Evaluation Criteria & How to Score High

Horizon Europe Standard IA Criteria (single-stage)

| Criterion | Weight | Threshold |

|-----------|--------|-----------|

| Excellence | 5 | 4/5 |

| Impact | 5 | 4/5 |

| Quality & Efficiency of Implementation | 5 | 3/5 |

Minimum overall score to be ranked for funding: ≥ 10/15 and each threshold met.

What Evaluators Will Look For

1. Excellence (max 5 pts, threshold 4)

* Soundness of the concept and credibility of the technological approach (federated infrastructure, FAIR-by-design, AI integration, self-driving labs)

* Clear, measurable objectives addressing all three project phases

* Progress beyond state-of-the-art (compare with NOMAD, Materials Project, NIST-CHiMaD, etc.)

* Quality of the interdisciplinary methodology (materials science, data science, SSH)

⮑ Tip: Provide a concise “State-of-the-art gap table” and cite > 80 up-to-date references.

2. Impact (max 5 pts, threshold 4)

* Contribution to expected outcomes listed in the topic text (access to FAIR data, virtual design acceleration, trust infrastructure, sustainability mechanism)

* Scale and significance of the benefits for EU industry & research; quantified KPIs (e.g. 30 % reduction in experimental iterations, €X cost saving, Y % faster TRL uplift)

* Credibility of the business case & exploitation strategy (revenue forecast, pricing, IP & data-sharing model)

* Alignment with EU policies (Advanced Materials Roadmap, Critical Raw Materials Act, Digital Europe data spaces, Open Science)

* Appropriate measures for dissemination, communication & citizen engagement

⮑ Tip: Include Letters of Intent from ministries, industry associations & EOSC Association.

3. Quality & Efficiency of Implementation (max 5 pts, threshold 3)

* Coherence and effectiveness of work plan, work packages, milestones, risk management

* Appropriateness of the consortium (complementarity, gender balance, multidisciplinarity, stakeholder coverage)

* Allocation of resources – balanced budget, justified large-scale items (HPC time, lab automation)

* Quality of management structures, decision-making, IPR & data governance

* Risk mitigation, especially for data security, ethics, supply-chain delays

⮑ Tip: Provide a RASCI matrix and a concise Gantt-chart; show contingency budget (~5 %).

Common Weaknesses to Avoid

* Vague definition of ‘federated infrastructure’ without concrete architecture

* Over-optimistic AI/self-driving lab milestones unsupported by TRL evidence

* Lack of long-term operations funding plan

* Inadequate industrial commitment (letters only, no budgeted tasks)

* Missing standardisation and regulatory strategy (SSbD, REACH, ISO)

Deliver a proposal that scores ≥ 4.5 in each main criterion to be competitive for the single high-budget grant foreseen.

Application Strategy – How to Win this IA

1. Build a Mission-Driven, Balanced Consortium

* Nominate national or regional materials-data hubs as core beneficiaries (fulfilling the ‘publicly-funded organisation mandated by a competent ministry’ expectation).

* Secure industrial champions in at least five sectors (e.g. batteries, aerospace, biomedical, construction, electronics) to co-lead the demonstrators and provide real industrial data.

* Onboard specialised SMEs (ontology development, AI/ML, lab-automation) to showcase SME uptake.

* Include an SSH partner to guide stakeholder analysis, trust-building mechanisms and inclusive governance.

2. Co-create a Convincing Vision & Roadmap

* Articulate a 10-year vision beyond the 4-year project: how the Materials Commons will become the default European marketplace for materials data & workflows.

* Map the three project phases to clear, timed KPIs (e.g. “By M18: federation layer in TRL 4; by M36: 5 interoperable data spaces at TRL 6; by M48: self-driving labs reach autonomy level 4 and reduce experimental cycles by 50 %”).

3. Nail the Use-Cases & Impact Narrative

* Select five high-value industrial pilots that (i) demonstrate cross-sector relevance, (ii) quantify acceleration (time-to-material, CO₂, cost), and (iii) generate reusable, FAIR datasets.

* Provide a first business case and exploitation plan per use-case using credible market data (size, adoption barriers, revenue streams, pricing of data services, subscription models).

4. Address Sustainability From Day 1

* Design a mixed funding model (member fees, data-as-a-service, pay-per-compute, national co-funding, venture capital for spin-offs).

* Commit ministries/funders in an Advisory Board Letter of Support indicating future co-investment in Ops phase.

5. Technical Excellence & Openness

* Reference existing open-source middleware (e.g. NOMAD, OPTIMADE, Materials-Cloud) and explain integration strategy versus new development.

* Provide an open API specification draft and a clear standardisation workflow coordinated with CEN-CLC and ISO TC-229.

* Plan for EOSC onboarding: align metadata profile, persistent identifiers, AAI and catalogue integration.

6. Proposal Writing Tips

* Follow the Impact-Pathway logic: Expected Outcome → Specific Objective → KPI → Activity / Deliverable.

* Use diagrams: architecture, governance, data/AI workflow, self-driving lab hierarchy.

* Reserve 10–12 pages for the business case & exploitation strategy (mandatory for Destination 2 IAs).

* Explicitly answer “why EU funding is needed” (critical mass, cross-border standardisation, public good).

* Cross-check compliance with all HEU mandatory sections (data management, gender, ethics, security).

7. Time-line & Internal Workflow

1. Expression of Interest & NDA signing – by Nov 2024

2. Consortium agreement outline – Jan 2025

3. Draft Part B v0 – April 2025

4. Final budget & commitments – July 2025

5. Red-team review – August 2025

6. eSubmission – at least 24 h before 23 Sept deadline