The PISTIS project aimed to build a secure and trusted platform for sharing and trading data assets, utilizing technologies like blockchain, NFTs, and AI to ensure transparency and fair monetization. It sought to foster interoperable data spaces, facilitating collaboration and data flow across different sectors while complying with data regulations and empowering organizations to unlock the value of their data. By advancing data sharing technologies and promoting trust, PISTIS strove to revolutionize how data is exchanged and utilized, driving innovation and economic growth.
AEGIS brought together the data, the network & the technologies to create a curated, semantically enhanced, interlinked & multilingual repository for public & personal safety-related big data. It delivered a data-driven innovation that expanded over multiple business sectors & took into consideration structured, unstructured & multilingual datasets, rejuvenated existing models and facilitated organisations in the Public Safety & Personal Security linked sectors to provide better & personalised services to their users.
DataVaults goal was to set, sustain and mobilize an ever-growing ecosystem for personal data and insights sharing and for enhanced collaboration between stakeholders (data owners and data seekers). This relied exactly on the DataVaults personal data platform’s extra functionalities and methods for retaining data ownership, safeguarding security and privacy, notifying individuals of their risk exposure, as well as on securing value flow based on smart contract. DataVaults aimed to deliver a framework and a platform that had personal data, coming from diverse sources in its centre and that defined secure, trusted and privacy preserving mechanisms allowing individuals to take ownership and control of their data and share them at will, through flexible data sharing and fair compensation schemes with other entities (companies or not). The overall approach rejuvenated the personal data value chain, which could from then on be seen as a multi-sided and multi-tier ecosystem governed and regulated by smart contracts which safeguard personal data ownership, privacy and usage and attributes value to the ones who produce it.
European organisations face a double bind. Real-world data in many sensitive domains is scarce, fragmented, biased or too confidential to share, while the obligations that govern its use — GDPR, the AI Act, the Data Act and sector-specific rules — keep expanding. Synthetic data can close the data gap, but current pipelines offer little proof that a generated dataset is representative, unbiased and lawful to use, which leaves organisations unable to rely on it where it would help most.
SYNTACTIC builds a European “one-stop-shop” platform for the entire synthetic data lifecycle, organised as five toolboxes over a FAIR-by-design foundation: synthetic data generation, with multi-modal privacy-preserving generative models, digital-twin augmentation, model unlearning, robustness and explainability; regulatory auditing and management, with automated checks against GDPR, the AI Act and the Data Act, translation of legal obligations into technical ones, and auditable DPIA/FRIA workflows; data lifecycle management, covering provenance, curation, versioning, annotation and benchmarking against real-world data; cybersecurity, enforcing zero-trust, threat intelligence and DevSecOps; and an interoperability gateway that connects securely and semantically to European Data Spaces and initiatives such as the AI Regulatory Sandboxes and AI-on-Demand. A dedicated Knowledge Hub adds guided workflows, training modules and best practice to raise AI literacy and compliance awareness among European SMEs and professionals.
The platform is developed in an agile, iterative cycle and validated in six pilots spanning telecommunications, maritime, Green Deal and industrial compliance, finance, aerospace (IRIS²) and healthcare — each measuring gains in data quality and representativeness, bias mitigation, regulatory alignment and secure data-space interoperability. By coupling generative modelling with compliance-by-design, SYNTACTIC aims to reduce the cost and time of compliance and widen access to high-quality synthetic datasets, with results reusable beyond the project.
RenAI drives a new paradigm in the use of Generative AI for science by delivering a suite of domain-specific and cross-domain foundation models, co-created with European research infrastructures and scientific communities. Trained on curated multimodal datasets — imaging, time series and tabular data — these models support a wide range of downstream applications, from environmental monitoring to health research, while ensuring reproducibility, robustness and scientific credibility.
The project creates a federated ecosystem, aligned with the EOSC federation strategy, where researchers can easily access, fine-tune and apply these models, supported by tools for data discovery, transformation, benchmarking and compliance. Synthetic data generation further enhances research capacity by enabling experimentation in data-scarce or privacy-sensitive domains. To ensure usability, RenAI provides the coResearcher, an intuitive LLM-based assistant that lowers technical barriers and enables seamless interaction with models, services and workflows.
RenAI builds on and extends initiatives such as AI4EOSC, iMagine and EOSC Data Commons, embedding its services into the EOSC federation to guarantee interoperability, openness and long-term sustainability. The project also establishes strong links with EOSC governance and EU policy frameworks, aligning its outputs with the AI Act, FAIR principles, open science requirements, and security and ethics standards.
Beyond technical innovation, RenAI emphasises uptake and impact through training programmes, community engagement and cross-disciplinary collaboration, ensuring that infrastructures and researchers alike benefit from its outputs. By integrating ethical, transparent and trustworthy AI into scientific practice, RenAI positions Europe at the forefront of applying GenAI to accelerate discovery and address societal challenges across domains.
AI-DAPT aims to deliver an innovative and impactful research agenda that will provide tangible benefits to a variety of stakeholders that struggle with making AI services. Seeking to reinstate the pure data-related work in its rightful place, and reinforcing the generalizability, reliability, trustworthiness, and fairness of Al solutions, AI-DAPT vision relies on the implementation of an AIOps framework to support and automate AI pipelines that continuously learn and adapt based on their context. It enables proper purposing, collection, documentation, (bias) valuation, annotation, curation and synthetic generation of data, while keeping humans-in-the-loop across five axis: (i) Data Design for AI, (ii) Data Nurturing for AI, (iii) Data Generation for AI, (iv) Model Delivery for AI, (v) Data-Model Optimization for AI.
INSIEME (Italian for ‘together’) aims to deploy a Common European Energy Data Space (CEEDS) to facilitate data sharing, interoperability, and innovation in the energy sector. The project will build on previous initiatives like the Energy Data Space Cluster Projects and establish a federated architecture to connect existing data exchange platforms across Europe.
Key objectives include developing standardized data formats, security protocols, governance frameworks, and business models to enable secure and trusted data exchange between utilities, market actors, energy communities, and consumers. INSIEME will implement core CEEDS components and deploy them across multiple use cases and pilots in over 15 EU countries, covering areas such as energy efficiency, flexibility management, collective self-consumption, grid services, electromobility, renewable integration, and sector coupling.
The versatile consortium comprises of partners, including technology providers, data space operators, distribution and transmission system operators, energy community platforms, artificial intelligence experts, and EU associations. Through field tests, standards development, and stakeholder engagement, INSIEME aims to provide a secure, interoperable, and financially sustainable data space that accelerates the clean energy transition by enabling innovative energy services and empowering consumers across borders.
More information: https://insieme.energy/index.html