CASRAI Dictionary

Tag: data management plan checklist

  • What Is a Data Management Plan? UKRI, NIH and EU Essentials

    A data management plan (DMP) is a formal document that sets out how a research project will collect, document, store, secure, share, and preserve its data, from the first data point to long-term archiving. Most major funders — including UKRI, the US National Institutes of Health (NIH), and Horizon Europe — now require a DMP at application stage, and increasingly expect it to be aligned with the FAIR principles. This explainer defines a data management plan, sets out why funders mandate one, breaks down its core components, and maps each section onto FAIR.

    A data management plan is, in one sentence: a written commitment describing what data a project will generate, how that data will be organised and protected while the project runs, and how — or whether — it will be shared and preserved once the project ends.

    What is a data management plan?

    A data management plan is a structured, funder- or institution-facing document describing how a project will handle its data across the full research lifecycle. It is drafted at proposal stage, before data collection begins, and treated as a living document revisited as the project evolves.

    A DMP is not a policy statement bolted onto a grant application. Reviewers use it to check that an applicant has thought through data volumes, storage costs, ethical constraints, and sharing obligations before funding is committed. Institutions use it to assign responsibility for storage and eventual deposit; funders use it to enforce open-data commitments after the award is made.

    Why do funders require a data management plan?

    Funders require a DMP because public and charitable research funding carries an expectation that resulting data — not just the resulting publication — is managed responsibly and, where possible, made available for verification and reuse. A DMP is the mechanism funders use to check this before they pay for the research, and to hold grantees to it afterwards.

    The three funders named in this explainer take slightly different approaches to timing and enforcement:

    Funder Governing policy When the DMP is due How it is enforced
    UKRI UKRI Common Principles on Data Policy, implemented per council (e.g. MRC, NERC) At proposal stage Assessed during peer review; council-specific detail expected proportional to data volume
    NIH NIH Data Management and Sharing (DMS) Policy, effective 25 January 2023 At application stage, for all NIH grants that generate scientific data Formal element of merit review; compliance with the approved plan is a condition of the award
    Horizon Europe Horizon Europe Data Management Plan requirement under the Model Grant Agreement A summary at proposal stage; the full DMP is due by month six and updated through the project Grant-agreement condition, monitored through periodic and final reporting

    The NIH policy is a useful marker of where funder expectations are heading: before January 2023, only NIH grants that explicitly generated large datasets needed a plan. Since that date, a Data Management and Sharing Plan is required for essentially all NIH-funded research that produces scientific data, replacing the earlier, narrower DMP requirement. Horizon Europe applies the principle “as open as possible, as closed as necessary” — data defaults to open, and any restriction must be justified in the plan itself, typically via deposit in European Open Science Cloud (EOSC)-federated infrastructure.

    What are the core components of a data management plan?

    What is included in a data management plan varies slightly by funder template, but nearly every DMP — UK, US, or EU — covers the same five areas:

    • Data types and volume: what kinds of data the project will generate or reuse (numerical, image, text, biological samples, code), in what formats, and at roughly what scale.
    • Documentation and metadata: how the data will be described so a third party — or the researcher, eighteen months later — can understand and reuse it without asking the original team.
    • Storage and security: where data will live during the project, how it is backed up, and who has access, particularly for sensitive or identifiable data.
    • Sharing and preservation: which data will be shared, through which repository, on what timeline, and which data will not be shared, with a stated justification.
    • Ethics, consent, and legal compliance: how personal, sensitive, or Indigenous data will be handled under relevant data-protection law and participant consent terms, and how intellectual-property or commercial-sensitivity constraints are addressed.

    A sixth element, often folded into the above, is roles and responsibilities: naming who on the project team is accountable for each of these tasks, since a DMP with no named owner tends not to get implemented.

    How do FAIR principles map onto a data management plan?

    The FAIR principles — Findable, Accessible, Interoperable, Reusable, published in Scientific Data in 2016 — are now the reference framework funders use to judge whether a DMP’s sharing commitments are substantive rather than nominal. Each FAIR letter corresponds to a specific, checkable DMP section:

    FAIR principle DMP section it governs What a reviewer checks for
    Findable Documentation and metadata A persistent identifier (e.g. a DOI) and rich, indexed metadata assigned at deposit
    Accessible Storage and sharing A stated repository and access protocol, plus clear conditions where access is restricted
    Interoperable Data types and formats Use of standard, non-proprietary formats and controlled vocabularies rather than bespoke formats
    Reusable Preservation and licensing A clear usage licence, provenance information, and community data standards followed at deposit

    This mapping is why a DMP written purely as a compliance checklist tends to fail review: a plan can name a repository (satisfying Accessible) while leaving metadata and licensing (Findable and Reusable) unaddressed, and a funder assessor trained on FAIR criteria will flag the gap.

    Common questions about data management plans

    What is in a data management plan?

    A DMP typically sets out the types of data to be produced, the metadata standards used to describe them, the storage and backup arrangements during the project, the access and sharing policy, and the plan for long-term archiving so the data remains usable after the project ends.

    How do you write a data management plan?

