Both ESM2025 and XAIDA are large climate research consortia where this education foundation's role is almost certainly science communication and pedagogical translation of results.
FOUNDATION POUR L'EDUCATION A LA SCIENCE DANS LE SILLAGE DE LA MAIN A LA PATE
French science education foundation translating frontier climate modelling and AI-driven research into accessible knowledge for schools and the public.
Their core work
This Paris-based foundation is the institutional home of "La main à la pâte," France's flagship inquiry-based science education program originally launched by Nobel Prize physicist Georges Charpak. Their core work is translating complex scientific research into accessible educational materials and pedagogical frameworks for schools, teachers, and the broader public. In H2020 climate research consortia, they serve as the science communication and education outreach partner — bridging frontier climate science (earth system models, AI-driven extreme event attribution) and non-specialist audiences. They bring credibility and a national education network to research projects that need to demonstrate societal impact and public engagement.
What they specialise in
Both projects sit squarely in the P3-CLIMATE pillar, covering earth system modelling, carbon cycles, and extreme event attribution — all topics requiring expert educational translation.
XAIDA (2021–2026) introduces deep learning, causal networks, and AI-based extreme event detection — suggesting growing involvement in explaining AI methods to non-technical audiences.
How they've shifted over time
Both projects started in 2021, so the temporal arc is short, but a thematic shift is visible in the keyword sets. Their initial project (ESM2025) centres on foundational climate modelling — carbon and methane cycles, model realism, earth system interactions — suggesting involvement in communicating the basic science of how climate models work. Their second project (XAIDA) pivots toward AI-driven attribution of extreme weather events, introducing deep learning, causal networks, and impact projections. The trend points toward a growing role in explaining AI-assisted climate science to educational and general audiences, a space that is rapidly expanding in demand.
They are moving from communicating foundational climate modelling toward explaining AI and machine learning methods applied to climate attribution — a niche with very few qualified education partners in Europe.
How they like to work
This foundation has never coordinated an H2020 project, always joining as a partner — consistent with a specialist outreach role within large research-led consortia. Their two projects together involved 32 unique partners across 11 countries, indicating comfort working in sizeable, multinational consortia. For a prospective consortium builder, they represent a reliable, low-friction education and dissemination partner who adds societal impact narrative without competing for scientific leadership.
They have collaborated with 32 unique partners across 11 countries, all within just two projects — suggesting they joined well-connected, large-scale RIA consortia rather than building a personal network organically. Their geographic reach is European, with a likely concentration in France and major climate research hubs.
What sets them apart
Few European organisations combine the institutional credibility of a major national science education program with direct participation in frontier climate research consortia. Their connection to "La main à la pâte" gives them access to tens of thousands of French teachers and a proven track record of making hard science accessible. For any climate or environment project needing a credible, France-rooted education partner to satisfy societal impact requirements, this foundation is a rare and specific fit.
Highlights from their portfolio
- ESM2025Largest grant received (EUR 296,376), focused on next-generation earth system models — a high-profile climate modelling initiative where this foundation's education role carries significant public engagement weight.
- XAIDAIntroduces AI and deep learning into the foundation's portfolio for the first time, signalling an expansion from traditional science education into explaining data-driven climate science methods to non-specialist audiences.