Expected Outcome:

• Tools to perform automated data and metadata curation / inclusion / minimisation by data providers, foster data quality assurance and enable compliance checks and risk/security management developed, tested and deployed in the 1+MG data infrastructure in alignment with agreed 1+MG requirements and related standards and procedures, as well as with the legislative and technical framework of the European Health Data Space and European Digital Identity Framework.
• Tools, APIs and interfaces developed, tested and deployed in the GDI, covering well documented needs of users from research, healthcare and public health policy, in alignment with the European Health Data Space.
• Citizens’ portal, allowing at least management of GDPR rights of citizens and citizens’ engagement, to be established and operational at month 12 at the latest.

Objective:

This action aims to enhance the European genomic data infrastructure by supporting the deployment of advanced tools for data curation and use and its alignment with the European Health Data Space (EHDS). Data quality and volume are key success factors for health data infrastructures supported under the Digital Europe Programme, such as the one developed by the Genomic Data Infrastructure (GDI) project implementing the 1+ Million Genomes (1+MG) initiative of the Member States. The availability of thoroughly curated genomic data and related clinical and phenotypic information is a prerequisite for accelerating the move to the next level of multi-modal data modelling and deployment as well as health sector innovation in Europe. 1+MG and the projects implementing the initiative have worked and agreed on common data standards and ontologies, data quality criteria and thresholds, data inclusion policies and the necessary standard operational procedures. On that basis, data holders will need to quality-check and curate the datasets that they will be making available to 1+MG and EHDS (HealthData@EU) to ensure their interoperability and high value for users. Wide accessibility of the data and user-friendliness of the tools and services facilitating the access to data are critical success factors of the 1+MG data infrastructure. While such functionalities for research purposes have been already largely covered in the deployment project GDI, application in other use scenarios, in particular healthcare and public health policy, requires additional tools, application programming interfaces (APIs) and interfaces to address the corresponding specific user needs and requirements. The citizen perspective must be also factored in to ensure, through appropriate IT tools, full compliance with the General Data Protection Regulation (GDPR) rules regarding citizens’ rights on personal data protection. This action relates to the potential creation of a European Digital Infrastructure Consortium for genomic data (Genome EDIC) and supports the activities related to operating the 1+MG data infrastructure established with the support of Digital Europe under Work Programme 2021-2022 (GDI project), including its alignment with the requirements, technical specifications, and processes established by the EHDS Regulation to ensure a smooth functioning within the HealthData@EU infrastructure.

Scope:

Data tools for the 1+MG data infrastructure should be based on common standards and as automated as possible, and should enable data quality check at source, benchmarking, annotation and enhancement on the data provider end and by the operator of the data infrastructure, as appropriate. This covers the whole process of data inclusion, integration and access provision, as well as compliance assessment, risk management and data security assurance on the side of data infrastructure. All steps and functionalities should be designed to support the needs and requirements of three main use scenarios, i.e. research (largely already covered by the GDI project), and clinical care and public health policymaking. They should follow and implement the standards and procedures agreed within the 1+ Million Genomes initiative (1+MG Framework) and be compliant with the EHDS Regulation. For example, data curation tools should facilitate the description of datasets using a metadata standard compatible with the one required for the EU Dataset Catalogue of the EHDS (Health DCAT-AP), including its data quality and utility label as defined in the project QUANTUM. Metadata should also cover information about legal conditions and enablers for sharing the respective dataset. Moreover, the action should leverage best practices and strategies for linking clinical and genomic data at individual level, within the framework of EHDS where appropriate, to maximise access to data while preserving the security and privacy of data subjects (e.g. sampling, anonymisation and pseudonymisation techniques, data gap filling). Data minimisation tools should support compliance with the respective GDPR rules. Moreover, this action covers piloting and the deployment of tools, APIs and interfaces to provide high quality data services to the users of the 1+MG data infrastructure for healthcare and public health policy purposes as well as addressing any remaining user needs in research not yet covered by other projects. For example, APIs and interfaces for data discovery, and federated analysis and modelling in a secure processing environment will enable users to find, access and integrate the data at the required level of data protection safeguards to serve their projects, clinical questions or policy development. Adequate data de-identification/synthetisation methods and support to multi-modal data discovery and analysis across the data infrastructures (e.g. linking up with the Cancer Image Europe and HealthData@EU infrastructures) should also be considered and implemented. The tools delivered and deployed should be user-friendly and clearly support high uptake of the data infrastructures and their services. Furthermore, the action is expected to establish a citizen portal for 1+MG enabling citizens to exercise their GDPR rights, such as obtaining information about their data inclusion / processing and their legal basis, manage the consent, requesting data access, rectification or erasure. All solutions deployed by the project should be compatible with the Simpl middleware platform, where appropriate, and ensure interoperability with the HealthData@EU infrastructure. Tools related to authentication should be eIDAS-compliant. While fully respecting the prerogative of participants within the 1+MG data infrastructure to determine who can access what data and under which conditions, suitable links to the AI Factories should be envisaged. The project should include a description of data access and usage arrangements.