    Start from the funder’s own template rather than a blank page, since UKRI, NIH, and Horizon Europe each specify required headings. Describe data types and volumes first, then storage, ethics, and sharing, and be explicit about what will not be shared and why — a stated exception is stronger than a silent gap.

    Do I need a data management plan?

    If the project is funded by a body with a research-data policy — which now includes most major UK, US, and EU funders — a DMP is mandatory at application stage, not optional. Institutions increasingly also require one for internally funded or unfunded projects that handle sensitive data, as a matter of good practice.

    What does a good data management plan look like?

    A strong DMP is specific rather than generic: it names an actual repository rather than “a suitable repository,” gives a realistic storage volume, and assigns a named person to each task. It is written to be checked against, not filed and forgotten — funders increasingly audit compliance with the plan they approved, not just its existence.

    What this means for researchers and institutions

    Why data management plans matter is shifting from a compliance formality to an operational one. NIH’s move to require a plan for essentially all data-generating awards, not just large-dataset ones, signals broadening rather than narrowing scrutiny. Horizon Europe’s mid-project update requirement means the document cannot be written once and ignored; it is checked against actual practice at reporting milestones.

    For institutions, this means DMP-writing guidance, repository access, and named data stewards are becoming a baseline service rather than a specialist offering — mirroring how research-administration functions increasingly treat authorship, funding acknowledgement, and data policy as connected obligations. For individual researchers, treating the DMP as a working document rather than a one-off application formality is now the defensible position across every major funder covered here.

    For funder-specific DMP templates and requirements, consult the relevant funder’s own guidance pages; for the broader compliance context these plans sit within, see CASRAI’s research administration resources and research-terminology dictionary.

  • Data Management Plan Templates by Discipline

    A data management plan template sets out what data a project will produce, how it will be documented, stored, protected and shared, and who is responsible for each step. There is no single universal template: a physical-science plan built around instrument calibration and file formats looks very different from a life-science plan built around clinical consent and genomic repositories, or a social-science plan built around participant anonymisation and survey metadata. A data management plan (DMP) is a formal document, usually required at grant application or award stage, that describes how research data will be handled across the full research lifecycle, from collection through to long-term preservation or disposal.

    What every data management plan template must include

    Regardless of discipline, funders expect the same core sections. The Science Europe RDM Guide structures its template around 15 questions covering six core requirements: data description, documentation and metadata, storage and backup, legal and ethical requirements, data sharing, and responsibilities and resources. The UK’s Digital Curation Centre (DCC) publishes a parallel checklist used by most UK universities as the basis of their local DMP guidance.

    • Data description — types, formats, volumes and provenance of data to be generated or reused
    • Documentation and metadata standards that will make the data intelligible to others
    • Storage, backup and security arrangements during the active project
    • Ethical, legal and consent considerations, including any restrictions on sharing
    • Preservation, repository choice and long-term access arrangements
    • Roles, responsibilities and resourcing for data management tasks

    These core sections are then adapted to the realities of the data itself. That adaptation — not the boilerplate headings — is where discipline-specific templates diverge, and where a generic one-size-fits-all template becomes a liability rather than a help.

    Physical sciences: instrument and sensor data

    Physical-science DMPs — physics, chemistry, astronomy, earth and environmental sciences — are dominated by high-volume instrument, sensor and simulation output rather than by human-subjects concerns. The template needs to say more about formats, calibration and reduction, and comparatively little about consent.

    • Community-accepted file formats (for example FITS in astronomy, NetCDF in climate and earth science) to guarantee interoperability
    • Instrument and calibration metadata, so raw readings remain interpretable and reproducible years later
    • Data volume and velocity planning — strategies for reduction, transfer and storage of large or continuous streams from sensors, telescopes or particle detectors
    • Software and code versioning, since simulation and analysis code is often as essential to reproducibility as the raw data itself
    • Discipline repositories such as PANGAEA (earth and environmental data) or domain-specific archives maintained by observatories and facilities

    The European Research Council’s DMP template for Horizon Europe-funded projects follows the same emphasis: it requires beneficiaries to describe data volumes, formats and FAIR compliance rather than consent procedures, reflecting the instrument-heavy profile of much ERC-funded physical-science work.

    Life sciences: biological and clinical data

    Life-science DMPs — biology, medicine, genomics, clinical research — carry a heavier ethical and regulatory load. A strong template treats consent, de-identification and repository choice as first-class sections, not footnotes.

    • Ethical and legal compliance: how human-subject or animal data will be de-identified, and how the plan aligns with UK GDPR and relevant research ethics committee approvals
    • Community ontologies and minimum-information standards such as MIAME for microarray data or Darwin Core for biodiversity records, which allow datasets to be compared across studies
    • Persistent identifiers for samples, datasets and participants, supporting findability without compromising anonymity
    • Genomic and clinical data deposition requirements — for example, NIH policy requires eligible genomic datasets to be deposited in controlled-access repositories such as dbGaP or GEO
    • The UK’s Medical Research Council (MRC) requires a personalised DMP, using a published UKRI template, for MRC-funded studentships and non-doctoral training grants

    Because clinical and genomic data are rarely fully open, life-science templates typically distinguish between what will be shared openly, what will be shared under a data access agreement, and what cannot be shared at all — a three-tier distinction that is largely absent from physical-science templates.

