| Topic | HORIZON-RAISE-2027-01-04 |
|---|---|
| Status | Open |
| Call | HORIZON-RAISE-2027-01 — AI in Science |
| Type of action | Coordination and Support Actions · lump sum |
| Opening | 13 October 2026 |
| Deadline | 2 February 2027 |
| Budget | around EUR 1 million per project · 1 project expected · EUR 1 million in total |
| Part B page limit | 28 pages (standard application form; check your call) |
| Template | Work Programme 2026–2027 application form |
Expected outcomes
The topic does not say whether all outcomes must be addressed; treat them all as relevant. The topic says: “Project are expected to contribute to the following outcomes:”
- EO1 a sustainable operational model and reference blueprint for a Data Lab for Science linking with the European Open Science Cloud (EOSC) and the Resource for AI Science in Europe (RAISE), enabling researchers to accelerate the uptake of AI in Science.
- EO2 easier access for researchers to large amounts of AI-ready research data, and to associated high-quality data services to seamlessly develop and train AI models for science.
- EO3 better alignment, interoperability and scaling across countries and disciplines of AI workflows and related services such as data pooling, anonymisation, curation, or synthetic data generation.
What evaluators will look for in this topic
Expected outcomes
- The topic does not say whether all outcomes must be addressed; treat them all as relevant.
- For each outcome (EO1 to EO3), name who will use or adopt your results (a specific group, not “industry” or “society”), how they get them, and the indicator that shows the change, with baseline, target and date.
- Use the topic's own words for each outcome, so the evaluator can tick it off.
What the scope asks for
Evaluators check the proposal against these sentences of the scope. Answer each one visibly, in the call's own words.
- “Proposals should include all of the following activities:.”
- “The action should build on relevant results of Horizon Europe projects that contribute to the development of EOSC and RAISE, as well as EU-funded projects developing AI solutions for science such as foundation models in science, data commons, or AI research assistants.”
- “The action should closely coordinate with relevant Horizon Europe projects such as those funded under HORIZON-INFRA-2025-01-EOSC-03, HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-62 and HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61, as well as with relevant projects under EuroHPC and the AI Factories.”
Conditions that shape the proposal
- Part B, sections 1–3: 28 pages (standard application form). Text beyond the limit is not read; tables and figures count.
- Lump sum: the budget is fixed per work package and paid when each work package is completed. Every work package needs activities and deliverables an evaluator can verify, and the effort must match the money.
- Coordination and support action: no research is funded. Evaluators look for coordination, networking, dissemination or policy support with a clear audience.
Impact pathway and traps
- The scope expects pilots or demonstration. Name the sites, the partners who run them and what is measured there.
- Open science or data practices are named. Make the data management plan concrete: which data, which repository, when.
- Keep outputs, outcomes and impacts apart: what you deliver, what changes when others use it, and the long-term effect.
- Link your outcomes to the destination's expected impacts in the Work Programme. Destination impacts are long-term effects, not project deliverables.
- Name the barriers outside the project (regulation, markets, user behaviour) that could block uptake, and what you will do if they do.
- Section 2.3 (the summary canvas) is optional on this call; the old split into scientific, economic and societal impact is no longer required.
The scope, as published
Data Labs are introduced in the Data Union Strategy as a connector between AI factories and the Common European Data Spaces, providing specialised services to enable turning data into useable resources for AI training and the development of AI applications such as data pooling, curation, pseudonymisation, and synthetic data generation. A significant number of tools and services within the scope of the Data Lab concept have been developed by research infrastructures and scientific service providers, notably organisations that operate or contribute resources to EOSC, the Common European Data Space for research and innovation. The development of Data Labs addressing the needs of scientists is a key element in the AI in Science strategy, as a collaboration between RAISE and EOSC. This action aims to anchor the implementation of the Data Lab model with the development of EOSC and RAISE. The Data Lab for science will be a key deliverable and a key element of the data work strand of RAISE. Proposals should include all of the following activities: Identify, consolidate and orchestrate existing data, services and other relevant results of Horizon Europe EOSC-related projects, as well as other community-endorsed promising solutions in view of setting up and implementing a Data Lab for Science, including a mapping of such results and solutions to facilitate their navigation, access and use. The results and solutions may encompass relevant data services, but also related policies, guidance and training approaches. Identify and collect necessary elements for seamlessly integrating a Data Lab for Science into the existing European data and high-performance computing infrastructures enabling cohesive workflows. Identify technical, operational, legal, organisational or any other type of bottlenecks or gaps preventing the seamless sharing, reuse or pooling of data for AI development and applications in science. Develop a blueprint for a Data Lab for Science addressing such bottlenecks and support its implementation through the demonstration of use cases, in coordination with the EOSC Federation and relevant developments in the EuroHPC Federation. Develop governance and financing models that ensure a sustainable pathway for a Data Lab for Science beyond the project’s duration. Identify researchers’ needs and gaps on data services in different scientific disciplines that are not addressed by existing solutions and propose recommendations for future policy and funding actions. Connect and encourage collaboration among projects offering data and data-related services for AI in Science. The action should build on relevant results of Horizon Europe projects that contribute to the development of EOSC and RAISE, as well as EU-funded projects developing AI solutions for science such as foundation models in science, data commons, or AI research assistants. The action should closely coordinate with relevant Horizon Europe projects such as those funded under HORIZON-INFRA-2025-01-EOSC-03, HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-62 and HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61, as well as with relevant projects under EuroHPC and the AI Factories.
Other topics in HORIZON-RAISE-2027-01
- HORIZON-RAISE-2027-01-02 — Automated Scientific Discovery – Food (RAISE pilot)
Source and attribution
Topic text from the official topic page on the EU Funding & Tenders Portal, retrieved 19 October 2026. © European Union, reused under Commission Decision 2011/833/EU (CC BY 4.0). Changes: the expected outcomes and impacts are split into numbered statements, long passages are shortened, and the evaluator checklist is GrantWise's own commentary. Your call and the Work Programme always win; check them before you submit. GrantWise is independent and not affiliated with, endorsed by, or acting on behalf of the European Commission.