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Get Free AccessThis present deliverable, D3.2 “Interdisciplinary Collaborations between EATRIS and other Research Infrastructures” builds on EATRIS-CONNECT D3.1 which describes the Life Science domain through a translational lens connecting researchers, patients, the data lifecycle and healthcare interventions. It presents a blueprint for connecting European research infrastructures (RIs) across domains to accelerate the development of personalised medicine (PM). The main outcomes of this deliverable are:1. A conceptual model of an Interdisciplinary Research Graph linking data, services, and expertise across domains for the advancement of PM.2. A proof-of-concept prototype demonstrating how a graph can be structured and queried.3. Two use cases (pancreatic cancer; environmental exposure and childhood cancers) illustrating the value of such a graph in identifying partners, mapping services, and exposing gaps. Key findings: •Interconnecting diverse infrastructures reveals new opportunities for research on complex health challenges where biology, environment, and social determinants intersect.•The graph can serve as a navigation tool for researchers and policymakers to discover services and build collaborations, while also supporting funding proposals with a structured interdisciplinary narrative.•Important gaps remain, including limited coverage of artificial intelligence (AI)/machine learning (ML) services and the need for richer, validated catalogues of RI offerings.•The effort highlights the value of complementary tasks, activities and services across research infrastructures to contribute to societal health challenges. This present deliverable does not aim to provide a fully operational system but to lay the foundation for one. Sustained development, governance, and community engagement will be required to transform the prototype into a long-term, operational knowledge base for European PM.
Florence Biétrix, Nektarios Liaskos, Emanuela Oldoni (2025). Inter-disciplinary collaborations between EATRIS and other RIs. , DOI: https://doi.org/10.5281/zenodo.20271220.
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Type
Article
Year
2025
Authors
3
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.5281/zenodo.20271220
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