Scientific discoveries increasingly rely on data and AI. Research infrastructures (RIs) generate huge amounts of complex data, but managing, analysing and using this information is becoming a major challenge. Scientists often lack the large-scale tools and expertise needed to process data quickly and effectively. With this in mind, the EU-funded RI-SCALE project is tackling this issue by creating scalable Data Exploitation Platforms. These platforms will combine scientific data with preconfigured AI tools and powerful computing resources, unlocking their full potential.
Mission
RI-SCALE aims to empower Research Infrastructures (RIs) to fully exploit the potential of their massive data holdings by providing scalable compute platforms and AI-based tools. The mission centers on enabling advanced, trustworthy, and energy-efficient data exploitation environments—called Data Exploitation Platforms (DEPs)—to support transformative science, innovation, and collaboration. Working with four RIs (ENES, EISCAT, BBMRI and Euro-BioImaging), RI-SCALE will test the technology in environmental and life sciences. It will also foster collaborations to help more RIs adopt these innovations.
Objective
To equip data-intensive RIs with scalable, AI-ready computing infrastructures—Data Exploitation Platforms (DEPs)—that enhance reuse and valorisation of their research data.
This will be achieved through:
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Co-design and validation of DEP technology
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Integration of AI tools and models
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Demonstrating AI use cases in key science domains
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Strengthening collaboration with industry and academia
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Establishing a long-term, sustainable model for DEP operations
Specific Objectives & Outcomes
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DEP Technology Prototype
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Co-design and build a prototype DEP across a distributed expert network.
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Support secure data replication, lifecycle management, and energy efficiency.
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AI-Enhanced Data Exploitation
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Integrate AI frameworks into DEPs to train, validate, and serve AI models.
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Support reuse of foundation and community models.
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Use Case Demonstration
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Deploy DEPs in 4 pilot RIs and validate through real-world use cases in environmental and health sciences.
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Connect with major Data Spaces (e.g., Copernicus, EUCAIM).
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Industry & Academia Collaboration
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Develop an engagement framework with SMEs, startups, and university spin-offs.
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Use credit-based systems to support model training and validation.
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Capacity Building & Training
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Run Competence Centre workshops and practical training/webinars.
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Build AI skills within RI communities and promote knowledge-sharing.
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Sustainable Operations
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Explore and initiate long-term partnerships for DEP sustainability.
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Foster collaboration between RIs, e-infrastructures, and commercial entities.
Ambition & Progress Beyond State of the Art
RI-SCALE’s ambition lies in:
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Transforming RIs into AI-ready data factories, bridging current gaps in compute capabilities.
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Delivering PB-scale model training and reuse with trust-based identity and access management.
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Enabling cross-domain reuse of tools and models by standardising DEP concepts across science domains.
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Supporting future transnational virtual access to DEP resources through usage tracking.
