models, data assimilation capabilities, atmospheric forcing, and bathymetric/boundary constraints are essential to make such prediction possible. The ocean models dynamically interpolate data in conjunction with data assimilation, convert atmospheric forcing into oceanic responses, and forecast the ocean weather. The results are substantially influenced by ocean model simulation skill and it is advantageous to use an ocean model that is eddy-resolving, not just eddy-permitting. Because the most abundant ocean observations are satellite surface data, and subsurface data are very sparse, downward projection of surface data is a key challenge in ocean data assimilation. The need for accurate prediction of ocean features that are inadequately observed places a major burden on the ocean model, data assimilation, and atmospheric forcing. The sensitivity of ocean phenomena to atmospheric forcing and the time scale for response affect the oceanic data requirements and prediction system design. Outside of surface boundary layers and shallow regions, forecast skill is about one month globally and over many subregions, and is only modestly reduced by using climatological forcing. In addition, global ocean prediction systems must demonstrate the ability to provide initial and boundary conditions to nested regional and coastal models that enhance their predictive skill.

Salinity is usually neglected in underwater acoustics because of its minimal contribution on sound speed variation. However, it is required to consider in calculation of sound speed and acoustic propagation for low salinity water freshened by continental run-off. Furthermore, the acoustic characteristics of low salinity environment are not fully investigated yet. Therefore, regional difference of acoustic propagation in low salinity water was studied by comparing the acoustic characteristics of the East China Sea with those of the tropical Atlantic Ocean in this paper. Frequency dependency of sound propagation in the haline channel was analyzed with transmission losses and low frequency cut-off using oceanic data from NODC (National Oceanographic Data Center). The tropical Atlantic Ocean showed larger channel depth, critical angles and less transmission loss in the haline channel than the East China Sea. The cut-off frequency in the haline channel was computed as around 1 kHz and 5 kHz in the tropical Atlantic Ocean and the East China Sea, respectively. The effects of low salinity water on sound propagation showed regional characteristics, and need to be considered in sonar operation near sea surface. [This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract UD130007DD.]

We have compared and evaluated the water mass census in the Greenlend-Iceland-Norwegian (GIN) Sea area from climatologies, observational data sets and model output. The four climatologies evaluated were: the 1998 and 2001 versions of theWorld Ocean Atlas (WOA98, WOA01), and the United States Navy’s GDEM90 (Generalized Digital Environmental Model) and MODAS01 (Modular Ocean Data Assimilation System) climatologies. Three observational data sets were examined: the multidecadal (1965-1995) set contained on the National Oceanographic Data Center’s (NODC) WOD98 (World Ocean Data) CD-ROM, and two seasonal data sets extracted from observations taken on six cruises by the SACLANT Research Center (SACLANTCEN) of NATO/Italy between 1986-1989. The model data is extracted from a global model run at 1/3 degree resolution for the years 1983-1997, using the POP (Parallel Ocean Program) model of the Los Alamos National Laboratory. The census computations focused on the Norwegian Sea, in the southern part of the GIN Sea, between 10◦W-10◦E and 60◦N-70◦N, especially for comparisons with the hydrocasts and the model. Cases of such evaluation computations included: a) “short term” comparisons with quasi-synoptic CTD surveys carried out over a 4-year period in the southeastern GIN Sea; b) “climatological” comparisons utilizing all available casts from the WOD98 CD-ROM, with four climatologies; and c) a comparison between the WOA01 climatology and the POP model output ending in 1997. In this region in the spring, the fraction of ocean water that has salinity above 34.85 is ∼ 94%, and that has temperatures above 0◦C is ∼ 33%. Three principal water masses dominated the census: the Atlantic water AW, the deep water DW and an intermediate water mass defined as Lower Arctic Intermediate Water (LAIW). Besides these classes, both the climatologies and the observations exhibited the significant presence of deep water masses with T-S characteristics that do not fall into the “named” varieties, e.g., Norwegian Sea or Greenland Sea deep water (NSDW, GSDW). The seasonal volumetric changes for the Atlantic (AW), intermediate (LAIW) and deep waters (DW) in the GIN Sea are in reasonably good agreement between the climatologies, and with the results of hydrographic census surveys. Typical seasonal changes (spring-summer) involve about 30 × 103 km3 of AW increase and 33 × 103 km3 of LAIW decrease, and a decrease of about 32 × 103 km3 of DW between spring and autumn.