CASRAI Dictionary

Tag: data management plan template

  • DMPonline vs DMPTool vs Argos: DMP Tool Guide

    DMPonline, DMPTool and Argos are the three leading platforms for writing a data management plan (DMP): DMPonline (Digital Curation Centre, UK) and DMPTool (California Digital Library, US) share the same open-source DMP Roadmap codebase, while Argos (OpenAIRE) is built for machine-actionable, European open-science workflows. The right choice depends on your funder’s templates, whether your institution offers a branded instance, and whether you need structured API export.

    A data management plan tool is software that walks a researcher through funder- and institution-specific questions, stores the resulting answers as a structured document, and — increasingly — exports that document in a machine-readable format rather than as static prose. DMPonline is the Digital Curation Centre’s web-based DMP-writing service, built on the open-source DMP Roadmap platform it co-develops with the California Digital Library. This guide compares it against DMPTool and Argos on the three factors that actually decide adoption: funder-template coverage, institutional branding, and API export.

    What is DMPonline, and who runs it?

    DMPonline is a free web application, developed and hosted by the Digital Curation Centre (DCC), based at the University of Edinburgh. It supports researchers in producing a data management plan against a specific funder or institutional template, with embedded guidance text at each question. It is the standard reference tool for UK Research and Innovation (UKRI) grant-holders and is widely adopted across UK and European universities.

    Many institutions run their own branded instance rather than sending researchers to the generic service — the University of Manchester, University of Sheffield, University of Plymouth and University of Exeter all operate dedicated DMPonline subdomains with local templates and guidance layered on top of the shared DCC platform.

    DMPonline vs DMPTool: same codebase, different communities

    DMPonline and DMPTool are not separate products built by rival teams — they run on the same open-source codebase, DMP Roadmap, jointly developed by the DCC and the California Digital Library (CDL). The practical difference is community and funder coverage, not underlying functionality.

    DMPTool, operated by the CDL (part of the University of California system), is the default choice for US-based researchers, carrying templates for agencies such as the National Science Foundation (NSF) and National Institutes of Health (NIH). DMPonline carries the equivalent depth for UK and European funders, including UKRI’s constituent research councils and Wellcome Trust. Because both draw on the same codebase, a plan exported from either tool follows a broadly comparable data model — the divergence sits in which templates, guidance text and institutional branding are pre-loaded, not in the software itself.

    What is Argos, and how does it differ?

    Argos is a DMP-writing platform developed within OpenAIRE, the European open-science infrastructure, rather than from the DMP Roadmap lineage. Argos was designed from the outset around machine-actionable output, producing plans as structured objects intended to connect into the wider European research-information graph rather than sit as a standalone PDF.

    Its templates lean towards Horizon Europe and European Research Council (ERC) requirements, and its architecture emphasises linking a DMP’s contents — datasets, repositories, funders, organisations — to persistent identifiers already circulating in the OpenAIRE Research Graph. For a European-funded project embedded in that ecosystem, this integration is a genuine functional difference, not just a branding one.

    Funder-template coverage: which tool fits your funder

    Template coverage is usually the deciding factor, since a funder-specific template determines exactly which questions a plan must answer. The table below summarises where each platform’s template strength lies.

    Platform Steward Strongest funder coverage Typical user base
    DMPonline Digital Curation Centre UKRI councils, Wellcome Trust, UK institutional templates UK and European universities
    DMPTool California Digital Library NSF, NIH, US federal agency templates US universities and research institutes
    Argos OpenAIRE Horizon Europe, ERC, EOSC-aligned funders European open-science projects

    None of the three restricts researchers to their “home” funder templates — DMPonline hosts non-UK institutional templates, and DMPTool lists non-US funders too — but the depth of guidance and the freshness of template maintenance concentrate where each tool’s steward organisation has direct funder relationships.

    Institutional branding and API export compared

    Beyond templates, two practical factors distinguish the tools for an institution deciding which one to adopt.

