An initiative of the European Commission

AI in Science​ Infrastructure Landscape​

SCIANCE is gathering evidence on scientific challenges, AI capabilities, infrastructures and emerging practices in “AI in Science​ Infrastructure Landscape​”​, supporting the identification of strategic priorities for RAISE.

Context and scope​

AI-enabled science depends on a diverse ecosystem of computing resources, data infrastructures, research facilities, software platforms, innovation and skills support, regulatory and trust frameworks and scientific communities. SCIANCE mapped the European AI-in-Science landscape to identify the actors, infrastructures and capabilities supporting AI adoption across research domains, while highlighting emerging trends and ecosystem gaps.

State-of-the-Art Insights​

AI Technologies and Innovations

  • High-performance computing (HPC) and AI Factories
  • Scientific data ecosystems and Data Spaces
  • OpenAIRE Graph and research knowledge infrastructures
  • Federated and distributed computing
  • Digital twins and simulation platforms
  • AI-ready research infrastructures
  • Interoperable workflow and provenance systems

Structural barriers​

  • Fragmented infrastructure landscape
  • Uneven access to compute and AI expertise​
  • Interoperability and metadata challenges​
  • Dependence on project-based funding​
  • Geographic concentration of capabilities​

Relevance for scientific research and workflows​

Scientific progress increasingly depends on access to data, computing power, AI expertise and interoperable services. The SCIANCE landscape analysis provides the ecosystem perspective needed to connect scientific challenges, AI technologies and infrastructure investments, supporting discussions on future infrastructure needs, coordination mechanisms and strategic priorities for RAISE.

European ecosystem​

SCIANCE identified more than 1,100 European AI infrastructures and initiatives support AI adoption in scientific research, revealing a strong ecosystem centered on AI development and deployment, software and data services, and AI research. Key enablers include EOSC, EGI, OpenAIRE, EuroHPC, AI Factories, Data Spaces and a broad network of AI research infrastructures and institutions, European Digital Innovation Hubs and AI communities. The European AI in science landscape is diverse and expanding but remains distributed across countries and siloed between scientific domains and application areas.

Emerging trends​

  • AI Factories and Data Labs
  • Federated AI and data ecosystems
  • Machine-actionable science
  • Digital-twin infrastructures
  • AI observatories and ecosystem intelligence platforms
  • Open and sovereign AI infrastructures
  • Agentic AI orchestrated workflows and infrastructure

Outlook

Europe has many of the building blocks required for AI-enabled science. The next challenge is to improve coordination, discoverability and interoperability across infrastructures, communities and services. RAISE aims to help connect these capabilities into a more coherent and accessible European AI-in-Science ecosystem.

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