    Social sciences: qualitative and survey data

    Social-science DMPs — sociology, psychology, education, economics — centre on informed consent, anonymisation and the management of qualitative material such as interview transcripts and survey responses.

    • Informed consent procedures and how participants are told what will happen to their data, including any future-use or secondary-use provisions
    • Anonymisation and de-identification plans for both qualitative data (transcripts, recordings, field notes) and quantitative survey data
    • Metadata standards for survey and social data, such as the Data Documentation Initiative (DDI), used by archives to describe questionnaires and variables
    • Data access agreements for restricted or sensitive datasets, specifying who can apply for access and under what conditions
    • Long-term archiving through a recognised social-science data service — in the UK, the UK Data Service (based at the UK Data Archive, University of Essex) is the standard repository for shareable social and economic data

    Qualitative data management also needs its own sub-plan: transcription protocols, version control across iterative coding, and a named point at which raw recordings are destroyed or securely archived under the original consent terms.

    Dimension Physical sciences Life sciences Social sciences
    Dominant data type Instrument, sensor, simulation output Genomic, clinical, biological samples Interview, survey, observational records
    Key standards FITS, NetCDF MIAME, Darwin Core DDI (Data Documentation Initiative)
    Primary risk to manage Volume, format interoperability Participant privacy, consent, regulation Anonymisation, re-identification risk
    Typical repository PANGAEA, facility archives dbGaP, GEO, ENA UK Data Service / UK Data Archive

    Frequently asked questions

    How do you write a data management plan?

    Start from your funder’s own template where one exists — UKRI, the ERC and most UK universities publish one — then work through data description, storage, ethics, sharing and preservation in turn. Discipline-specific detail, such as file formats or consent procedures, should be added within each section, not bolted on afterwards.

    What is included in a data management plan?

    A complete DMP describes the types of data that will be produced, the metadata and documentation standards used, storage and backup arrangements, ethical and legal requirements, sharing and access conditions, and preservation plans, with named roles for each task.

    Do all researchers need a data management plan?

    Most UK and EU research funders — including UKRI, Horizon Europe and members of cOAlition S — now require a DMP as a condition of funding. Even without a mandate, a DMP reduces the risk of data loss and supports compliance with institutional data protection policy.

    What does a good data management plan look like?

    A good DMP is specific rather than generic: it names the exact repository, format and access conditions that apply to the project’s actual data, not boilerplate language borrowed from an unrelated template. It is also a living document, revisited as the project changes.

    What this means for research administrators

    Research offices that hand every applicant the same generic DMP template are setting up avoidable review delays: reviewers increasingly expect discipline-appropriate detail on formats, consent or anonymisation rather than restated boilerplate. Building three lightweight discipline variants — physical, life and social science — from one core checklist, as outlined above, lets an institution keep a single governance structure while giving each researcher a template that actually matches their data.

    The underlying reference point across all three variants is the FAIR data principles — Findable, Accessible, Interoperable, Reusable — first formalised by Wilkinson et al. in Scientific Data (2016) and now embedded in funder policy from Horizon Europe to UKRI. Fair data management is the common thread; the template detail is where disciplines genuinely diverge. Institutions building or revising DMP guidance should treat the research administration function, not just the library, as the natural owner of discipline-specific template maintenance, and consult the CASRAI Dictionary for consistent definitions of the terms used across templates.

  • NSF Data Management Plan: A Directorate Guide

    An NSF data management plan (DMP) is a required proposal component describing how a project will handle, share, and preserve research data — and since 27 April 2026, its exact content is set by a structured Research.gov webform that adapts to the proposal’s lead directorate, meaning BIO, ENG, GEO, MPS, OPP, SBE, and EDU proposals no longer follow one identical template. Treating NSF as a single monolithic funder — the default approach in most DMP guides — now produces plans that miss directorate-specific expectations.

    A data management and sharing plan (DMSP) is the National Science Foundation’s formal proposal document setting out how a funded project will manage, share, and archive the data, samples, and other research products it produces. Under the Proposal & Award Policies and Procedures Guide (PAPPG) section II.D.2(ii) and Policy Notice NSF 26-202, every full proposal must include one — or a documented justification if the project will not generate data.

    What Does an NSF Data Management Plan Require in 2026?

    Every NSF proposal must address six general elements under PAPPG II.D.2(ii), regardless of directorate. These form the baseline that directorate-specific guidance then narrows or extends.

    • The types of data, samples, physical collections, software, and curriculum materials the project will produce
    • The standards used for data and metadata format and content, with a documented workaround where no standard exists
    • Policies for data access and sharing, including privacy, confidentiality, security, and intellectual-property protections
    • Policies for data reuse, redistribution, and the production of derivative products
    • Plans for archiving data and other research products and preserving long-term access to them

    Under Policy Notice NSF 26-202, a proposal that will not generate data does not need a full plan — a short justification statement satisfies the requirement instead.

    How Do NSF DMP Requirements Differ by Directorate?

    NSF publishes supplementary DMP guidance for seven directorates and offices — BIO, ENG, GEO, MPS, OPP, SBE, and EDU — plus at least one program-specific supplement (DMREF, under MPS). Where a directorate has issued no supplement, the general PAPPG rules apply by default. The table below summarises what each adds on top of the baseline.

