CYBELE generates innovation and create value in the domain of agri-food, and its verticals in the sub-domains of PA and PLF in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.

Weather and climate extremes pose challenges for adaptation and mitigation policies as well as disaster risk management, emphasizing the value of Climate Services (CS) in supporting strategic decision-making. Today CS can benefit from an unprecedented availability of data, in particular from the Copernicus Climate Change Service(C3S), and from recent advances in Artificial Intelligence (AI) to exploit the full potential of these data. The main objective of CLINT is the development of an AI framework composed of Machine Learning (ML) techniques and algorithms to process big climate datasets for improving Climate Science in the detection, causation and attribution of Extreme Events (EE), including tropical cyclones, heatwaves and warm nights, and extreme droughts, along with compound events and concurrent extremes. Specifically, the framework will support (1) the detection of spatial and temporal patterns, and evolutions of climatological fields associated with EE, (2) the validation of the physically based nature of causality discovered by ML algorithms, and (3) the attribution of past and future EE to emissions of greenhouse gases and other anthropogenic forcing. The framework will also cover the quantification of the EE impacts on a variety of socio-economic sectors under historical, forecasted and projected climate conditions by developing innovative and sectorial AI-enhanced CS. These will be demonstrated across different spatial scales, from the pan European scale to support EU policies addressing the Water-Energy-Food (WEF) Nexus to the local scale in three types of Climate Change Hotspots. Finally, these services will be operationalized into Web Processing Services, according to most advanced open data and software standards by Climate Services Information Systems (CSIS), and into a Demonstrator to facilitate the uptake of project results by public and private entities for research and CS development.

Today's building permit issuance is mainly a manual, document-based process. It therefore suffers from low accuracy, low transparency and low efficiency. This leads to delays and errors in planning, design and construction. Several EU countries have developed attempts to push forward the digitalisation of building permit procedures. But none of these have led to complete adoption of digital building permit processes within municipalities. The aim of CHEK is to take away barriers for municipalities to adopt digital building permit processes by developing, connecting and aligning scalable solutions for regulatory and policy context, for open standards and interoperability (geospatial and BIM), for closing knowledge gaps through education, for renewed municipal processes and for technology deployment in order to reach TRL 7. CHEK will do this by providing an innovative kit of both methodological and technical tools to digitise building permitting and automated compliance checks on building designs and renovations in European urban areas and regions. The CHEK consortium consists of a multidisciplinary team covering GIS, BIM, municipal processes and planning, data integration and standardisation. In addition, the consortium is a multisectoral mix of research&education, AEC- and software-companies, governmental institutions, and international standardisation organisations. The multisectoral and multidisciplinary consortium is essential to align and connect all aspects of digital permit processes required to meet the highly ambitious project objectives. Several partners are already collaborating in the European Network for Digital Building Permit (EUnet4DBP). The institutions in the advisory board, representing governments and municipalities of other European countries, will further assist the development, exploitation, and upscaling of results. The best practices and developed software following the logic of OpenAPI will enable replicability in any other European country.

SEADOTs (Social-Ecological Ocean Management Applications using Digital Ocean Twins) has the objective of advancing holistic, just and sustainable ocean management by bringing a predictive component for social-ecological aspects into comprehensive digital ocean twins (DOTs). These DOTs will combine digital twins of the ocean (DTO) with human activities in the ocean and combine socio-ecological and socio-economic data with ocean data, ecosystem data, and a variety of models. By creating and demonstrating applications in the Norwegian North Sea, the Southern North Sea and the Baltic Sea that address current challenges and developments and can simulate the intricate interactions between human activities and marine ecosystems, SEADOTs aims to facilitate and inform political decision making, marine spatial planning and adaptive management. SEADOTs ambition is to help safeguard ocean ecosystems, promote sustainable resource use, and enhance social and economic well-being. The project will leverage developments from ongoing Mission and Green Deal projects where partners are involved in, including the European Digital Twin projects Iliad and EDITO, OLAMUR and CLIMAREST and demonstrate Ocean Management Applications with Digital Ocean Twins on the EU Digital Twin Ocean (DTO) infrastructure as well as distributed platforms for socio-ecological, socio-economic and political endpoints. For that purpose will SEADOTs work with data acquisition and beyond the state of the art and the objective to provide spatially-explicit social-ecological data and data interoperability with geospatial ocean data also after the project period in suitable repositories, through stakeholder capacity building and through collaboration with the co-funded projects of this call. The SEADOTS consortium was built across scientific and technical excellence and is accompanied by an Advisory Board that spans marine spatial planning, political aspects, gaming and social science as well as Ocean Best Practices.