AI Based Application Success Predictor

1️⃣ Strong, Mission-Aligned Impact (Most Important Across EC Calls)

The EC is impact-driven: proposals must show how the project will:

Solve a major European or global societal challenge

Deliver measurable, lasting benefits for EU citizens

Produce outputs that can be used by policymakers, industry, or society

Align with Horizon Europe missions, priorities, and strategic agendas

Predictor: Clear, quantifiable, EU-level impact → strongest scoring factor.

2️⃣ Clear, Ambitious, but Achievable Objectives

Successful proposals show:

2–4 well-defined objectives linked to the Work Programme call text

Clearly articulated research questions or innovation goals

Logical, realistic expected outcomes and deliverables

Feasible scientific and technical approaches

Predictor: Balanced ambition + feasibility.

3️⃣ Excellent, Cutting-Edge Science or Innovation

For RIA/IA/CSA or ERC-level grants, reviewers expect:

High novelty and innovation

Strong grounding in current state-of-the-art

Clear advancement beyond existing approaches

Solid theoretical or experimental foundations

Robust methodological design

Predictor: Scientific excellence is essential for competitive scoring.

4️⃣ Strong Consortium with Complementary Expertise

EC proposals are consortium-driven (except ERC/EIC Accelerator).

High-scoring consortia:

Cover all needed competencies (science, industry, policy, ethics, dissemination)

Include SMEs, industry partners, NGOs, and public bodies when relevant

Are geographically diverse across EU Member States and Associated Countries

Demonstrate strong leadership and communication structures

Predictor: Well-constructed consortium with clear roles.

5️⃣ Clear Pathway From Outputs → Outcomes → Impact

Evaluators look for a credible trajectory showing:

How research leads to specific outputs (data, tools, prototypes)

How outputs lead to uptake or use

How use produces societal, economic, scientific, or policy impact

Strong Key Performance Indicators (KPIs) and impact metrics

Predictor: Clearly mapped impact pathway.

6️⃣ Strong Implementation Plan (Work Packages, Deliverables, Gantt Chart)

Winning proposals have:

Well-designed Work Packages (WPs) with clear scope and responsibilities

Interdependencies identified and risk-mitigation strategies

Detailed milestones and deliverables

Feasible budget aligned with tasks

Strong project management plan

Predictor: High implementation quality boosts the “Excellence” and “Implementation” scores.

7️⃣ Policy Relevance and Contribution to EU Strategies

Especially critical for health, climate, digital, and social calls.

Proposals score higher when they link to:

EU Cancer Mission

EU Green Deal

Digital Europe strategy

EU Biodiversity Strategy

EU Health Union & One Health

Open Science & FAIR data mandates

Predictor: Clear alignment with EU policies.

8️⃣ Strong Stakeholder & Citizen Engagement (Especially in Social & Health Missions)

EC values inclusivity:

Patient groups

Civil society organizations

Public sector bodies

Regulatory agencies

Citizen science components

Stakeholder letters of intent or commitment strengthen credibility.

Predictor: Engagement adds impact and relevance.

9️⃣ Robust Data Management, Open Science, and Ethics

Mandatory components include:

FAIR Data Management Plan

Open access publications

Ethics self-assessment

GDPR compliance

Data security, governance, and ethical approvals

Animal-use reduction and justification (if applicable)

Predictor: Clear compliance with ethical and data obligations.

10️⃣ Well-Justified Budget and Resource Allocation

Budget must be:

Proportional to tasks

Transparent and reasonable

Efficiently distributed among partners

Free from padding or unjustified costs

Predictor: Realistic budgets improve Implementation scores.

🚫 COMMON PITFALLS THAT LEAD TO EC GRANT REJECTION