    Directorate/Office Distinguishing emphasis
    Biological Sciences (BIO) Deposit in community-recognized public repositories (e.g. GenBank-class databases) with persistent identifiers linking data to publications
    Engineering (ENG) Broad coverage of software, models, and physical collections; attention to intellectual property where research has commercial potential
    Geosciences (GEO) Discipline-specific repository requirements — Ocean Sciences awardees, for example, are directed to the Biological and Chemical Oceanography Data Management Office (BCO-DMO)
    Mathematical & Physical Sciences (MPS) General guidance, plus a dedicated program-level supplement for the Designing Materials to Revolutionize and Engineer our Future (DMREF) programme
    Office of Polar Programs (OPP) Governed by a separate Dear Colleague Letter establishing a distinct data and code/sample management policy rather than the standard DMSP framework alone
    Social, Behavioral & Economic Sciences (SBE) Heavy emphasis on human-subjects protections — anonymisation, handling of personally identifiable information, informed consent for data sharing, and deposit in recognised social-science archives
    STEM Education (EDU) Maintains its own directorate-level data-management-plans page addressing education-research data and human-subjects considerations

    This directorate layering is why a plan drafted for a BIO proposal will read very differently from one drafted for an SBE or OPP proposal, even though both start from the same PAPPG baseline.

    What Changed With the April 2026 Research.gov Webform?

    Effective 27 April 2026, NSF replaced the free-standing two-page PDF data management and sharing plan with a structured webform submitted directly through Research.gov. This is the single most consequential change to NSF DMP practice since the policy’s creation, and it makes most “download the template” search results obsolete.

    • The plan is now entered field-by-field in Research.gov rather than uploaded as a standalone PDF attachment
    • The webform adapts its prompts to the proposal’s selected lead directorate, formalising the differences long implied but not enforced by the old PDF format
    • Investigators should verify current guidance for their directorate before assuming a saved PDF template from a prior submission still matches the required fields

    Institutional research offices that maintain locally cached “NSF DMP template” documents should retire the PDF version and point investigators to the live Research.gov webform for the current submission year.

    NSF Data Management Plan Checklist, by Directorate

    Use this sequence to build a directorate-appropriate plan rather than a generic one:

    • Confirm the proposal’s lead directorate and pull its specific guidance page (BIO, ENG, GEO, MPS, OPP, SBE, or EDU) alongside the general PAPPG II.D.2(ii) requirements
    • List every data type, sample, and software product the project will generate
    • Identify the metadata standard and, for BIO or GEO proposals, the target public repository (e.g. BCO-DMO for ocean sciences)
    • For SBE or human-subjects work, document anonymisation, consent, and PII-handling procedures explicitly
    • For OPP proposals, check whether the relevant Dear Colleague Letter policy supersedes the standard DMSP structure
    • Set an archiving and long-term preservation plan with a named repository or institutional data service
    • Submit through the Research.gov webform rather than attaching a standalone PDF

    Common Questions About NSF Data Management Plans

    Does NSF require a data management plan?

    Yes. NSF requires a data management and sharing plan as a mandatory component of every full proposal, per PAPPG II.D.2(ii). Proposals that will not produce data must instead include a written justification explaining why no plan is needed.

    What is included in a data management plan?

    A complete plan covers the types of data produced, the metadata standards applied, access and sharing policies, provisions for reuse and derivatives, and an archiving and preservation plan for long-term accessibility.

    Do I need a data management plan?

    Any NSF full proposal needs one unless the project genuinely generates no data, samples, or research products — in which case a short justification statement, not a full plan, satisfies the requirement under Policy Notice NSF 26-202.

    What This Means for Research Administrators

    Directorate-tailored webforms shift the compliance burden earlier in the proposal cycle. Research offices that previously offered a single boilerplate DMP template now need directorate-aware review checkpoints, because a plan that satisfies BIO’s repository expectations will not automatically satisfy SBE’s human-subjects requirements or OPP’s separate policy letter. Institutions supporting multi-directorate portfolios should update internal guidance documents to reference the correct directorate page rather than a single generic NSF DMP resource.

    The Outlook for NSF Data Management Requirements

    The move to a structured, directorate-tailored webform signals that NSF intends to enforce, rather than merely suggest, discipline-specific data practices. Investigators and research offices that continue treating the NSF data management plan as a single generic two-pager risk submitting plans that technically comply with PAPPG but miss the sharper, directorate-specific expectations now built into the submission system itself.

  • DCC Data Management Plan Tool vs US Options

    DMPonline, run by the UK’s Digital Curation Centre (DCC), is the de facto standard tool for producing a DCC data management plan at British institutions, built around UK funder templates and institutional branding; US-centric platforms such as DMPTool cover the same workflow but are tuned to NSF and NIH requirements instead. For a UK institution choosing between them, the decision turns on three factors: funder template coverage, institutional customisation, and export format compatibility.

    A data management plan (DMP) is a formal document, typically required at the grant-application stage, that describes how research data will be collected, documented, stored, shared and preserved throughout and after a project.

