CASRAI Dictionary

Tag: data management plan tools

  • 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.

  • Machine-Actionable Data Management Plans: What Changes

    Data management plans (DMPs) have traditionally been static, prose documents written once at proposal stage and rarely opened again. That is changing. Funders, repositories and institutional systems are converging on machine-actionable data management plans (maDMPs) — DMPs structured so that software, not just people, can read and act on them. The shift is being driven by the RDA DMP Common Standard, a specification from the Research Data Alliance that turns free-text plans into structured, exchangeable data. This article explains what “machine-actionable” means in practice, what the standard actually changes, which tools implement it, and why funders are pushing the sector in this direction.

    What “Machine-Actionable” Actually Means

    A conventional DMP is a Word document or PDF: a human writes prose describing what data will be collected, how it will be stored, and where it will end up. A reviewer reads it once, files it, and rarely revisits it. Nothing in that document can be queried, validated automatically, or passed to another system without someone re-typing it.

    A machine-actionable DMP replaces (or accompanies) that prose with structured fields — dataset descriptions, distribution details, metadata standards, licences, repository identifiers — encoded so that a repository, funder portal, or research information system (CRIS) can parse them directly. The foundational framing paper, Ten Principles for Machine-Actionable Data Management Plans (Miksa, Simms, Mietchen & Jones, PLOS Computational Biology, 2019, cited over 130 times), describes the goal as embedding DMPs in existing research workflows so parts of the plan can be generated, validated and updated automatically rather than retyped at every stage.

    • Structured, not free-text — fields for dataset type, format, volume, access conditions and repository are discrete and machine-parseable.
    • A living document — updated through the project lifecycle rather than filed once and forgotten.
    • Interoperable — exportable between DMP tools, repositories, CRIS platforms and funder systems without manual re-entry.
    • Partially automatable — some fields (e.g. ORCID iDs, grant metadata, repository policies) can be pre-filled from connected systems.

    Definitions of related research-data terms are catalogued in the CASRAI Dictionary.

    The RDA DMP Common Standard: What It Changes

    The RDA DMP Common Standard for Machine-actionable Data Management Plans, developed by an RDA working group, defines a shared JSON schema for representing a DMP’s core elements: project and funder metadata, one or more datasets, each dataset’s distribution (repository, licence, access level), and the metadata standards applied to it. The schema is published and version-controlled openly on GitHub, so any tool builder can implement it without licensing constraints.

    Before a common schema existed, each DMP tool stored plans in its own proprietary structure. A plan created in one system could not be meaningfully exported to another, and funders could not aggregate structured data across grant portfolios without manual extraction. The Common Standard changes that by giving every participating tool the same underlying data model, so a DMP authored in one platform can, in principle, be exported as valid maDMP JSON and ingested by another.

    This matters most at the points where a DMP currently has to be re-keyed: submitting to a funder portal, registering a dataset with a repository, and reporting compliance at project close. A structured, standard-conformant DMP removes several of those manual hand-offs.

    Which Tools Implement the Standard

    Three tools dominate current maDMP practice, each maintained by a different non-profit research-infrastructure organisation:

    Tool Maintaining organisation Primary user base maDMP support
    DMPonline Digital Curation Centre (DCC), University of Edinburgh UK and international institutions API and structured export aligned to the RDA Common Standard
    DMPTool California Digital Library (CDL/UC3) US universities and federal-grant researchers Templates mapped to funder requirements; RDA-aligned export in progress
    ARGOS OpenAIRE, originally built under the EU FAIRsFAIR project Horizon Europe and EOSC-affiliated researchers Native maDMP JSON, direct repository and metadata-standard linking

    DMPonline and DMPTool both originated as template-driven questionnaires aligned to specific funder wording, then layered structured export on top as the Common Standard matured. ARGOS was built later, directly on the RDA schema, as part of the EU-funded FAIRsFAIR (“Fostering FAIR Data Practices in Europe”) project, which is why it links more natively to repositories and metadata standards rather than treating them as free-text fields. Institutions choosing between them should check which one their funder or repository already exchanges data with, rather than assuming full interoperability across all three.

    Why Funders Are Moving in This Direction

    Funders adopted DMP requirements originally to make researchers think about data stewardship before, not after, the fact. Horizon Europe requires a DMP as a formal deliverable for data-generating projects, due within six months of the project start and updated at least at the mid-term and final reporting points — a recurring obligation that is far easier to track programmatically than by re-reading prose each time. The US National Institutes of Health introduced its Data Management and Sharing Policy in 2023, requiring a DMS plan for every funded project involving scientific data, which has pushed US institutions toward tools that can validate plans at scale rather than review them manually.

    For funders managing thousands of active grants, machine-actionable plans mean compliance can be checked computationally — flagging, for instance, a dataset with no named repository or an access licence inconsistent with funder policy — instead of requiring programme officers to re-read each document individually. For research administrators, the practical benefit is fewer duplicate data-entry tasks across grant systems, repositories and institutional CRIS platforms, and DMPs that can be audited at renewal or close-out without starting from scratch.

    Common Questions About Machine-Actionable DMPs

    What is a machine-actionable data management plan?

    A machine-actionable data management plan (maDMP) is a DMP whose content is structured — typically as JSON conforming to the RDA DMP Common Standard — so that repositories, funder systems and research information platforms can read, validate and act on it automatically, rather than relying on a human re-reading free-form prose.

    What should a data management plan include?

    A DMP typically describes the types and volume of data to be generated, metadata standards applied, storage and security arrangements, ethical and legal considerations, roles and responsibilities, and the data-sharing and long-term preservation plan, including the intended repository and access licence.

    Why is research data management important?

    Sound research data management improves the integrity, reproducibility and reuse value of research outputs. It ensures data remain findable and accessible after a project ends, satisfies funder and publisher mandates, and reduces the risk that valuable data become unusable or unrecoverable once the original team disperses.

    The direction of travel is clear: DMPs are moving from a one-off compliance document to structured metadata that persists and updates across a project’s life, feeding repositories, funder reporting and institutional systems without re-transcription. Institutions that adopt an RDA-aligned tool now — DMPonline, DMPTool or ARGOS — are better positioned as more funders begin to require, rather than merely accept, structured plans.