The emergent European Data Economy relies on the availability of data as a basis for further innovation and exponential development of technologies, especially the development of trustworthy ‘made in Europe’ AI that reflects European values. Data Spaces, platforms and marketplaces are enablers, key to unleash the potential of such data. However, data sharing and data interoperability are still at their infancy. Through DataBri-X, European Data Spaces, platforms and marketplaces and their wide range of business, governmental and public, research and civil society stakeholders will be equipped with a holistic and flexible data governance process and a seamless integrated standards based toolbox for data- and metadata management which can be assembled along relevant requirements, provides open source as well as commercial tools (the bricks / bri-X), and mechanisms to load 3rd party resources like language resources or AI models, and can be easily deployed into Data Spaces and thereby will contribute to make Europe the most successful area in the world in terms of data sharing and data re-use, to gain the full benefit from the value of data, while respecting the legal framework relating to security and privacy. The project's objective is to provide a holistic, energy-efficient and user-friendly toolbox of practical, robust and scalable bricks/Bri-X (processes, technologies and tools) that improve the interoperability, usability, discoverability, quality, and integrity of data and metadata, with the aim of making data sets ready for expanded digital value creation in the context of European Data Spaces. The DataBri-X toolbox will be offered in compliance with accountability, fairness, privacy, and confidentiality regulations as well as FAIR principles and will build on existing and emerging initiatives. The DataBri-X consortium comprises 14 partners from 6 EU members and 1 associated country (UK), that together form a complete value chain of actors.

Artificial Intelligence (AI) is transforming research, industry, and society, with Large Language Models (LLMs) playing a central role. While LLMs excel in natural language understanding, reasoning, and content generation, they also exhibit hallucinations, security vulnerabilities, ethical concerns, and regulatory issues. These challenges are particularly critical in healthcare and education, where accuracy, reliability, and fairness are essential. Addressing these shortcomings requires AI paradigms that enhance interpretability, robustness, and compliance. THIRDWAVE aims to establish an international, interdisciplinary network to advance LLM-driven neuro-symbolic AI, integrating symbolic AI with LLMs to create interpretable, reliable, and domain-aware systems. This approach enables AI to leverage structured knowledge, improve decision-making, and comply with domain-specific constraints, making it more applicable to real-world challenges. The project is structured around four key objectives: O1) Understanding LLMs: Analyzing capabilities and limitations to improve performance, usability, and trustworthiness. O2) Enhancing LLMs: Improving fairness, factual accuracy, and robustness through external knowledge sources and human collaboration. O3) Advancing LLM-driven Neuro-Symbolic AI: Developing hybrid systems that combine LLMs with symbolic reasoning for structured knowledge representation and better decision support. O4) Use Cases & Evaluation: Applying LLM-driven neuro-symbolic AI in healthcare, education, geodata, and food information engineering, validating scalability and societal impact. By fostering collaboration among AI researchers, domain experts, and industry partners, THIRDWAVE will bridge the gap between data-driven and knowledge-driven AI, ensuring LLMs become interpretable, ethically aligned, and domain-aware. The project’s findings will inform AI regulation, advance research, and drive innovation, contributing to responsible AI development.

In support of the development and adoption of ECCCH by Cultural Heritage Professionals and Researchers (CHPR), ECHOLOT will make the creation, provision, and reuse of high-quality, semantically rich, and interoperable Cultural Heritage (CH) data accessible to scholars and institutions, significantly lowering the threshold for joining the collaborative cloud. ECHOLOT will address the fundamental issue that, while ever larger amounts of diverse CH data are available through CH institutions (CHIs), the active reuse of this data especially in research and the creative sectors, is hampered by poor quality and lack of interoperability. It will achieve this by seamlessly integrating as a core service in the ECCCH that enables the quality curation and enrichment of CH data, including multimedia, through AI-enhanced workflows combining automated processing and human input. Moreover, it will natively support embedding rights metadata and chain-of-production provenance, thereby preserving the value and integrity of CH datasets. ECHOLOT will serve as an interoperability hub facilitating the exchange of CH data between systems. It will enable CHPRs to publish simultaneously to aggregators, such as Europeana, and open knowledge platforms from the Wikimedia ecosystem, as well as contribute to ECCCH and Common European Data Space for Cultural Heritage (DS4CH). Together, these technical innovations will revolutionise CHPR practices and increase the availability of CH data for reuse and collaboration across institutional and sectoral boundaries. ECHOLOT’s solutions will be validated and tested in five case studies presenting a wide spectrum of CH actors. Maximising the adoption of ECHOLOT and ECCCH will be enabled by social and organisational change measures, including innovative business models co-created with relevant stakeholders. Training resources, interactive workshops and open source software best practices will further support capacity building and long-term sustainability.

The LUMEN project is a groundbreaking initiative aimed at revolutionizing cross-domain collaboration and discovery processes in the fields of Mathematics (Maths), Social Sciences and Humanities (SSH), Earth System (ES), and Molecular Dynamics (MD), and beyond. Leveraging the successful GoTriple platform, renowned for its service to the SSH community, LUMEN seeks to extend its functionality to foster interoperability across scientific domains. Through interdisciplinary solutions spanning all four domains, LUMEN will redefine the process of discovery with radical innovations, simplifying initial research phases and facilitating access to advanced AI-powered tools for researchers. By expanding existing discovery platforms in the SSH and Maths domains and developing new platforms for other domains, LUMEN aims to fundamentally transform EOSC services, promoting innovative and customizable solutions for data discovery, attracting new users, and fostering Open Science principles. The project will also drive multidisciplinary cooperation through collaborative platforms and onboard new scientific communities into EOSC, providing them with the necessary tools. Ultimately, LUMEN seeks to revolutionize how research outputs are created, shared, and utilized across scientific domains, enhancing scientific discoveries, fostering interdisciplinary collaboration, and promoting innovation and trust in European scientific research.

Social Sciences and Humanities (SSH) provide essential knowledge to society, informing our shared cultural, economical and ethical decisions. However, SSH knowledge remains largely disconnected and poorly available, impeding its full potential in research and societal applications. The GRAPHIA project aims to build the first comprehensive SSH Knowledge Graph (KG) that integrates currently disconnected data into a single entry point, leveraging existing infrastructure and data resources. GRAPHIA aspires to significantly improve SSH data visualisation and analysis capacities, pioneer advanced AI solutions tailored for SSH and develop re-usable, cutting-edge digital tools. GRAPHIA addresses the unique opportunities posed by the qualitative, diverse and context-rich data typical of SSH research where existing solutions fall short. GRAPHIA's innovative approach, centered around the KG, provides an expansive representation of SSH knowledge, while leveraging AI for its enrichment, access and deeper analysis, including an LLM4SSH. GRAPHIA aims to empower researchers to uncover patterns and insights from unstructured data, illuminating social phenomena and cultural trends with unprecedented clarity. A key component of GRAPHIA is the SSH Citation Index, an innovative framework for citation data extraction and enrichment across all SSH disciplines that dramatically speeds up access and understanding of previous literature on any topic. GRAPHIA transcends traditional research approaches by integrating industry partners into a cohesive partnership, which is pivotal to amplify the project's impact, drive forward innovations that are not only academically significant but also commercially viable, provide access to new markets and technologies and foster an environment of co-innovation. GRAPHIA is committed to open science and increasing EU Research Infrastructures capabilities, enhancing global competitiveness, while facilitating broad and long-lasting impact of project results.