    Contents

    What is DMPonline and who maintains it?

    DMPonline is a free, web-based tool that helps researchers create, review and share data management plans that meet institutional and funder requirements. It is provided by the Digital Curation Centre, a UK-wide body hosted by the Universities of Edinburgh, Glasgow and Bath that has produced data curation guidance since 2004.

    DCC’s own resource pages describe DMPonline as “a flexible web-based tool to assist users to create personalised plans according to their context or research funder,” supplemented by a published checklist and funder-requirements summary that institutions reuse in local guidance. This positions DMPonline less as a generic form-filler and more as a curated gateway into UK research data policy.

    How does DMPonline compare with US-based DMP tools like DMPTool?

    The principal US equivalent is DMPTool, operated by the California Digital Library’s University of California Curation Center (UC3). Functionally, the two platforms are close cousins: DMPonline and DMPTool both run on the open-source DMP Roadmap codebase, a joint DCC/California Digital Library development effort, which is why their editing interfaces and template logic look similar.

    The divergence is in orientation. DMPTool’s own platform messaging emphasises “machine-actionable data management and sharing plans (DMSPs)” and a mechanism for registering a persistent DMP ID for each plan — a feature aimed squarely at US funder and repository integration. DMPonline instead foregrounds UK and European funder templates and DCC-authored guidance, with less emphasis on identifier registration.

    • Governance: DMPonline is DCC-run (UK); DMPTool is UC3/California Digital Library-run (US), though both share development history.
    • Primary audience: DMPonline serves UK and European researchers; DMPTool serves US researchers, chiefly those funded by NSF or NIH.
    • Identifier support: DMPTool actively promotes DMP ID registration for machine-actionable plans; DMPonline’s strength is curated funder-specific question sets.

    Which funder templates do UK and US platforms cover?

    Funder template coverage is where the two ecosystems diverge most sharply, because UK and US funders impose structurally different DMP requirements.

    DCC’s published summary of UK funder expectations shows the requirement is not uniform across UKRI’s research councils: NERC mandates a single-page Outline Data Management Plan for all grant and fellowship applications; ESRC requires a data management and sharing plan as an integral part of every application; MRC requires a plan at proposal stage using its own template; BBSRC requires a data-sharing plan covering formats, standards and release timeframes; and EPSRC, by contrast, does not require a formal DMP at all, expecting only that data be preserved and shared. The Wellcome Trust asks for a data-sharing plan addressing seven set questions, and STFC recommends eight. DMPonline builds each of these directly into its template library.

    US coverage runs the other way. Under NSF policy, proposals submitted on or after 18 January 2011 must include a supplementary Data Management Plan document of no more than two pages. NIH’s Data Management and Sharing (DMS) Policy became effective on 25 January 2023, requiring an approved DMS plan for most funded research. DMPTool’s own release notes confirm ongoing template maintenance for both — recent updates added NSF templates mirroring the Research.gov webform and an updated NIH DMS plan template.

    Factor DMPonline (DCC, UK) DMPTool (UC3, US)
    Primary funder templates UKRI councils (AHRC, BBSRC, ESRC, MRC, NERC, STFC), Wellcome Trust, CRUK, British Heart Foundation, Horizon Europe NSF, NIH, plus other US federal agencies
    Governing body Digital Curation Centre (Edinburgh, Glasgow, Bath) California Digital Library / UC3
    Codebase DMP Roadmap (shared) DMP Roadmap (shared)
    Distinct feature DCC-curated guidance and checklist embedded per question DMP ID registration for machine-actionable plans
    Cost model Free to use; DCC-funded Free, community-supported by participating organisations

    How do institutional branding and export formats differ?

    Both platforms let subscribing institutions layer their own guidance on top of funder templates, but UK adoption of this feature is unusually deep. The Universities of Cambridge, Oxford, Edinburgh, Durham and York all direct researchers to institution-branded DMPonline instances with local examples, contacts and policy links rather than the generic DCC template alone.

    DMPTool offers equivalent institutional customisation and maintains a public directory of participating organisations, but its branding layer is oriented around US library and research-office workflows.

    On export formats, both tools produce human-readable plans (typically PDF or Word) for attachment to a grant application, and both are built to support machine-actionable outputs aligned with the Research Data Alliance’s DMP Common Standard — a specification the DCC helped develop through its long-standing role in RDA’s DMP Common Standards working group. DMPTool goes a step further operationally by issuing a registrable DMP ID per plan, which is not a standard DMPonline feature.

    Which platform should a UK institution choose?

    For a UK institution, DMPonline is the practical default because its template library already maps to UKRI council requirements, Wellcome, CRUK and Horizon Europe — the funders a UK-based researcher is actually likely to encounter. Choosing a US-centric tool instead would mean losing that pre-built mapping and manually adapting NSF- or NIH-oriented question sets to UK funder wording.

    The exception is genuinely transatlantic collaboration: a UK institution with US co-investigators or US sub-awards may need both platforms in parallel — DMPonline for the UK funder-facing plan, DMPTool where a US partner’s DMP ID or NSF/NIH template is contractually required.

    Common questions about data management plan tools

    What are examples of data management tools?

