Data management plan (DMP) template

Purpose & instructions

This living data management plan (DMP) helps you document how research data will be created, stored, described, shared, preserved, and governed so it remains findable, accessible, interoperable, and reusable (FAIR). Fill each section with the information applicable to your project. Keep the plan brief but specific—use links to repository records, persistent identifiers (DOIs), or policy pages where possible.

Suggested use: complete the template early, update after major milestones, and attach or link to dataset records before publication or deposit.

1 — Project & contact information

Project title: [Enter project title]
Principal investigator / Data steward: [Name, role, email]
Project start / end dates: [YYYY-MM-DD — YYYY-MM-DD]
Version / DMP last updated: [version, date]

2 — Dataset inventory (for each dataset)

List each dataset separately. For each dataset provide the following information (use one bullet block per dataset):

1. Dataset name / short description — what it contains and purpose.
2. Data types & formats — e.g., CSV, FASTQ, TIFF, NetCDF, XLSX, JSON.
3. Estimated size — e.g., 200 GB raw, 5 GB processed.
4. Sensitivity / classification — public / restricted / controlled (contains personal data, commercial IP, or hazardous materials info).
5. Expected repository or storage location — institutional server, cloud bucket, subject repository (name + URL).
6. Planned persistent identifiers — DOI, accession numbers, ORCID links for creators.
7. Related code / protocols / notebooks — locations and licenses.

Example entry: Dataset: Field sensor readings — CSV (timestamp, temp, humidity); size 30 GB; classification: public after QA; repository: Zenodo (pending DOI); related code: GitHub repo / DOI.

3 — Metadata & standards

Which metadata schema(s) will you use? Explain how you will apply them and where metadata records will be stored.

• Chosen schemas/standards: e.g., Dublin Core, DataCite, DDI, MIxS, MIAME, domain-specific ontologies.
• File-level metadata: naming conventions, required fields, timestamps, version numbers.
• Controlled vocabularies / ontologies you plan to use.
• Example metadata record (short): title, creator, description, date, format, license, identifier, keywords.

4 — Storage, backup & access controls

Describe where active data will be stored, how backups are performed, and who can access which data.

• Primary storage location and responsible team.
• Backup frequency & method (e.g., daily incremental to institutional backup; weekly snapshot to cloud).
• Access control model: who has read/write/admin rights; authentication method (institutional SSO, VPN).
• Encryption at rest/in transit if required.
• Retention of intermediate/temporary files and versioning approach.

5 — Data quality & curation processes

Explain procedures that ensure data quality, provenance tracking, and curation for reuse.

• Quality checks (validation scripts, manual review, calibration logs).
• File naming conventions and directory structure.
• Provenance capture: how versions and processing steps are recorded (e.g., workflows, notebooks with time-stamped outputs).
• Long-term curation tasks (format migration plans, checksums, integrity audits).

6 — FAIR & reuse considerations

State how the datasets will meet FAIR principles and what remains out of scope.

• Findable: planned metadata and identifiers (DOIs).
• Accessible: repository access model and any embargoes or restrictions.
• Interoperable: file formats and vocabularies used to maximize interoperability.
• Reusable: license, provenance, and sufficient documentation (README, methods, codebooks).

7 — Sharing, licensing & publishing

Describe how and when data will be shared, what licenses apply, and any embargoes.

• Preferred license(s): e.g., CC0, CC-BY, CC-BY-SA, custom data use agreement.
• Sharing timeline: immediate on publication, after embargo, or restricted.
• Mechanism for sharing: repository name(s), accession process, API access.
• Citation guidance you'll provide to users of your data.

8 — Ethical, legal & privacy considerations

Note human subject data, consent terms, anonymization/de-identification, and legal constraints.

• Does data include personal or sensitive information? Yes / No
• If yes: consent scope, de-identification methods, controlled access procedures.
• Relevant approvals: ethics board reference numbers, data protection impact assessments.

9 — Retention, archive & preservation

State retention periods, chosen archival repository, and preservation actions.

• Retention period: e.g., 5 years, 10 years, indefinite—justify if shorter or longer.
• Archival repository: (name, URL), planned deposit date or trigger.
• Preservation actions: format migration, checksum monitoring, metadata refresh schedule.

10 — Roles, responsibilities & resources

Define who will do what and what resources are allocated.

• Data steward / maintainer: name and contact.
• Curator(s) and responsibilities: metadata, QC, repository submission.
• Budget or cost estimates for storage, curation, and repository fees.

11 — Funder & publisher requirements

List any specific funder or publisher DMP expectations and where they are addressed in this plan.

• Funder name and policy reference.
• Specific compliance items and where to find evidence (e.g., repository links, ethics approvals).

12 — Review schedule & change log

How often the DMP will be reviewed and a short change log.

• Review frequency: e.g., annually / on project milestone / before publication.
• Change log: Date — change summary — author.

13 — Final checklist (quick confirmation)

• Dataset inventory completed for each dataset.
• Metadata standards and schema selected and documented.
• Storage & backup plan documented and tested.
• Data sensitivity and access controls defined.
• License and repository choices recorded.
• Responsible contacts and review schedule assigned.

Sign-off: [Name, role, date]

Notes & examples

Keep answers concise and link to fuller artifacts (repository records, README files, code repositories). Consider producing a short README for each dataset that contains file-level descriptions, example commands to load data, and a small example script that reproduces a key figure.