Direct and harmonised access to Essential Climate Variable related in-situ observation data from 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.

SeaDataNet is actively operating and further developing a Pan-European infrastructure for managing, indexing and providing access to ocean and marine data sets and data products, acquired from research cruises and other observational activities in European marine waters and global oceans. It is undertaken by the National Oceanographic Data Centres (NODCs), and marine information services of major research institutes, from 35 coastal states bordering the European seas. Its data centres have the essential capabilities and facilities for data quality control, long term stewardship, retrieval and distribution. Development and provision of standards, services and tools for marine and ocean data management has proven instrumental for wider uptake. This is further encouraged by cooperation and synergy with EU marine research projects, and by adoption of the SeaDataNet infrastructure in the EMODnet (European Marine European Marine Observation and Data Network) development. EMODnet is an initiative in the framework of the EU Marine Directive.

Research projects heavily rely on the exchange and processing of data and in this context Pangeo (https://pangeo.io/), a world-wide community of scientists and developers, thrives to facilitate the deployment of ready to use and community-driven platforms for big data geoscience. The European Open Science Cloud (EOSC) is the main initiative in Europe for providing a federated and open multi-disciplinary environment where European researchers, innovators, companies and citizens can share, publish, find and re-use data, tools and services for research, innovation and educational purposes. While a number of services based on Jupyter Notebooks were already available, no public Pangeo deployments providing fast access to large amounts of data and compute resources were accessible on EOSC. Most existing cloud-based Pangeo deployments are USA-based, and members of the Pangeo community in Europe did not have a shared platform where scientists or technologists could exchange know-how. Pangeo teamed up with two EOSC projects, namely EGI-ACE (https://www.egi.eu/project/egi-ace/) and C-SCALE (https://c-scale.eu/) to demonstrate how to deploy and use Pangeo on EOSC and emphasise the benefits for the European community. The Pangeo Europe Community together with EGI deployed a DaskHub, composed of Dask Gateway (https://gateway.dask.org/) and JupyterHub (https://jupyter.org/hub), with Kubernetes cluster backend on EOSC using the infrastructure of the EGI Federation (https://www.egi.eu/egi-federation/). The Pangeo EOSC JupyterHub deployment makes use of 1) the EGI Check-In to enable user registration and thereby authenticated and authorised access to the Pangeo JupyterHub portal and to the underlying distributed compute infrastructure; and 2) the EGI Cloud Compute and the cloud-based EGI Online Storage to distribute the computational tasks to a scalable compute platform and to store intermediate results produced by the user jobs. To facilitate future Pangeo deployments on top of a wide range of cloud providers (AWS, Google Cloud, Microsoft Azure, EGI Cloud Computing, OpenNebula, OpenStack, and more), the Pangeo EOSC JupyterHub deployment is now possible through the Infrastructure Manager (IM) Dashboard (https://im.egi.eu/im-dashboard/login). All the computing and storage resources are currently supplied by CESNET (https://www.cesnet.cz/?lang=en) in the frame of EGI-ACE project (https://im.egi.eu/). Several deployments have been made to serve the geoscience community, both for teaching and for research work. To date, more than 100 researchers have been trained on Pangeo@EOSC deployments and more are expected to join, in particular with easy access to large amounts of Copernicus data through a recent collaboration established with the C-SCALE project. In this presentation, we will provide details on the different deployments, how to get access to JupyterHub deployments and more generally how to contribute to Pangeo@EOSC.

