Data and AI are the fuel of scientific discoveries, and Research Infrastructures (RIs) are at the forefront of this process, generating massive and increasingly more complex datasets. However, the growing size, diversity, and velocity of research data and software demand large-scale infrastructures and technical expertise from those on the user side. RI-SCALE will address this challenge by delivering Data Exploitation Platforms (DEPs). These scalable environments will co-host scientific data with preconfigured AI frameworks and models on powerful compute resources and unlock full data and AI potential for scientific users, RI operators and industry. RI-SCALE will design and develop the DEP technology with four RIs: ENES, EISCAT, BBMRI and Euro-BioImaging. DEP instances will be deployed for environmental and life sciences, validating the technology through 8 scientific and 4 technical use cases. These will run on national e-infrastructures from the EGI Federation and (pre)exascale machines from EuroHPC. RI-SCALE will collaborate with Destination Earth, EUCAIM cancer images data space, Copernicus Data Space Ecosystem, EOSC and Gaia-X to ensure interoperability within the broader landscape. The project will also facilitate industry and university collaborations, provide training and consultancy events to increase the uptake of AI technologies by additional RIs and explore sustainable DEP operation models for RI communities.

Increasing demand for sophisticated clinical diagnostics makes current diagnostic capacities insufficient. A potential solution lies in semi-automatic systems speeding up the diagnosis process. Artificial intelligence (AI) and machine learning seem to be very promising approaches to the automation of diagnostic systems. However, most academic AI systems are opaque black boxes that cannot be easily understood, tested and certified. Also, academic AI solutions are often hard to reproduce, and their evaluation is insufficiently connected with clinical practice. This motivates MU and MMCI to team with two advanced partners (AP), MUG and TUB, and establish a BioMedAI infrastructure allowing close cooperation of computer science and clinical experts to develop explainable trustworthy AI solutions. Both AP possess rich experience with AI solutions for healthcare. Namely, processing large amounts of sensitive image and clinical data, interactive machine learning methods with a human-in-the-loop, and validating AI methods for healthcare. The main body of the BioMedAI project concentrates on training computer science researchers at MU and clinical experts at MMCI in the development of explainable AI methods based on high-quality medical data and validated in a clinical setting. Concretely, we propose organizing thematic workshops, virtual training with hands-on experience in developing explainable AI tools, and two summer schools. One will be oriented towards basic research in explainable AI methods for image and clinical data processing, and the other one towards the FAIR management of sensitive medical data. Furthermore, the BioMedAI project will also increase the visibility and presence of the explainable AI research in healthcare at MU and MMCI by training a PR manager responsible for presenting the research to various stakeholders, and by training the existing project management staff at MU and MMCI in writing grant applications for projects in EU and elsewhere.

The main objective of this project is to establish a Data Space for rare cancers (RC) that will make possible the re-use of existing multisource health data (cancer registry data, national registries, data from biobanks etc.) across European healthcare systems leveraging emerging interoperability technologies and AI approaches. The realized "Rare Cancer Data Ecosystem" is expected to improve the quality and the organization of RC patients care, and to increase knowledge on rare cancers advancing health research, so that all patients have equal access to high quality specialist care. The project approach will be experienced in the framework of the European reference network for rare adult solid cancers (EURACAN).

BBMRI-ERIC: the Biobanking and BioMolecular resources Research Infrastructure - European Research Infrastructure Consortium, aims to establish, operate and develop a Pan-European distributed research infrastructure in order to facilitate the access to biological resources as well as facilities and to support high quality biomolecular and biomedical research. The ADOPT BBMRI-ERIC proposal aims at boosting and accelerating implementation of BBMRI-ERIC and its services. Its main deliverables are designed to complete or launch the construction of key Common Services of the Research Infrastructure as required for ESFRI-projects "under implementation", reflecting the targets of the European Research Area (ERA). One of the challenges in the post-genomic era is the research on common complex diseases, such as cancer, diabetes and Alzheimer’s disease. Revealing these diseases will depend critically on the study of human biological samples and data from large numbers of patients and healthy individuals. The EU’s ageing population is will result in an increase in many of those diseases and consequently an increased healthcare expenditure for senior citizens. BBMRI-ERIC is a specific European asset having become a fundamental component in addressing the ongoing and future requirements particularly of Europe's health service frameworks, including competitiveness and innovativeness of health-related industries. Its implementation is essential for the understanding of the diversity of human diseases, biological samples and corresponding data, which are required for the development of any new drug or diagnostic assay and are, therefore, critical for the advancement in health research, ultimately leading to personalised medicine. BBMRI-ERIC will provide a gateway access to the collections of the European research community, expertise and services building on the outcome of ADOPT BBMRI-ERIC.

The 15-month coordination and support action “4.UNCAN.eu” will generate a strategic agenda to launch UNCAN.eu, a European initiative to UNderstand CANcer proposed by the Mission Board and the European Beating Cancer Plan. This research agenda will be built with the final aim of achieving a new breakthrough in cancer prevention and treatment that will contribute to saving European citizens’ lives and help ensuring an optimal quality of life to disease survivors. To reach a new level of understanding, UNCAN.eu will take advantage of recent advances in research data generation and data sciences. Reliable, high-quality cancer research data generated by experimental model analysis and collected from longitudinal follow-up of cancer patients will be shared and integrated at an unprecedented scale within a Federated Cancer Research data hub, in the context of the General Data Protection Regulation. This information will be used by relevant players in Europe and beyond to address urgent and essential scientific and medical challenges in cancer prevention, early diagnosis, treatment and survivorship, in males and females of various ages. These challenges, identified in close interaction with European patients and citizens, will be tackled through competitive, ambitious and innovative, cross-border and trans-disciplinary research programmes built in a problem-solving manner. The definition of challenges will integrate inequalities in cancer research across regions and member states in order to boost the research potential of less-developed regions in Europe. Players will be committed to open science principles, including FAIR (findable, accessible, interoperable, and reusable) guiding principles for scientific data collection, management and stewardship. The new understanding gained from the collection and analysis of this wealth of data will apply secondarily to other diseases.