ERC Data Management Plans: Requirements

The European Research Council (ERC) funds frontier research through Horizon Europe, the European Union’s research and innovation framework programme for 2021-2027. Because ERC grants are Horizon Europe grants, ERC-funded projects follow the same Data Management Plan (DMP) rules that apply across Horizon Europe — but a few points specific to how ERC panels evaluate proposals and how ERC grant agreements are administered are worth spelling out on their own. This guide covers the ERC/Horizon Europe DMP mandate, the one-page research data management (RDM) statement required at proposal stage, the month-6 deliverable timeline, and practical guidance for preparing a compliant plan.

For the general Horizon Europe FAIR Data Management Plan template and section-by-section structure, see CASRAI’s Horizon Europe FAIR Data Management Plan guide — this page focuses on what is distinct to ERC-funded projects specifically: the no-opt-out rule, the proposal-stage statement, and ERC’s own guidance documents.

Is a Data Management Plan mandatory for ERC grants?

Yes, and without exception. Every ERC grant that generates, collects, or reuses research data must produce a DMP. This is a direct consequence of Horizon Europe’s Open Science requirements, which apply to all Horizon Europe actions, including ERC frontier-research grants (Starting, Consolidator, Advanced, and Synergy Grants).

This is a real change from the prior framework programme. Under Horizon 2020, DMPs were tied to the Open Research Data Pilot, and it was possible for a project to opt out of participating — and therefore out of producing a DMP — entirely. As the ERC’s own Work Programme guidance makes clear, that opt-out no longer exists under Horizon Europe: since 2021, ERC grantees cannot opt out of submitting a research data management plan. A DMP is a mandatory deliverable for any ERC project with a research-data component, full stop.

What ERC grantees retain is not an opt-out from writing a plan, but the ability to document legitimate, specific exceptions within the plan — for example, withholding a defined dataset from open access on intellectual-property, personal-data/privacy, or security grounds, provided the DMP explains and justifies the restriction. That distinction matters: “we have sensitive data” is a reason to write a more careful DMP, not a reason to skip writing one.

The one-page RDM statement at proposal stage

Before any DMP deliverable is due, ERC applicants encounter a lighter-weight requirement embedded in the proposal itself. The Horizon Europe standard application form — used across Horizon Europe funding, including ERC calls — asks applicants who expect to generate or reuse research data or other research outputs to describe, in a maximum of one page, how those outputs will be managed. This description sits within the methodology section of the proposal, under the Excellence evaluation criterion, alongside the rest of the scientific approach.

In practice, this means the one-page RDM statement is evaluated as part of the scientific quality of the proposal, not as a separate compliance checkbox. Reviewers reading the Excellence section are looking for a credible, proportionate sketch of what data the project will produce, in what format, and roughly how it will be handled — not a fully worked-out FAIR data plan. That level of detail comes later, in the month-6 deliverable.

Practical tips for the one-page statement:

Name the types of data or outputs the project expects to generate (e.g., experimental datasets, code, models, biological samples with associated metadata) rather than describing data management in the abstract.
State, at a high level, where data will be stored during the project and where it is expected to be deposited for reuse (an institutional repository, a discipline-specific repository, or a generalist repository such as Zenodo).
Flag, briefly, any anticipated restrictions (personal data, third-party materials, security-sensitive outputs) so reviewers are not surprised later — you do not need to resolve these in one page, only acknowledge them.
Keep it proportionate to project scale: a theoretical or purely computational project with minimal data output should say so plainly rather than padding the section.

Timeline: proposal, grant signature, and the month-6 deliverable

The ERC/Horizon Europe DMP timeline has two distinct checkpoints:

At proposal submission: the one-page RDM statement described above, submitted as part of the application and evaluated under Excellence.
By month 6 of the project: a full, detailed DMP submitted as a formal deliverable, addressing the FAIR principles (Findable, Accessible, Interoperable, Reusable) in depth — repository choice, metadata standards, licensing, access conditions, storage and preservation arrangements, and responsibilities for keeping the plan current.

The month-6 deadline is counted from the start of the project (i.e., grant signature and project commencement), not from the call deadline or evaluation outcome. Because ERC grants can take many months to move from submission to signature, the practical lead time to prepare the full DMP is often shorter than researchers expect once the clock actually starts.

Horizon Europe guidance also expects the DMP to be a living document: grantees are expected to revise and update it as the project evolves, typically with further updates around the midpoint and end of the project, reflecting any changes in what data is produced, how it is shared, or what exceptions have become necessary.

What format does an ERC-compliant DMP need to follow?

The ERC does not mandate a single required DMP template or format. Because research practices and data types vary enormously across the disciplines ERC funds — from particle physics to classical philology — a one-size template would fit almost none of them well. Instead, ERC grantees are expected to address the FAIR principles and the standard elements the European Commission’s Horizon Europe DMP template lays out, using whichever format best suits the discipline and project.

