PLANET4Health provides new knowledge and tools on environment degradation and its impact on human animal and ecosystems health. The project results will support policy making process and citizens awareness on sustainable planetary health, climate and environmental policies and adaptation and mitigation strategies to natural hazards. PLANET4health will develop collaborations from a large variety of organizations from the: environmental and climate science, public health, epidemiology, veterinary medicine, social, political and economic science, engineering, law and ethics, and communication, to produce solid knowledge and tools to facilitate learning and practice on the interaction between the natural system and human health. Four tailor-made case studies will be performed: 1) One Health effects of vector-borne diseases, 2) air pollution, 3) food contamination arising from soil and water contamination, 4) mental wellbeing linked to environmental and climate stressors, in different geographical area thanks to the large project network, that will draw universal conclusions and replicable solutions to improve the predictive capability and preparedness. The consortium will produce research, technological innovation, tailored outreach and training, and policy solutions through a cross-sectorial multidisciplinary scientific collaboration in line with the transnational character of planetary health. For these aims the project will: a) collect, organize and assure open availability of new and already existing data on climate and environmental indicators linked to One Health; b) carry out analyses on data and build innovative, inter-operable and multifunctional digital prototypes; c) produce new knowledge and tools to support One Health policies by applying social science theories and involving citizens, policymakers and stakeholders; d) offer open-access data, tools and research material to public authorities for decision making and academic research for further study.

DigiSolar is set to revolutionize large-scale solar thermal (ST) systems by seamlessly integrating them with other renewable energy sources, enabling hybrid-ST solutions that drive decarbonization, electrification, and energy flexibility. This transformative approach will accelerate the energy transition for industrial process heat and district heating networks, making clean energy more accessible, cost-effective, and future-ready. At its core, DigiSolar will deliver DS Suite, a unified digital platform designed to optimize every stage of hybrid solar thermal system’s lifetime. DS Suite leverages advanced digital twin technology with AI-driven fault detection, supply and demand forecasting and model predictive control (MPC), as well as life cycle assessment. The DS Suite will empower planners, operators, and end-users with cutting-edge tools for performance verification, predictive maintenance, and real-time energy optimization. Its investor-trusted planning capabilities and user-friendliness, currently lacking in the market, will unlock large-scale solar thermal investments, accelerate solar thermal market penetration, by de-risking and boosting deployment. DigiSolar’s innovative business models further enhance cost-competitiveness, lowering return-on-investment periods while ensuring a reliable, carbon-free energy supply. Backed by a highly skilled consortium of 3 leading R&D institutions and 9 industry partners—including solar thermal technology providers, engineering firms, utilities, and digital solution experts—the project brings together the expertise needed to confidently develop, validate, and deploy DS Suite at scale in 4 demo-sites towards 100% decarbonisation industrial, district heating and cross-sectoral applications of solar thermal in South and West Europe. By bridging the gap between technology and implementation, DigiSolar will strengthen Europe’s leadership in renewable energy, paving the way for a resilient and sustainable energy future.

The FAIR principles provide a framework for enabling proper access and reusability of scientific data, and implementing them is a key goal of the European Open Science Cloud (EOSC). However, providing access to sensitive or confidential data while preserving privacy/confidentiality and usability for researchers is still an open question. Existing solutions like safe rooms, safe pods, or data safe havens are often challenging for the development of reproducible research and seem counter-intuitive when dealing with open science and FAIR principles. The SIESTA project aims to provide a set of tools, services, and methodologies for the effective sharing of sensitive data in the EOSC, following a cloud-based model and approach. SIESTA will provide user-friendly tools with the aim of fostering the uptake of sensitive data sharing and processing in the EOSC. The project will deliver trusted cloud-based environments for the management and sharing of sensitive data that are built in a reproducible way, together with a set of services and tools to ease the secure sharing of sensitive data in the EOSC through state-of-the-art anonymization techniques. The overall objective is to enhance the EOSC Exchange services by delivering a set of cloud-based trusted environments for the analysis of sensitive data in the EOSC demonstrating the feasibility of the FAIR principles over them.

The AI4EOSC (Artificial Intelligence for the European Open Science Cloud) delivers an enhanced set of advanced services for the development of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) models and applications in the European Open Science Cloud (EOSC). These services are bundled together into a comprehensive platform providing advanced features such as distributed, federated and split learning; novel provenance metadata for AI/ML/DL models; event-driven data processing services or provisioning of AI/ML/DL services based on serverless computing. The project builds on top of the DEEP-Hybrid-DataCloud outcomes and the EOSC compute platform and services in order to provide this specialized compute platform. Moreover, AI4EOSC offers customization components in order to provide tailor made deployments of the platform, adapting to the evolving user needs. The main outcomes of the AI4EOSC project will be a measurable increase of the number of advanced, high level, customizable services available through the EOSC portal, serving as a catalyst for researchers, facilitating the collaboration, easing access to high-end pan-European resources and reducing the time to results; paired with concrete contributions to the EOSC exploitation perspective, creating a new channel to support the build-up of the EOSC Artificial Intelligence and Machine Learning community of practice.