    The main dedicated DMP tools are DMPonline (Digital Curation Centre, UK-focused) and DMPTool (California Digital Library, US-focused), both built on the shared DMP Roadmap codebase. Institutions also use repository platforms, electronic lab notebooks and metadata catalogues as complementary data-management infrastructure alongside a dedicated DMP editor.

    What should a data management plan include?

    A UK-funder-conformant data management plan typically covers what data will be created, how it will be documented and stored, data security and ethical considerations, intellectual property, and the timeline and method for sharing or preserving the dataset after the project ends. Exact sections vary by funder template.

    What are DMP tools?

    DMP tools are web-based platforms that guide researchers through funder-specific question sets to produce a compliant data management plan, then export it as a document or machine-actionable record. DMPonline and DMPTool are the two most widely adopted examples, each aligned to a different national funder landscape.

    What this means for research administrators

    Research offices supporting grant applications should treat platform choice as a compliance decision, not a preference. Using DMPonline’s UKRI-mapped templates reduces the risk of a plan being rejected for missing council-specific requirements, since NERC, ESRC and MRC each specify distinct mandatory content.

    Institutions with international grant portfolios should budget administrative time for maintaining both a DMPonline and a DMPTool account, rather than assuming one platform can serve every funder relationship a research-active department holds.

    The outlook for DMP tooling in UK institutions

    DMPonline’s advantage for UK institutions is structural, not cosmetic: it is built around the funder landscape UK researchers actually face, from NERC’s single-page mandate to Wellcome’s seven-question format. US-centric tools remain the right choice for US-funded work, and the shared DMP Roadmap codebase means the two ecosystems are likely to keep converging on machine-actionable export standards even as their funder template libraries stay nationally distinct. For UK research administration teams, the practical rule is simple: default to DMPonline for UK and European funders, and add DMPTool only where a specific US funder or collaborator requires it.

  • Horizon Europe Data Management Plan: Field Guide

    The Horizon Europe Data Management Plan (DMP) template is a seven-section document — data summary, FAIR data, other research outputs, resource allocation, security, ethics, and other issues — that the European Commission recommends beneficiaries use to show how project data will be made Findable, Accessible, Interoperable and Reusable. It is due as a project deliverable within the first six months of a grant and must be kept current for the project’s duration.

    A Data Management Plan is a living document describing how research data and other outputs are generated, documented, secured, and shared, both during and after a funded project. Under Horizon Europe, the plan is not a formality: it is the mechanism through which the Commission’s FAIR data mandate under the Horizon Europe Programme Guide (Open Science, Article 17) is operationalised at project level.

    What the Horizon Europe DMP template covers

    The Commission’s recommended template, published on the Funding & Tenders Portal, structures the plan around seven headline sections. Each section exists to answer a specific compliance question the Commission needs resolved before, during, and after data generation.

    The data summary section opens the document. It requires beneficiaries to state whether data will be newly generated, reused from existing sources, or both; to describe expected data types, formats, and volumes; to explain how the data relates to the project’s objectives; and to identify who outside the consortium might find the data useful. If reuse of existing data was considered and rejected, that decision must be justified in writing.

    Section by section: what each part requires

    The template’s largest section — FAIR data — is split into four sub-parts that map directly onto the FAIR acronym. This is where most of the compliance burden, and most drafting errors, sit.

    Making data findable

    Beneficiaries must state whether data and metadata will receive a persistent identifier such as a DOI, which metadata standard will be used (for example Dublin Core or a discipline-specific schema), and whether search keywords will be added so the dataset can be indexed by data catalogues and harvested by aggregators such as OpenAIRE.

    Making data accessible

    This sub-section requires naming the trusted repository, stating whether data will be openly accessible by default, and — where access is restricted — providing a documented justification tied to legitimate interests, GDPR, security, or intellectual property constraints. Metadata should remain openly accessible under a CC0 licence even when the underlying dataset is closed.

    Making data interoperable

    Here the plan must name the vocabularies, ontologies, or methodologies used so the data can be combined with other datasets and read by non-project tools without manual reformatting.

    Increasing data reuse

    The final FAIR sub-section covers licensing terms (CC BY or CC0 by default, following the “as open as possible, as closed as necessary” principle), data provenance documentation, and the quality-assurance processes applied before deposit.

    Beyond FAIR data, three further sections complete the template:

    Section Core requirement Primarily maps to
    3. Other research outputs Software, models, workflows, protocols and physical samples managed under the same FAIR logic as data Findable, Reusable
    4. Allocation of resources Costs of making data FAIR, named responsibility for data management, long-term preservation funding Accessible
    5. Data security Secure storage, backup, recovery provisions, and secure transfer of sensitive data Accessible, Reusable
    6. Ethical aspects Handling of personal or sensitive data in line with GDPR and the project’s ethics review Accessible
    7. Other issues Any procedure or standard not captured elsewhere (e.g. national/funder-specific rules) All four FAIR pillars

    Beneficiaries should also confirm that deposited datasets carry the minimum metadata fields the Commission expects: author(s), a description or abstract, the deposit date, the licence, embargo terms if any, and the grant project name, acronym, and number.