    • Institutional branding. Both DMPonline and DMPTool support institution-specific branded sub-sites — a university can present its own logo, guidance text and curated template list under its own subdomain while the underlying platform remains centrally maintained. Argos, built for the OpenAIRE/EOSC ecosystem, is more typically deployed as a shared service with organisation profiles rather than fully white-labelled institutional instances.
    • API and machine-actionable export. All three platforms are converging on the RDA DMP Common Standard, developed by the Research Data Alliance’s working group on machine-actionable DMPs, which defines a shared JSON structure for exporting plan content. This is what allows a plan written in one tool to be read, in principle, by a funder system, a repository, or a research-information system rather than only by a human reader.

    For research administrators evaluating tools as part of broader research administration workflows, the practical question is less “which tool is best” and more “which tool’s export format and branding options integrate with our existing repository, CRIS and grants-management systems”.

    Common questions about choosing a DMP tool

    Do I need a data management plan?

    Most major funders — including UKRI, Wellcome Trust, the NSF, the NIH and Horizon Europe — require a data management plan as a condition of funding. If your grant application names one of these funders, you need a DMP, and using DMPonline, DMPTool or Argos is the fastest route to a compliant one.

    How do I write a data management plan?

    Writing a DMP means working through a funder-specific template — covering what data you will create, how it will be documented, where it will be stored, and how it will be shared or preserved. DMPonline, DMPTool and Argos each provide the relevant template with embedded guidance, rather than requiring you to draft one from a blank page.

    What is included in a data management plan?

    A DMP typically covers the types of data to be produced, the metadata and documentation standards used, access and sharing policies, and the plan for long-term archiving and preservation. Machine-actionable tools structure these elements so they can be exported and reused by other systems, not just read once.

    Choosing a tool: what the decision actually hinges on

    Because DMPonline, DMPTool and Argos are all converging on the same RDA DMP Common Standard for export, the choice between them is rarely a compatibility question. It comes down to fit: which platform already carries deep templates for your funder, whether your institution operates a branded instance you are expected to use, and whether your downstream systems consume RDA-conformant JSON export.

    For a UK or European researcher working with UKRI or Wellcome funding, DMPonline is the default starting point. For a US researcher working with NSF or NIH funding, DMPTool serves the equivalent role. For a Horizon Europe or ERC-funded project deeply embedded in the EOSC ecosystem, Argos’s machine-actionability and graph integration make it the stronger fit. As the RDA Common Standard matures further, expect the practical differences between the three to narrow to templates and branding alone, with export interoperability becoming a solved problem rather than a selection criterion.

  • 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.

  • 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.

  • Clinical Data Management Plan vs Research Data Management Plan: What’s the Difference

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    A clinical data management plan and a research data management plan are two of the most frequently conflated documents in the clinical trial lifecycle. Both use the acronym “DMP” in casual conversation, both get drafted before a study starts, and both concern “data” in the broadest sense — but they answer to different masters, cover different lifecycle stages, and are read by different audiences. Submitting the wrong one to the wrong reviewer is a recurring, avoidable compliance headache for trial units and research offices alike.

    What Is a Clinical Data Management Plan?

    A Clinical Data Management Plan (CDMP) is an operational, trial-specific document that describes exactly how data will move from case report form (CRF) to locked database. It is written by or with the clinical data management (CDM) function — not the principal investigator’s grants office — and it sits alongside the protocol as one of the working documents that Good Clinical Practice (GCP), per ICH E6, expects a sponsor to maintain and be able to produce on inspection.

    A CDMP typically specifies:

    • CRF or eCRF design and the electronic data capture (EDC) system to be used
    • Database build, edit-check specifications and data validation rules
    • Data entry conventions (single vs double entry, query turnaround)
    • Medical coding dictionaries and versions, such as MedDRA and the WHO Drug Dictionary
    • Discrepancy management and serious adverse event reconciliation procedures
    • Roles, responsibilities and sign-off authority for database lock

    Because it is inspected against GCP, a CDMP is a living, version-controlled document updated through the study rather than filed once and forgotten.

    What Is a Research Data Management Plan?

    A Research Data Management Plan (RDMP) is a funder- or institution-facing document submitted at the grant proposal stage, well before a trial’s CDMP would even exist. Its job is compliance with funder and institutional data policy, not trial operations. UK Research and Innovation (UKRI) requires a data management plan for relevant grant applications, Horizon Europe applicants complete one through the Data Management Plan template built into the Horizon Europe Programme Guide, and the NIH Data Management and Sharing (DMS) Policy has required a DMS plan for NIH-funded research since January 2023.