In practice, most ERC grantees do one of the following:

Adapt the European Commission’s own Horizon Europe DMP template (available from the Funding & Tenders Portal), which is the most widely used starting point and the one your institution’s research office is most likely to expect.
Use a DMP-authoring tool that already implements the Horizon Europe/FAIR structure, such as OpenAIRE’s Argos or DMPonline — see CASRAI’s DMPTool vs. DMPonline comparison for how the two major planning tools differ if your institution supports both.
Follow institutional guidance where a university research-data office has already standardized a Horizon Europe/ERC template across its portfolio of EU-funded projects.

Whichever format is used, funders and reviewers are increasingly expecting DMPs to be structured enough to be machine-actionable rather than static prose — see CASRAI’s explainer on the machine-actionable DMP (maDMP) concept and the RDA DMP Common Standard that underpins the tooling behind Argos and DMPonline.

What an ERC DMP needs to cover

Following the FAIR structure that both the European Commission’s template and ERC guidance point to, a complete DMP should address, for each dataset or output the project expects to produce:

Data summary: the purpose of data collection/generation, its relation to project objectives, types and formats, origin, expected scale, and to whom it may be useful.
FAIR data: how the data will be made findable (naming conventions, metadata, persistent identifiers such as DOIs), accessible (repository, access procedures, licensing), interoperable (standard vocabularies and formats), and reusable (licence terms, embargo periods, quality assurance).
Allocation of resources: the costs of making data FAIR, who is responsible for data management within the consortium or team, and how long-term preservation will be funded after the project ends.
Data security: how data will be stored and secured during the project, including recovery/storage arrangements for sensitive data.
Ethical aspects: any ethical or legal issues affecting data management, cross-referenced to the project’s ethics self-assessment where relevant.

See CASRAI’s general FAIR Data Principles entry and Data Management Plan (DMP) dictionary term for the underlying vocabulary this structure draws on, and the Data Management Plan Template and Structure guide for a discipline-neutral walkthrough of each section.

Legitimate exceptions and restricted data

The no-opt-out rule applies to the requirement to have a DMP, not to a requirement that every dataset be made openly available. Horizon Europe and ERC guidance both recognize that some data legitimately cannot or should not be shared openly. Recognized grounds include:

Intellectual property protection, where premature disclosure would undermine patentability or commercial exploitation.
Personal data and privacy, where sharing would breach data-protection obligations (including under the EU General Data Protection Regulation).
Security concerns, including national security or dual-use research considerations.
Confidentiality obligations owed to third parties or research participants.
The core objective of the action, where opening specific data would jeopardize achieving the project’s main goal.

The requirement is not that these grounds excuse a grantee from the DMP process — it is that the DMP itself must document which data falls under a restriction, on what basis, and how the restriction will be handled (e.g., metadata remains open and discoverable even where the underlying data is not).

How this differs from other funders’ DMP requirements

Researchers moving between US and EU funding will notice real differences in how DMP requirements are structured. NIH’s Data Management and Sharing Policy and the NSF’s directorate-level DMP requirements each set their own expectations for plan length, content, and submission timing — see CASRAI’s NIH vs. NSF Data Management Plans comparison and DMPTool guide for NSF and NIH plans for how those requirements work in practice. The ERC/Horizon Europe structure is distinctive in splitting the obligation into two stages — a short proposal-stage statement evaluated as part of scientific quality, followed by a detailed post-award deliverable — rather than requiring one complete plan at submission. Reviewers assessing what a “good enough” DMP looks like at each funder generally follow similar underlying criteria; see CASRAI’s DMP Review Criteria guide for the common threads across funders.

Frequently asked questions

Can I opt out of writing a DMP if my ERC project generates only a small amount of data?

No. Horizon Europe removed the opt-out that existed under Horizon 2020’s Open Research Data Pilot. Any ERC project that generates, collects, or reuses research data must produce a DMP, regardless of data volume — though the plan itself can and should be proportionate to how much data is actually involved.

When exactly is the ERC DMP due?

The full DMP is due as a formal project deliverable normally by month 6, counted from the start of the grant (project commencement following grant signature), not from the application deadline. A shorter, one-page description of anticipated data management is required earlier, at proposal stage, as part of the Excellence section of the application.

Does the ERC require a specific DMP template?

No. The ERC does not mandate one required format, given how widely data practices differ across the disciplines it funds. Grantees are expected to cover the FAIR principles, resource allocation, security, and ethical considerations, typically using the European Commission’s Horizon Europe DMP template or a tool such as Argos or DMPonline that implements the same structure.

What happens if my project doesn’t generate research data at all?

If a project genuinely generates no research data or other outputs requiring management, this should be stated plainly in the proposal-stage description rather than left blank. Consult your institution’s research-data office or EU grants office if you are unsure whether your project falls under the requirement — most projects that produce any measurements, code, models, or samples do.

Can data be kept closed under an ERC grant?

Yes, on documented, legitimate grounds — intellectual property, privacy/personal data, security, third-party confidentiality, or where openness would jeopardize the project’s core objective. The DMP must identify which data is restricted and why; it cannot simply omit restricted data from consideration.

ERC Data Management Plans Under Horizon Europe: Requirements, Timeline & Templates