    When the DMP is due, and whether the template is mandatory

    The European Commission states plainly on the template’s own download page that “the template is recommended but not mandatory” — beneficiaries may use their own format provided it still satisfies the underlying research-data-management obligations in the Grant Agreement.

    The timeline itself is fixed regardless of format:

    • Proposal stage: a short, typically one-page DMP outline is required as part of the proposal; a full DMP is not expected yet, except where a Work Programme calls for one at signature (for example, public-emergency topics).
    • Month 6: the full initial DMP must be submitted as a formal project deliverable.
    • During the project: the DMP is a living document; for projects running longer than 12 months, at least one updated version must be submitted.
    • Project end: a final DMP records how data were actually managed, preserved, and shared.

    Trusted repositories for deposit are those holding certifications such as CoreTrustSeal, Nestor Seal DIN 31644, or ISO 16363, or domain-specific repositories widely endorsed by the relevant research community. A 2024 metadata-readiness review commissioned by the European Research Council (Lazzeri, 2024) found that only a handful of repositories — including Zenodo, DANS, and HAL — met the Commission’s “Essential” metadata-readiness level outright, with several others still retrofitting mandatory fields. This is a practical planning risk: naming a repository in Section 2.2 that later turns out metadata-incomplete forces a DMP revision mid-project.

    Common questions about the Horizon Europe DMP

    Is the Horizon Europe DMP template mandatory?

    The template itself is optional; beneficiaries can use another format. What is mandatory, under the Grant Agreement, is establishing a DMP by month 6, keeping it updated, and depositing data in a trusted repository consistent with FAIR principles.

    When is the Data Management Plan due in Horizon Europe?

    A full initial DMP is due by month 6 of the project as a formal deliverable. A brief DMP-like outline is required earlier, at proposal stage, and the plan must be updated again for projects longer than 12 months.

    What do the FAIR data principles require in a Horizon Europe DMP?

    FAIR requires data to be Findable via persistent identifiers and rich metadata, Accessible through a trusted repository with a clear access policy, Interoperable using recognised standards or vocabularies, and Reusable under a documented licence with provenance and quality information.

    Does the DMP need to be updated after submission?

    Yes. The Commission treats the DMP as a living document. Any material change — a new dataset, a changed repository, an altered access decision — must be reflected in an updated version submitted as a subsequent deliverable.

    What this means for research administrators

    For UK institutions, the compliance picture has a domestic wrinkle worth flagging: the UK re-associated to Horizon Europe from 1 January 2024, and UK-based participants funded via the UKRI Horizon Europe Guarantee are still contractually bound by the same DMP and FAIR data obligations as any other beneficiary — the Guarantee changes the funding route, not the data-management requirements. Research offices supporting UKRI-guaranteed grants should apply the Horizon Europe template rather than a UKRI-native one.

    Institutions preparing their first Horizon Europe DMP can reduce drafting time using ARGOS, OpenAIRE’s free tool built around Horizon Europe’s own template structure, which prompts for each of the seven sections with contextual guidance. Because Section 4 requires naming a responsible individual and Section 5 requires named security measures, research administration teams should treat DMP drafting as a cross-functional task involving the data steward, the ethics lead, and the finance officer who costs the resource-allocation section — not a document a single researcher completes alone. Institutions with broader research administration workflows should build DMP review into the same grant-management checkpoints used for ethics and finance sign-off, rather than treating it as a stand-alone open-science task.

    The direction of travel across EU funding is toward more structured, field-level FAIR reporting rather than narrative compliance statements — institutions that build DMP drafting into standard grant-lifecycle checkpoints now will spend less time on ad hoc revisions as reporting expectations tighten.

  • UKRI Data Management Plan Template Guide for Multi-Council Grants

    UKRI’s common data management plan template asks applicants to describe, section by section, how research data will be generated, documented, stored, shared and preserved — but the level of detail, word limit and submission requirement differ by council: MRC and BBSRC mandate a full plan, NERC requires only a one-page outline, and EPSRC does not require submission at all.

    A data management plan (DMP) is a structured document, submitted with or alongside a grant application, that specifies how research data will be collected, documented, stored, shared and preserved throughout and after a funded project. For UKRI-funded researchers, the practical difficulty is not knowing what a DMP is — it is knowing which version of the UKRI data management plan template applies to their council, how long it should be, and what each field is actually asking for. This walkthrough goes section by section across the four councils most research administrators handle together on multi-strand or interdisciplinary awards: MRC, BBSRC, NERC and EPSRC.

    What does UKRI’s common data management plan template cover?

    UKRI does not operate a single, mandatory template across all seven research councils. Instead, each council publishes its own guidance built around a common core of questions: what data will be produced, how it will be documented, where it will be stored, who can access it, and how long it will be retained. This shared structure is why researchers refer informally to a “UKRI data management plan template”, even though the actual document you complete depends on which council is funding the work.

    The starting point for most multi-council applicants is the MRC data management plan template, a Word document published via UKRI’s publications library, because several other councils’ library-hosted templates (including NC3Rs-badged studies) reuse its structure. NERC, BBSRC and EPSRC each layer council-specific expectations — word limits, submission timing, and retention periods — on top of that shared skeleton.

    How do requirements differ across MRC, BBSRC, NERC and EPSRC?