The increase of atmospheric CO2 levels by as much as about 10% since the beginning of 21st century and its impact on the Earth’s climate and the biosphere represent a major concern. Based on a compilation of in situ data extrapolated over the global coastal ocean, previous studies indicate that the world’s coastal shelves absorb about 0.25 Pg C/year (~17% of oceanic CO2 influx), although these areas represent only 7% of the oceanic surface area. However, large uncertainties in coastal carbon fluxes and stocks exists due to their under sampling in both space and time. Moreover, all of the methods used to assess the land carbon sink rely upon accurate estimate of oceanic carbon as a key constraint or input. In this context, satellite remote sensing of ocean colour play a central role, as this Essential Climate Variables is currently the only ones for monitoring coastal and open ocean waters globally and systematically, at high spatial and temporal resolutions. Based on the development of recent algorithms performed in the frame of several research projects funded by CNES, EUMETSAT, ANR, and the COPERNICUS marine service, we present and discuss the first global vision of key aquatic carbon components over the global coastal ocean at high spatial resolution. The different algorithms developed to assess the particulate organic carbon, POC, dissolved organic carbon, DOC, and the in-water partial pressure of CO2, pCO2w, from ocean colour remote sensing will be presented with their quantified uncertainties. Theses algorithms will then be applied to the remote sensing reflectances obtained from the ESA Data User Element Project GlobColour to assess the spatio-temporal patterns of POC, DOC, and pCO2w over the global coastal waters from 1998 to 2024. The main temporal patterns will be presented and discussed and the global coastal carbon hot spots of long-term significative changes will be identified.

Modern Environmental and Earth sciences demand seamless integration across atmospheric, marine, terrestrial, and biodiversity data, and this process is often hindered by disciplinary silos. The ENVRI-Hub addresses this challenge directly by serving as the unified Virtual Research Environment (VRE) for Europe's Environmental Research Infrastructures. It moves beyond a simple data portal to function as an integrated platform where discovery, access, and analysis converge.The hub provides researchers with a centralised gateway to discover and access FAIRified research assets, enabled for cross-disciplinary work. Crucially, it enables these assets to be leveraged in situ through any VRE using a unified machine actionable API/toolset. , in order to support data analytics in scientific workflows. VREs will allow users to compose, execute, and share reproducible analytical pipelines - from accessing Essential Climate Variables (ECVs) to running complex AI analytics. This architecture not only streamlines the scientific process but also underpins applications like environmental Virtual Laboratories and foundational work for future applications like Digital Twins.This presentation will detail the ENVRI-Hub's technical architecture for enabling VRE support. We will demonstrate, through specific scientific use cases, how its Catalogue of Services and AI-powered Knowledge Base work synergistically to reduce data friction. The contribution will highlight how this integrated environment supports workflow builders in creating robust, cross-domain analyses, thereby accelerating scientific results and advancing collaborative, data-driven Environmental and Earth science.

Authored by: the FIM4R Community The European Open Science Cloud (EOSC) will soon enter the next phase of integration and consolidation with the establishment of a common service portal listing underpinning services that enable distributed resources in the areas of computation, data, open access, and above-the-net collaboration services. The existing e-Infrastructures that are anticipated to be part of the EOSC each provide their own capabilities in terms of trust and identity management, integrity protection and risk management, as well as capabilities to support business continuity and disaster recovery in case of security incidents. There are also specific trust, collaboration management, and security services that are jointly managed by multiple e-Infrastructures for the benefit of (but in many cases not exclusively) the European research and collaboration community as a whole. These include for instance the glue between the EOSC AAI suite of services that each implement the AARC Blueprint Architecture (AARC BPA), i.e. the AAI services such as those from both national and pan-European Infrastructures as well as components such as credential translation bridge services.

In most places on the planet vegetation thrives: it is known as “greening Earth”. However in certain regions, especially in the Arctic, there are areas exhibiting a browning trend. This phenomenon is well known but not fully understood yet, and grasping its impact on local ecosystems requires involvement of scientists from different disciplines, including social sciences and humanities, as well as local populations. Here we focus on the Troms and Finnmark counties in northern Norway to assess the extent of the problem and any link with local environmental conditions as well as potential impacts. We have chosen to adopt an open and collaborative process and take advantage of the services offered by RELIANCE on the European Open Science Cloud (EOSC). RELIANCE delivers a suite of innovative and interconnected services that extend the capabilities of the European Open Science Cloud (EOSC) to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The RELIANCE project has delivered 3 complementary  technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining. RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work. We will show how we are using these technologies along with EGI notebooks to work open and share an executable Jupyter Notebook that is fully reproducible and reusable. We use a number of Python libraries from the Pangeo software stack such as Xarray, Dask and Zarr. Our Jupyter Notebook is bundled with its computational environment, datacubes and related bibliographic resources in an executable Research Object. We believe that this approach can significantly speed up the research process and can drive it to more exploitable results. Up to now, we have used indices derived from satellite data (in particular Sentinel-2) to assess how the vegetation cover in Troms and Finnmark counties has changed. To go a bit further we are investigating how to relate such information to relevant local parameters obtained from meteorological reanalysis data (ERA5 and ERA5-land from ECMWF). That should give a good basis for training an Artificial Intelligence algorithm and testing it, with the objective of getting an idea about the possibility of “predicting” what is likely to happen in the near future with certain types of vegetation like mosses and lichens which are essential for local populations and animals.

