Data Lifecycle Plan & FAIR Checklist

Purpose & how to use this template

This template helps project teams capture enough information about their data to make it findable, accessible, interoperable, and reusable (FAIR) over time. Fill the sections with project-specific values, use the example rows and patterns as starting points, and assign owners and dates so actions are trackable. Use the FAIR maturity checklist near the end to identify gaps and prioritize improvements.

1) Project overview

• Project name:
• Project ID / grant number:
• Principal investigator / lead: Name, contact
• Data manager / steward: Name, contact
• Start / projected end dates:
• Repository / publication target (if known): e.g., Zenodo, institutional repository

2) Data inventory (recommended table)

List each dataset, file collection, instrument output, or derived data product as a separate row. Be concrete about formats and locations.

3) File formats & naming conventions

Choose durable, well-documented formats. Prefer open, non-proprietary formats for archival copies (CSV, TIFF, NetCDF, JSON, Parquet). Record both working and archival formats.

Recommended filename pattern: <project>_<datasetID>_<YYYYMMDD>_v<major.minor>.<ext>
Example: projectA_DS-001_20240715_v1.0.csv

4) Metadata: required & recommended fields

At minimum, capture these fields for each dataset:

• Title
• Dataset ID
• Creator(s) and affiliations
• Contact / custodian
• Abstract / description
• Keywords / controlled vocabulary terms
• Date(s) collected / generated
• Version
• License or terms of use
• Provenance / methods summary
• Related publications / DOIs
• Sensitivity / access restrictions

Consider using established metadata schemas (DataCite, Dublin Core, schema.org, discipline-specific standards) so automated discovery and repository ingestion are simpler.

5) Storage, backup & integrity

• Primary storage location and responsible team
• Backup policy: frequency (daily/weekly), backup location (offsite/cloud), retention periods
• Integrity checks: record when checksums (MD5/SHA256) are created and validated
• Encryption requirements for sensitive data (at rest / in transit)

6) Access controls & governance

Define roles and responsibilities:

• Owner (decision authority for dataset)
• Steward / data manager (operational caretaker)
• Users (who can read/use) and editors (who can modify)

Document access mechanisms (institutional SSO, API tokens, repository access), approval workflows, and Data Use Agreement templates if needed.

7) Provenance & versioning

Adopt a versioning scheme (semantic versioning vMAJOR.MINOR) and keep machine-readable provenance records where possible (which script produced this file, software versions, parameters). Link datasets to code, notebooks, and lab notebooks.

8) Legal, ethical & licensing

• Consent constraints (if human subjects): summary and links to consent forms
• IRB/ethics approvals and identifiers
• Export control or other legal restrictions
• Recommended license(s) for reuse (e.g., CC0, CC-BY, CC-BY-NC)

9) Retention, archival & disposal

Specify retention periods for each dataset, archival destination (institutional repository, subject repository), and disposal criteria and approval process.

10) FAIR maturity checklist (scored)

Score each item 0 = no evidence, 1 = partial, 2 = fully implemented. Add owner and actions for items scoring 0–1.

1. Findable
   • Unique persistent identifier for dataset (DOI/Handle) — score:
   • Rich metadata indexed in a searchable repository — score:
   • Metadata includes keywords and domain vocabularies — score:
2. Accessible
   • Clear access protocol and conditions documented — score:
   • Authentication/authorization described where needed — score:
3. Interoperable
   • Use of community standards and vocabularies — score:
   • Machine-readable metadata and formats — score:
4. Reusable
   • License and provenance clearly stated — score:
   • Methodology, code, and quality metrics provided — score:

Interpretation: add the scores and divide by the maximum to get a percent maturity. Prioritize actions on items scoring 0 or 1.

11) Implementation actions & timeline

12) Quick start checklist (first 48 hours)

• Record data inventory rows for all active datasets
• Assign owners and a data steward
• Decide archival target and licensing strategy
• Create checksums for critical files and store them with metadata
• Run FAIR checklist and identify top 3 remediation tasks

13) References & resources

• FAIR Principles — Wilkinson et al., 2016
• DataCite metadata schema
• Discipline-specific repositories (e.g., GenBank, Zenodo, Dryad)
• Institutional data management policy

Notes: Adapt fields and the checklist to your discipline and institutional policies. Keep this template with the project documentation so it remains discoverable and editable during project handovers.