Machine-learning production and independent validation of a global land-cover map at 10 m resolution
Resources
Explore resources about AI-enabled scientific research in Europe
Autoplex
Automatic tool to train and validate machine learning interatomic potentials.
Transkribus
Сooperative AI platform for handwritten text recognition and layout analysis of historical documents
MLCast
Open-source community infrastructure for machine-learning weather nowcasting
SciMuse
AI-assisted generation and prioritisation of scientific research ideas
RAISE Info Desk
Thursday 15th October from 11.00 – 17.30 | Friday 16th October from 09.00 – 12.00 | OAK Room/Cypress Suite, UCD University Club
RAISE Community Challenge Lab
AIS26
Hands-on Session | 15 October 2026 | 09:20-10:30 IST | Cedar Suite, UCD University Club
In-person and online | 15-16 October | AIS26 and via RAISE Forum
AI in science Good Practices
SCIANCE is identifying and analysing good practices in AI-enabled science. The aim is to identify transferable lessons that can inform researchers, research organisations, infrastructures and the wider European AI-in-Science community.
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.
Frugal AI for Science
SCIANCE is gathering evidence on scientific challenges, AI capabilities, infrastructures and emerging practices in “Frugal AI for Science”, supporting the identification of strategic priorities for RAISE.
AI for Open Science & Trustworthy AI
SCIANCE is gathering evidence on scientific challenges, AI capabilities, infrastructures and emerging practices in “AI for Open Science & Trustworthy AI”, supporting the identification of strategic priorities for RAISE.
AI for Research & Experimental Design
SCIANCE is gathering evidence on scientific challenges, AI capabilities, infrastructures and emerging practices in “AI for Research & Experimental Design”, supporting the identification of strategic priorities for RAISE.