The EU Copernicus Marine Service, implemented by Mercator Ocean International with a large network of observation and modelling production centers, is a world-leading, reference digital information service on the world ocean and EU regional seas.  The Copernicus Marine Service monitors in real time and over the past decades the world ocean across the entire water column using in situ and satellite observations and monitoring and forecasting systems. It provides free and fully open, regular and systematic reference information on the physical, biogeochemical ocean and sea-ice state for the global ocean and the European regional seas. The Copernicus Marine Service supports applications dealing with maritime safety, sustainable use of marine resources, healthy waters, informing coastal and marine hazard services, ocean climate services, protecting marine biodiversity.   For the last 10 years, the Copernicus Marine has been implementing unique capabilities to inform and support action for the Ocean.   Through a regular dialogue with the user community and taking into account observation, science and technology advances, the service is continuously evolving to better answer user and societal needs.   The first phase of Copernicus Marine 2 (July 2021 – December 2024) has ensured the continuity of service with respect to Copernicus Marine 1 (April 2014-July 2021) and implemented a series of evolutions for the Thematic Assembly Centers (TACs) and Monitoring and Forecasting Centers (MFCs).  User uptake has steadily increased with almost 80,000 registered users in 2025. International cooperation and impact have been strengthened in the framework of the UN Decade of Ocean Science.   In the second and last phase of Copernicus Marine 2 (January 2025 – June 2028), the objective is to build on these achievements to continue evolving Copernicus Marine product and service offer and maintain a world leading and state-of-the art marine service responsive to user and policy needs. Continuous evolutions of observation (TACs) and modelling (MFC) products will allow the integration of new satellite missions and in-situ observations, the improvement of processing techniques, the improvement of models, coupling and data assimilation, and an increasing use of Artificial Technique (AI) techniques. Synergies with the development of the EU Digital Twin Ocean will be strengthened.   An overview of recent achievements of Copernicus Marine will be given and the main objectives and scientific challenges of the new phase of Copernicus Marine 2 will be outlined.  Long-term perspectives (post 2028) will also be discussed. 

