The IFCA Advanced Computing and e-Science group leads and participates in a number of research and innovation projects.
FAIR Liquidity Unifying Interoperable Data and AI (FLUID-AI)
The FLUID-AI project introduces a new approach to address the lack of interoperability between data, AI/ML models and solutions within the EOSC. We introduce the concept of Data and Models Liquidity, building on and extending the FAIR principles to address the unique demands of AI-ready data and models. While the FAIR principles have improved data management, they fall short in supporting AI applications, which require data that are not only FAIR but also structured, annotated, and optimized for seamless integration into AI/ML workflows.
FLUID-AI identifies and addresses 3 major gaps within the EOSC ecosystem. First, we establish a collaborative Competence Centre (CC) to provide coordinated support, training, and resources, ensuring researchers and operators are equipped with the skills needed to leverage AI/ML tools effectively. Secondly, we promote unified data and models integration, implementing semantical and technical interoperability to enable effortless reuse and combination across platforms and scientific disciplines. Thirdly, we deliver accessible and intuitive platforms, reducing technical complexity so researchers can focus on scientific discover.
The project is organized in 3 different action pillars corresponding to the identified gaps. Together with 8 real-world use cases from representative Research Infrastructures would allow us to demonstrate the FLUID-AI impact, validating the project’s solutions, ensuring they are scalable, reproducible, and aligned with real-world research needs.
By promoting cross-disciplinary collaboration, standardization, and open science principles, FLUID-AI aims to transform the EOSC into a dynamic, AI-ready ecosystem. The project outcomes include the novel Data and Models Liquidity concept and framework, innovative tools and platforms, comprehensive guidelines, and a blueprint for trustworthy AI-ready repositories. All together will empower researchers to leverage the full potential of AI-driven scientific discovery.
AI Research Enhancement through Networked Agents (EOSC-ARENA)
The EOSC-ARENA (AI Research Enhancement through Networked Agents) will deliver a sovereign, generative and agentic Artificial Intelligence (AI) environment integrated with the European Open Science Cloud (EOSC). This AI environment will serve as a scientific assistant supporting the full research lifecycle, from literature review and hypothesis generation to analysis, reporting, and provenance capture. The project responds to pressing needs in the use of smart algorithms and AI/ML services in scientific research, fostering trust, transparency, and European technological sovereignty.
The project focuses on building an advanced, scalable, multi-agent system and a marketplace for Generative AI (GenAI) agents and services. It will provide federated training and inference, secure generation with augmented search and integrations based on the Model Context Protocol. The EOSC-ARENA system will be deployed on EU e-infrastructures and interoperable with EOSC EU nodes. Twelve real-life use cases from different scientific domains are selected to co-design, implement, and assess the effectiveness of the project solutions. At the same time, we will provide community engagement, skills development and guidance for responsible, human-centric AI that aligns with EU values and the Research Integrity Framework.
Main outcomes include an EOSC-ready platform release with agent execution and marketplace, open-source components, machine-actionable APIs and provenance mechanisms. Equally important will be policy guidance and training assets to accelerate trustworthy AI adoption. The project targets demonstrators integrated with EOSC services and contributes directly to the EOSC and its strategic research and innovation agenda by strengthening interoperability, FAIRness and sustainability of AI in European research.
Generative Artificial Intelligence for Earth System (GenAI4Earth)
Generative Artificial Intelligence (GenAI) is rapidly advancing, offering novel ways to exploit multi-disciplinary data and generate new knowledge for science. In Earth System Science (ESS), GenAI is emerging as a transformative technology, enabling a paradigm shift in understanding, predicting, and managing complex socio-environmental systems by cross-using diverse yet fragmented data sources (satellite and in-situ observations, models, experiments, texts).
GenAI4Earth will go beyond the state of the art by designing, deploying, and operating trustworthy, reusable GenAI services within the EOSC ecosystem, advancing discovery on Earth–climate–environment–life interactions in co-design with user communities and research infrastructures at national and European levels. Aligned with GenAI4EU and Apply AI initiatives, the project builds on FAIR data, models, and workflows, integrating them into EOSC (AI4EOSC, EOSC Nodes such as Data Terra and NFDI) to foster standards, best practices, and confidence in AI-enabled dataspaces and foundation models.
Connecting National Nodes to the EOSC federation (EOSC-CONNECT)
The EOSC-CONNECT project aims to expand, consolidate, and advance the EOSC Federation by focusing on nodes with a shared geographical scope – referred to as National Nodes.
The distinguishing feature of national EOSC Nodes is that they are supported/endorsed/acknowledged by the relevant national authority/ies, such as ministries responsible for EOSC strategy or national funding agencies (e.g. Research Councils). National Nodes are strategic to the EOSC Federation for several key reasons: alignment with national strategies; resource ownership; researcher engagement; existing infrastructure and expertise.
Secure Interactive Environments
for SensiTive data Analytics (EOSC SIESTA)
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.
