Large Research Infrastructures in the field of frontier Physics have opened new observational windows to the universe and explore the structure of matter in extreme detail. These advancements require high levels of expertise and sophistication. On the other hand, society, lacking this level of expertise, merely observes these developments through outreach activities but does not actively contribute in the development of new scientific knowledge. This fact induces a gap between frontier science and society that can spawn misconceptions about the content, context and mission of public funded frontier science. The REINFORCE project comes to answer to the questions: a) Can citizens contribute in the development of new knowledge in frontier science? b) Can citizens apply this new knowledge to solve societal problems? c) How can we integrate citizen feedback? REINFORCE goes beyond outreach programmes and aims to minimize the gap between Society and Large Research Infrastructures in the field of Physics through the

From edge to cloud, big data analytics is growing fast, and its energy consumption has become a reason of concern for national grids and they generate significant carbon emissions. The GLACIATION project aims to address this issue through energy-efficient data operations. By developing a novel Distributed Knowledge Graph (DKG) that stretches across the edge-core-cloud architecture, reduction in the energy consumption for data processing will be achieved through AI enforced minimal data movement operations. GLACIATION will achieve significant power consumption reduction through optimizing the location where analytics are carried out. The projects Meta Data framework will provide tools that incorporate privacy and trust aspects in the data operations. GLACIATION is demonstrated on three relevant industry settings which benefit from optimized data movement and power consumption reduction. More specifically, GLACIATION use cases cover public-service, manufacturing, and enterprise data analytics.

TEMA will greatly improve Natural Disaster Management (NDM, e.g., for wildfires, floods) by automating precise semantic 3D mapping and disaster evolution prediction to achieve NDM goals in near-real-time. It will analyze and fuse many heterogeneous extreme data sources: smart drone and in-situ sensors, remote sensing data, topographical data, meteorological data/predictions and geosocial media data (text, image and videos). TEMA will focus on the extreme nature of the data, due to their varying resolution and quality, very large volume and update rate, different spatiotemporal resolutions and acquisition frequencies, real-time needs and multilingualism. It will develop an integrated, ground-breaking NDM platform, focusing on real-time semantic extraction from multiple heterogeneous data modalities and sources, on-the-fly construction of a meaningful semantically annotated 3D disaster area map, prediction of disaster evolution and improved communication between service providers and end-users, through automated process triggering and response recommendations. Semantic analysis computations will be distributed across the edge-to-cloud continuum, in a federated manner, to minimize latency. Extreme data analytics will be performed in a trustworthy and transparent way, by greatly advancing state-of-the-art AI and XAI approaches. The constantly updated 3D map and the disaster evolution predictions will form the basis for an advanced, interactive, Extended Reality (XR) interface, where the current situation will be visualized and different response strategies will be dynamically evaluated through simulation by NDM personnel. The innovative, scalable and efficient TEMA platform will provide precise NDM support, based on extreme data analytics. It will be validated on two critical disaster use-cases (wildfires and floods), in four EU countries, and will form the basis for the TEMA NDM-Analytics-as-a Service (NDM-AaaS) model.

The main goal of Co-VAL is to discover, analyse, and provide policy recommendations for transformative strategies that integrate the co-creation of value in public administrations. The project aims to accomplish these objectives by conducting research on the paradigm shift from the traditional top-down model to demand and bottom-up driven models when citizens, civil servants, private, and third sector organizations voluntarily participate in the development of transformative innovations addressing changing needs and social problems. Co-VAL will push the boundaries of both research and practice by providing: i) a comprehensive and holistic theoretical framework for understanding value co-creation in public services from a service-dominant logic and a service innovation multiagent framework, ii) measurement and monitoring for transformations in the public sector by using both existing data and new metrics (large-scale survey), iii) investigation on 4 public-service-related co-creation areas of public sector tr

The digital transformation of food systems has entered a twilight zone: data-driven innovations have proven to be promising, but it is still unclear how to upscale adoption and have broader acceptance. The Data4Food2030 project aims to improve the data economy for food systems (DE4FS) by expanding its definition, mapping its development, performance and impact to create new insights and opportunities. This contributes to a more competitive and sustainable food system in the EU and supports implementation and adaptation of relevant policies such as a Digital Single Market, Green Deal and the Common Agricultural Policy. Data4Food2030 is a 4-year project that aims to 1) enlarge the knowledge base and insight into the DE4FS, 2) develop a system that monitors and evaluates the development, performance and impact of the DE4FS on relevant EU policies 3) identify drivers and barriers and turn these into opportunities, recommendations and solutions, 4) test solutions and evaluate recommendations in case studies and through stakeholder dialogues and 5) provide future scenarios and a roadmap and sustain the monitoring system to support policy development and accelerate the desired future state of the DE4FS. Data4Food2030’s approach is targeted at an improved future state of the DE4FS from which clear design principles, recommendations and solutions are derived for improving and adapting policies and practices at public and private level. As an essential part of the project, stakeholders are deeply engaged to provide input to various DE4FS concepts and evaluate several project outcomes to increase the impact of the project. Nine case studies provide real-life examples of the DE4FS at micro- and meso-economic level, deploying data and technologies, which are used for mapping and improvement to promote data-enabled business models. In this way, Data4Food2030 creates credible pathways to navigate properly through the twilight zone towards a fair, inclusive and innovative DE4FS.