Digital Twins integrate continuously, in an interactive, two-way data connection, the real and the virtual assets. They provide a virtual representation of a physical asset enabled through data and models and can be used for multiple applications such as real-time forecast, system optimization, monitoring and controlling, and support enhanced decision making. These recent tools take advantage of the huge online volume of data streams provided by satellites, IoT sensing and many real time surveillance platforms, and the availability of powerful computational resources that made process-solving, high resolution models or AI-based models possible, to build high accuracy replicas of the real world.In this paper, the adaptation of the concept of Digital Twins is extended from the ocean to the coastal zones, handling the high non-linear physics and the complexity of monitoring these regions, using the on-demand coastal forecast framework OPENCoastS (Oliveira et al., 2020; Oliveira et al., 2021) to build a user-centered data spaces where multiple services, from early-warning tools to collaboratory platforms, are customized to meet the users needs. Computational effort and data requirements for these services is high, integration of Coastal Digital Twins in federated computational infrastructures, such as European Open Science Cloud (EOSC) or INCD in Portugal, to guarantee the capacity to serve multiple users simultaneously.This tool is demonstrated in the coastal area of Albufeira, located in the southern part of Portugal, in the scope of the SINERGEA innovation project. Coastal cities face growing challenges from flooding, sea water quality and energy sustainability, which increasingly require an intelligent, real-time management. The urban drainage infrastructures  transport to the wastewater treatment plants all waters likely to pollute downstream beaches. Real-time tools are required to support the assessment and prediction of the quality of bathing waters, to assess the possible need to prohibit beach water usage. During heavy rainfall events, a decentralized management systems can also contribute to mitigate downstream flooding. This requires the operation of the entire system to be optimized depending on the specific environmental conditions, and the participation and access to all the information by the several stakeholders. This system integrates real-time information provided by different entities, including monitoring networks, infrastructure operation data and a forecasting framework. The forecasting system includes several models covering all relevant water compartments: atmospheric, rivers and streams, urban stormwater and wastewater infrastructure, and receiving coastal water bodies circulation and water quality predictions.ReferencesA. Oliveira, A.B. Fortunato, M. Rodrigues, A. Azevedo, J. Rogeiro, S. Bernardo, L. Lavaud, X. Bertin, A. Nahon, G. Jesus, M. Rocha, P. Lopes, 2021. Forecasting contrasting coastal and estuarine hydrodynamics with OPENCoastS, Environmental Modelling & Software, Volume 143,105132, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2021.105132.A. Oliveira, A.B. Fortunato, J. Rogeiro, J. Teixeira, A. Azevedo, L. Lavaud, X. Bertin, J. Gomes, M. David, J. Pina, M. Rodrigues, P. Lopes, 2019. OPENCoastS: An open-access service for the automatic generation of coastal forecast systems, Environmental Modelling & Software, Volume 124, 104585, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2019.104585.

Solving many environmental challenges requires connecting a variety of datasets with advanced statistical or AI models, and to access distributed computing resources to create workflows or digital twins. Digital twins are particularly challenging because they require the integration of many datasets and models, resulting in complex interactions. Furthermore, researchers rely on interdisciplinary collaboration to build workflows and digital twins. To that end, they need to discover, reuse, and sometimes modify existing assets. While this can be supported by Virtual Research Environments (VREs), most existing solutions are tailored to a specific domain or use case, making it difficult to integrate external resources or to support the complex model composition required by digital twins.To address these limitations, we have developed Notebook-as-a-VRE (NaaVRE), a VRE solution built on top of JupyterLab. NaaVRE enables researchers to create computational blocks by containerizing the cells of notebooks, to organize them into workflows, and to manage the full experimental cycle, including data and workflow sharing. The tool includes features such as metadata-driven resource discovery, workflow automation, and compatibility with external repositories. Designed for cloud infrastructures, NaaVRE provides cost-efficient and scalable solutions to support digital twin development.Using NaaVRE, we build customized virtual labs to address specific scientific problems. These gather models, data access tools, workflows, and documentation into a shared space, where a community of users can develop, share and reuse them. We present a framework to guide the development and operations of virtual labs throughout their entire lifecycle.We showcase NaaVRE and by building customized virtual labs for scientific data processing workflows and prototype digital twins. Those virtual labs are managed by LifeWatch ERIC and the University of Amsterdam following the aforementioned development framework. They include characterizing ecosystem structures with LiDAR data, tracking bird migration using radar, mapping invasive species, deriving essential variables in the context of the ENVRI-Hub NEXT project, and developing digital twins of ecosystems in the context of the Dutch NWO LTER-LIFE project.