    An RDMP typically covers:

    • What data types and volumes the project will generate or reuse
    • How data will be described, documented and made findable (metadata, identifiers)
    • Storage, security and access-control arrangements during the project
    • Ethical, consent and legal constraints on sharing (particularly for identifiable participant data)
    • Long-term preservation and repository plans, often with a DOI issued via DataCite
    • Alignment with the FAIR principles — Findable, Accessible, Interoperable, Reusable

    Unlike a CDMP, an RDMP is reviewed once (or at defined milestones) by a funder or research office, not audited line-by-line by a regulator during a GCP inspection.

    CDMP vs RDMP: Side-by-Side Comparison

    The table below sets out where the two documents genuinely diverge, so institutions running funded clinical trials know they usually need both — not one instead of the other.

    Dimension Clinical Data Management Plan (CDMP) Research Data Management Plan (RDMP)
    Primary purpose Ensure trial data is accurate, complete and audit-ready for database lock Satisfy funder/institutional policy on data stewardship and sharing
    Governing framework ICH E6 Good Clinical Practice; sponsor/CRO SOPs Funder mandates (UKRI, NIH, Horizon Europe); institutional RDM policy
    Typical author Data manager / clinical data management lead Principal investigator, often with library or research office support
    Created at Study set-up, before first patient enrolled Grant proposal stage, before funding is awarded
    Primary audience CDM team, biostatisticians, sponsor, regulatory inspectors Funder, ethics/IRB reviewers, institutional research office
    Content focus CRF design, edit checks, coding, database lock procedures Data description, storage, ethics, sharing, long-term preservation
    Review cadence Continuously updated through study conduct; inspected on audit Reviewed at proposal and, for some funders, at defined milestones

    Common Questions Answered

    What does a clinical data management plan include?

    A clinical data management plan includes CRF or eCRF specification, database design, data entry and validation procedures, edit-check logic, medical coding dictionaries such as MedDRA, discrepancy and adverse-event reconciliation processes, and clearly defined roles and responsibilities through to database lock, all maintained as a living, version-controlled document inspected under Good Clinical Practice.

    What should a data management plan include?

    A funder-facing research data management plan should describe the data types and volumes a project will generate, how data will be documented and made findable through metadata, storage and security arrangements, ethical and consent constraints on sharing identifiable data, and the eventual repository and preservation route, typically aligned to the FAIR data principles.

    What are the three phases of clinical data management?

    Clinical data management is generally organised into three sequential phases: study set-up, covering database build and CRF design; study conduct, covering data entry, cleaning and query resolution; and study close-out, covering final reconciliation, coding sign-off and database lock ahead of statistical analysis.

    Why the Distinction Matters for Research Administrators

    Institutions running externally funded clinical trials almost always need both documents, produced by different teams on different timelines. A funder reviewer looking for a FAIR-aligned sharing and preservation strategy will not find it in a CDMP’s edit-check specification — and a GCP inspector auditing database lock will not accept an RDMP’s high-level data-sharing statement as evidence of query resolution procedure.

    This is precisely the coordination gap that research administration functions increasingly exist to close: aligning the pre-award compliance document (the RDMP, owned by the grants office) with the operational trial document (the CDMP, owned by clinical data management) so that neither is quietly missing when a funder audit or a regulatory inspection arrives. Institutions that treat the two as interchangeable risk both funder non-compliance and GCP findings — for two entirely separate reasons.

    Consistent terminology helps here. Reviewers, auditors and research offices benefit from a shared reference for what each document is called and what it covers; the CASRAI research administration dictionary maintains definitions for terms that span exactly this pre-award-to-conduct boundary.

    Looking Ahead

    The line between the two documents is not static. ICH’s ongoing revision of E6 Good Clinical Practice has pushed sponsors toward more explicit, risk-based data governance language inside the CDMP itself, while funders such as UKRI and the NIH continue to tighten expectations for FAIR-aligned sharing inside the RDMP. Institutions that keep the two plans distinct — but explicitly cross-referenced — will be best placed to satisfy both regulators and funders as each side’s requirements keep evolving.