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AI4Access: AI-Driven User Access Services for Euro-BioImaging ERIC

Project description

AI-driven tool for imaging methods and services

Euro-BioImaging ERIC connects 39 Nodes and 295 facilities, providing open access to more than 120 imaging technologies. However, current catalogue-based access can be difficult to navigate, particularly for researchers outside the imaging field, due to complex terminology and the need for expert guidance. The EU-funded AI4Access project will develop a natural-language AI-powered tool that transforms scientific challenges into evidence-based recommendations for imaging methods, facilities, analysis pipelines, and data workflows. Using a hybrid semantic data structures, and user-centred design, the tool will improve accessibility, reproducibility, and efficiency in research. By embedding trustworthy AI principles and ensuring interoperability with European research infrastructures, AI4Access will provide a replicable model for AI-enabled research across Europe.

Objective

Euro-BioImaging ERIC connects 39 Nodes and 295 facilities, providing open access to 120+ imaging technologies. For many researchers, especially outside imaging, today’s catalogue-based access is difficult: it is jargon-heavy, assumes prior expertise, slows discovery, and leaves users lost in the extensive catalogue and costly instruments underused without heavy consultation.

AI4Access introduces the Research Navigator: a natural-language entry point that turns scientific problems into transparent, evidence-based recommendations for imaging methods, facilities, analysis pipelines, and data deposition. It relies on a hybrid knowledge base, ontology/PID-linked, structured records enriched with GenAI-augmented literature and past project evidence, and adapts advice to user needs. This shifts Euro-BioImaging from static lists to a problem-to-solution access framework.

The workplan follows a user-centred journey: WP1 co-designs the Navigator experience; WP2 encodes services semantically; WP3 extends to image-data workflows; WP4 delivers the generative-AI reasoning engine; WP5 proves value in pilots and training; WP6 embeds trust (provenance, explainability, GDPR/EU AI Act, energy-aware operation); WP7 drives uptake, governance templates, update workflows, and a white-label toolkit for reuse by other ERICs and RIs; WP8 secures integration and management.

Aligned with EOSC and bridging INFRA-SERV catalogues (ISIDORe, canSERV, AgroSERV), AI4Access lowers entry barriers, shortens the path from idea to experiment, improves reproducibility, and increases utilisation across Euro-BioImaging. Researchers and SMEs gain faster, clearer routes to services; Nodes and facilities gain visibility and sharper demand; and the RI ecosystem gains a replicable cross-RI access bridge and a blueprint for trustworthy, sustainable AI in Research Infrastructures. The project directly responds to the EU’s request to make access more effective and improve interoperability across the RI landscape.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

(opens in new window) HORIZON-INFRA-2025-01

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Coordinator

EURO-BIOIMAGING ERIC
Net EU contribution
€ 1 644 500,00
Address
PL 123
20521 Turku
Finland
Region
Manner-Suomi Etelä-Suomi Varsinais-Suomi
Activity type
Research Organisations
Links
Total cost
€ 2 055 625,00

Participants (3)