Addressing complex Earth System Science challenges is currently hindered by a pervasive fragmentation of the research ecosystem. This disconnect extends beyond data dispersion to include siloed organizational structures and isolated disciplinary communities, limiting the potential for holistic environmental analysis and cross-domain innovation. In response to the session's call for successful synergy examples, we present ITINERIS (Italian Integrated Environmental Research Infrastructures System). ITINERIS serves as a strategic operational model for shaping the Research Infrastructure (RI) landscape by integrating the Italian national nodes of 22 RIs across four critical domains: atmosphere, marine, terrestrial biosphere, and geosphere. This network encompasses ESFRI Landmarks (ACTRIS, EMSO, ICOS, Euro-Argo and LifeWatch), ESFRI Projects (e.g., e-LTER, DANUBIUS), EU RIs (e.g., ECORD), and key national RIs (e.g., the Laura Bassi research ship). We demonstrate how ITINERIS contributes to the European Open Science Cloud (EOSC) through three core pillars:• The ITINERIS HUB: An integrated digital platform that transforms fragmented RI repositories into a unified discovery and analysis layer. By combining a centralized metadata catalogue (populated via automated harvesting) with thematic Virtual Research Environments and advanced access and training services, the HUB acts as a fundamental building block of the Italian EOSC Node, enabling advanced analyses, AI‑driven workflows and models across more than 500,000 environmental datasets, backed by a vast array of services and resources from diverse RIs• Cross-Disciplinary synergies: Innovative use cases that overcome domain boundaries, such as Nature-Based Solutions and climate mitigation. These scenarios demonstrate the power of combining atmospheric observations with marine and terrestrial ecosystem data, enabling a holistic assessment of environmental compartments that was previously unattainable due to infrastructure silos.• National Access Framework: A harmonized operational framework serving as a blueprint for future nationally-funded access programs. Building on the success of the ITINERIS-ACTRIS pilot call, which tested a unified governance for physical, remote, virtual access and hybryd access, this model provides a validated approach for reducing administrative barriers and expanding access opportunities for the wider user communities. We invite the community to explore ITINERIS as a replicable model for national aggregation strategies, discussing the governance and sustainability challenges of this multi-stakeholder initiative and sharing best practices for other national clusters. By aligning national strategies with European standards like FAIR and ENVRI-FAIR, ITINERIS provides a validated roadmap and a scalable template for 'joining forces' to build a unified, interoperable European environmental research landscape.

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.

The impacts on ecosystems of global and local shifts in the environment due to e.g. climate change, land use change, and urbanisation present us with complex and interacting societal challenges. Solving a single problem often aggravates another. Scientific insights needed to face these challenges are based on interdisciplinary multi-scale research. It requires that we work together across (sub)disciplinary boundaries and combine our insights and knowledge to understand the complexities and interrelatedness within ecosystems. Making this combination is currently hampered by the availability and scatteredness of datasets and the lack of methods and tools to combine knowledge, data and models. Developing digital twins of ecosystems could provide a paradigm to achieve this combination of knowledge, data and models, and generate the insight and knowledge that we require to face the challenges of global and local change. A digital twin of an ecosystem is a digital representation of an ecosystem tailored to the user’s research objectives and perspective of that ecosystem. They bring together (long-term) data collected of multiple ecosystem facets and across multiple scales, process-based models, and data science and computational modelling tools, making them a versatile instrument. Digital twins can be used to understand ecosystem functioning, by aiding the discovery of intricate ecological relationships not yet understood or measured. Furthermore, they are ideal for exploring the effects of in situ management strategies, or for the detection of early-warning signals of global change impacts on ecosystem dynamics. LTER-LIFE is a scientific infrastructure, embedded in LTER-NL (Long-Term Ecosystem Research Netherlands) and LifeWatch ERIC, aimed at supporting shared and integrated ecological research, providing scientists the platform and tools to build digital twins of ecosystems. We do this by supporting the sharing of data and models and bringing these together into a site-focused virtual lab where people can work together. LTER sites provide an ideal opportunity for this as they often come with a long history of extensive data collection and research across multiple spheres. Drawing on experience of building digital twins in the Veluwe LTSER platform, this keynote will take you through the journey of digital twinning parts of an ecosystem with a long legacy of ecological and environmental research and data collection. As these data exist in many different formats, we built data mobilisation and FAIRification pipelines to augment the focal experimental sites in the Veluwe with relevant historical, privately-owned or national datasets. Subsequently, we explored the potential of the Notebook as a Virtual Research Environment (NaaVRE), which provided us with a virtual lab in which we developed workflows for processing and linking FAIRified data with process-based and data-driven models. Ultimately, we show that this way of working can bring together researchers to generate insights on the impacts of climate change, land use change, and urbanisation on the functioning of the ecosystem, providing scientific support for management and policy decisions.

