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
- Physical sciences: instrument and sensor data
- Life sciences: biological and clinical data
- Social sciences: qualitative and survey data
- Frequently asked questions
- What this means for research administrators
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.