    The single biggest source of error in multi-council DMPs is applying one council’s rules to another council’s proposal. The table below sets out the four core differences research administrators need to check before drafting.

    Council DMP required at application? Template source Length Minimum data retention
    MRC Yes — mandatory for all funding proposals MRC Data Management Plan template (UKRI publications library) 500–1,500 words; 1,500 words for longitudinal studies, population cohorts, genetic, omics, imaging data and biobanks 10 years (20 years for population health and clinical studies)
    BBSRC Yes — mandatory for grant applications BBSRC template via DMPOnline (Digital Curation Centre) Maximum 500 words (check individual grant-stream variation) 10 years after project completion
    NERC Yes — one-page outline at application; full plan later NERC Outline Data Management Plan template and guidance (UKRI publications library) One page at application; full plan agreed with the relevant NERC data centre within 3–6 months of award start 10 years minimum
    EPSRC No — not submitted with the application No dedicated EPSRC council template; DMPOnline hosts an EPSRC-structured version for internal use No fixed limit — proportionate to the project 10 years from the end of any privileged-access period

    EPSRC is the outlier: it does not require a DMP to be submitted with the proposal, but most host institutions’ own research data policies still require one to exist internally so costs and storage needs are planned accurately. STFC sits closer to MRC and BBSRC — a DMP is mandatory for most schemes and capped at two sides of A4 — but, unlike MRC, STFC does not prescribe a fixed template.

    Completing the template field by field

    Across MRC, BBSRC, NERC and EPSRC guidance, the same seven fields recur, even where wording and word allowances differ. Address each one in this order.

    • Data collection and generation. State the type of data (quantitative, qualitative, imaging, genomic, environmental sensor data, software), the format, the estimated volume, and whether it is newly generated or reused from an existing source.
    • Documentation and metadata. Name the metadata standard you will apply and describe accompanying documentation — a data dictionary, README file or laboratory notebook — needed for another researcher to interpret the dataset without you.
    • Ethics, consent and legal basis. Cover informed consent, anonymisation or pseudonymisation methods, and who holds intellectual property rights, particularly for MRC-funded clinical or population studies, where this field is scrutinised most closely.
    • Storage and security during the project. Specify where data will sit while the grant is active, backup frequency, and access controls — this is where EPSRC-funded teams should still document internal practice even though nothing is submitted to the council.
    • Long-term preservation. Name the repository (an institutional archive, a NERC environmental data centre, or the UK Data Service for ESRC-adjacent social science data) and confirm the retention period matches your council’s minimum from the table above.
    • Data sharing and access conditions. State which datasets will be shared openly, any embargo or proprietary period, and the justification if some data cannot be shared — commercial sensitivity, participant privacy or national security are the standard justifications UKRI accepts.
    • Responsibilities and resourcing. Name who owns data management delivery after the grant ends and itemise any storage, curation or specialist-staff costs, which can — and should — be included in the full economic cost of the proposal.

    For MRC and NERC applications specifically, the plan text is typically copied directly into the Je-S or funding-service application form rather than uploaded as a separate attachment — check the individual call documentation, since attachment rules vary by scheme and change between funding rounds.

    Common questions about the UKRI data management plan

    How do you write a data management plan?

    Start from your funding council’s specific template rather than a generic one, then work through data collection, documentation, storage, sharing and retention in turn. Keep language concrete and proportionate to your project’s data volume, and justify any decision not to share data rather than leaving it unexplained.

    What is included in a data management plan?

    A complete plan covers the types of data produced, the metadata and documentation standards used, storage and security arrangements, the repository chosen for preservation, access and sharing conditions, and the retention period. UKRI councils also expect a statement of who is responsible for delivery and what resources this requires.

    Do you need a data management plan for a UKRI grant?

    It depends on the council. MRC, BBSRC, NERC and STFC require a DMP to be submitted with most funding proposals, while EPSRC does not require submission, and AHRC has no general DMP requirement at all. Always confirm the specific call documentation, since requirements can vary by scheme within a single council.

    What does a good data management plan look like?

    A strong plan is specific to the project rather than generic, stays within the council’s stated word or page limit, and answers every field with a concrete detail — a named repository, a defined retention period, a stated metadata standard — instead of a vague intention. Reviewers assess it alongside the rest of the proposal during peer review.

    What this means for multi-council applicants

    Institutions running interdisciplinary programmes — a BBSRC-MRC joint call, or a NERC-EPSRC environmental engineering award — cannot draft one DMP and submit it unchanged to both funders. Word limits alone range from 500 words (BBSRC) to 1,500 words (MRC’s most data-intensive study types), and only NERC requires a two-stage outline-then-full-plan process. Research administration teams supporting these awards should build a field-by-field checklist per council into their proposal workflow, rather than relying on a single house template.

    As UKRI continues to consolidate open-research expectations across its councils, researchers should expect incremental convergence on shared metadata and repository standards — but not, in the near term, a single mandatory cross-council template. Until that happens, matching the right template to the right council, at the right length, remains the determining factor in a compliant submission.

    For teams coordinating research administration workflows across funders and councils, see CASRAI’s research administration resources, and consult the CASRAI Dictionary for definitions of related research data terminology.