Establishing A European Network of Trustworthy Digital Repositories (EOSC-FIDELIS)
The EU-funded FIDELIS project will harmonise the definition of trustworthy repositories and establish a European network of TDRs to foster and support the scientific environment envisioned by the EOSC and the community.
The European Open Science Cloud (EOSC) partnership is paving the way towards more accessible, reusable, and discoverable data, tools and services for researchers across Europe. Achieving this goal requires trustworthy digital repositories (TDRs) to preserve and maintain these resources over the long term.
The EOSC-FIDELIS network aims at developing, upgrading, and harmonising TDRs, facilitating collaboration with other repositories within the EOSC ecosystem. FIDELIS will collaborate with the EOSC EDEN project to jointly advance effective data preservation and curation in Europe.
ENGRAMMER
Engrammer is an innovative application that connects two worlds: personal memory and educational learning. Using AI inspired by neuroscience, you can register and recall your memories (text, image, audio) or learn historical content through games and interactive experiences.
Engrammer recuerda
- Safe storage and recall of personal memories.
- Natural AI interactions to help you relive memories and keep your mind active.
- Simple, accessible, and familiar interface.
- Reminders for important dates and events.
- Guaranteed privacy and control over personal data.
Engrammer aprende
- Experience-based learning, role-playing, and interactive challenges.
- Long-term memory consolidation using neuroscience techniques.
- Formative assessment with automatic feedback and AI-generated concept maps.
- Simple and accessible interface.
- Guaranteed privacy and control over personal data.
Artificial Intelligence for the European Open Science Cloud
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.
EUCAIM
Cancer Image Europe is pioneering a pan-European federated infrastructure for cancer images, fuelling AI innovations.
Our mission is to build a pan-European digital federated infrastructure of cancer-related images, which will be used for the development of AI tools toward Precision Medicine. We hope that this infrastructure will provide the means to develop AI tools that will be able to enhance the (cancer) diagnosis procedure, treatment and the identification of the need for predictive medicine benefiting patients across Europe.
Cancer Image Europe provides a robust, trustworthy platform for researchers, clinicians, and innovators to access diverse cancer images, enabling the benchmarking, testing, and piloting of AI-driven technologies. By connecting high-quality cancer image data and AI experts, Cancer Image Europe facilitates collaboration and accelerates the development of cutting-edge solutions for cancer diagnosis and treatment.
Greener Future Digital Research
Infrastructures.
GreenDIGIT brings together 4 major distributed Digital Infrastructures at different lifecycle stages, EGI, SLICES, SoBigData, EBRAINS,
To tackle the challenge of environmental impact reduction with the ambition to provide solutions that are reusable across the whole spectrum of digital services on the ESFRI landscape, and play a role model. GreenDIGIT will capture good practices and existing solutions and will develop new technologies and solutions for all aspects of the digital continuum: from service provisioning to monitoring, job scheduling, resources allocation, architecture, workload and Open Science practices, task execution, storage, and use of green energy. GreenDIGIT will deliver these solutions as building blocks, with a reference architecture and guidelines for RIs to lower their environmental footprint.
It will include the extension of a workload manager, Virtual Machine Manager, AI/ML training framework, and IoT/5G/network management solutions from 4 participating RIs with new brokering logic to optimize task execution toward low-energy use. User-side tools and Virtual Research Environments will also be expanded with energy usage reporting and reproducibility capabilities to motivate users to apply low-energy practices. The new solutions will be validated through reference scientific use cases from diverse disciplines and will be promoted to providers and users through an active dissemination and training programme, in order to prepare the next generation of Digital RIs with a low environmental footprint.
iMagine – Imaging data and services for aquatic science
iMagine provides a portfolio of free at the point of use image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis.
These services and materials enable better and more efficient processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant for healthy oceans, seas, coastal and inland waters.
By building on the computing platform of the European Open Science Cloud (EOSC) the project delivers a generic framework for AI model development, training, and deployment, which can be adopted by researchers for refining their AI-based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI-based applications in this field.
The iMagine compute layer consists of providers from the pan-European EGI federation infrastructure, collectively offering over 132,000 GPU-hours, 6,000,000 CPU-hours and 1500 TB-month for image hosting and processing. The iMagine AI framework offers neural networks, parallel post-processing of very large data, and analysis of massive online data streams in distributed environments. 13 RIs will share over 9 million images and 8 AI-powered applications through the framework. Having representatives so many RIs and IT experts, developing a portfolio of eye-catching image processing services together will also give rise to Best Practices. The synergies between aquatic use cases will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness, contributing to harmonisation across RIs and providing input for the iMagine Best Practice guidelines. The project results will be integrated into and will bring important contributions from RIs and e-infrastructures to EOSC and AI4EU.
Impetus4Change
The overarching objective of I4C is to improve the quality, accessibility and usability of near-term climate information
and services at local to regional scales to strengthen and support end-user adaptation planning and action. I4C will commit to Open Science through development of open access tools and exploitation of data/model outputs via relevant platforms thereby ensuring improved accessibility and usability of climate knowledge in the context of the EOSC.