Recently there’s been a lot of enthousiasm for the concepts of digital twins, virtual research environments, serious games, and other inspiring ideas to improve “the way we do science.” With eWaterCycle, we are no stranger to the cause. We’ve worked hard to build a platform that could make the scientific process – specifically, hydrological modelling – more accessible and engaging.   eWaterCycle gives users access to a centralized platform where they can perform hydrological experiments: simulating how water flows through a catchment area of choice. Complete with data, a suite of models, an interactive scripting environment, and a graphical explorer to quickly setup an experiment. It shares many characteristics with what is commonly understood of a digital twin. But is it, really?   Sadly, the concept of digital twins suffers from linguistic inflation. At a recent event on the topic, the main coffee chatter was along the lines of “but what actually is it?” In an arena filled with resonating buzz, a clear image can help to regain focus and a common frame of reference. Is eWaterCycle, as a platform that supports working with each other’s models and data, a digital twin? Or is it more an incubator of digital twins? Either way, eWaterCycle can help make things concrete and specific, because it already exists.   With several new projects promising to build digital twins of all sorts, we hope our experience can feed into the discussions on and development of new digital twins. Therefore, at EGU, we would like to reflect on the essence of our platform and our experience in building it. What is it (not)? What’s in it for you? What challenges did we face? And what does that mean for open science and collaborative research? 

• The objectives of this study match the CIE content as it provides academcians and practioners with innovative and helpful information on advanced computing technology in the industry. • Foundation models are large-scale machine learning models that have been trained on large amounts of data to perform a variety of tasks. • FMs are used to support management and operations in supply chain and logistics management and operations. • The paper is based on research conducted by the authors with several Italian and European organizations under a grant from the European Union's Next GenerationUE initiative. • The paper concludes with a real-world industrial case study to further substantiate these claims. The organizational environment is subject to constant change, driven by rapid technological progress and the advent of Industry 5.0. This dynamic scenario compels authors and practitioners in the fields of logistics and supply chain management and operations (LSCM) to explore the transformative impact of foundation models (FMs) and artificial intelligence (AI) on these areas. The integration of AI FMs, coupled with digital twins (DT) and cyber-physical systems (CPS), will not only optimize current processes but also fundamentally reshape the future of digital engineering (DE) in LSCM. Understanding the opportunities and challenges with FMs is critical to the strategic positioning in an ever-evolving marketplace where Industry 5.0 technologies drive innovation and efficiency. Organizations can gain a competitive advantage by analyzing the opportunities and risks of integrating FMs into LSCM operations and leveraging their ability to improve decision making and enhance predictive analytics and real-time monitoring through DT and CPS. This paper serves as a comprehensive and informative guide for evaluating the transformative potential of FMs in redesigning LSCM operations in the context of Industry 5.0, highlighting critical applications, exploring potential benefits and challenges, and pointing to upcoming innovations such as AI-driven DT and smart cyber-physical systems that are revolutionizing supply chain (SC) processes. The detailed analysis based on the Supply Chain Operations Reference (SCOR) components shows that FM-based solutions, supported by AI and Industry 5.0 technologies, can significantly improve LSCM business processes. This paper presents an integrated functional framework and technical architecture for implementing FM in LSCM, emphasizing the role of DT and CPS in enabling smart, adaptive, and resilient SC. The paper concludes with an industrial case study that further substantiates these claims and shows the practical application of FM in a shipbuilding organization. The case study describes successful approaches to implementing and effectively utilizing AI and DT. It provides a practical, functional, and technical framework for applying the transformative impact of FMs on LSCM within the Industry 5.0 paradigm. This integration highlights the synergy between FM, AI, DT, and CPS and paves the way. It paves the way for a more innovative, sustainable, and competitive management of supply networks and their operations.