FAIR data readiness checklist
Use this checklist to carry out an initial review of a research dataset before preparing it for repository deposit, FAIR assessment or publication.
The checklist helps identify missing documentation, metadata, identifiers, access information, file organisation and reuse conditions. It is intended as a practical readiness review rather than a formal FAIR certification.
Resource information
Resource type
Initial FAIR readiness checklist
Intended users
Researchers, research groups, data curators and data stewards
Recommended use
Early data review, FAIR planning, consultation and preparation for publication
Status
Recommended self-assessment checklist of the Competence Center
Download the checklist
Use the XLSX version to record answers, comments, responsible persons and corrective actions. The DOCX version is suitable for review and discussion within a research group.
Available formats
What does the checklist assess?
The checklist reviews whether the dataset can be identified, understood, accessed under clearly stated conditions, processed by other systems and reused by researchers beyond the original project.
Readiness is not the same as full FAIR compliance
A positive readiness review indicates that the principal information and organisational arrangements are available. A detailed FAIR assessment may still require repository-level, metadata-level and machine-actionability checks.
Main assessment areas
The checklist first reviews basic dataset management and documentation prerequisites. It then assesses readiness across the four FAIR dimensions: Findable, Accessible, Interoperable and Reusable.
Basic dataset readiness
Responsibility, file organisation, documentation, file inventory and quality-control information are reviewed as practical prerequisites for FAIR preparation. They are not an additional FAIR principle.
Findable
Metadata, identifiers, keywords, version information and repository discovery.
Accessible
Access conditions, authentication, embargoes and continued availability of metadata.
Interoperable
File formats, metadata structures, vocabularies, units and relationships.
Reusable
Licence, provenance, methods, quality information and sufficient reuse documentation.
Detailed readiness checklist
Review each criterion against the actual dataset and available documentation. Record unresolved issues in the downloadable checklist.
| Area | Readiness criterion | Why it matters |
|---|---|---|
| Basic dataset readiness | A responsible person or research group has been identified. | Responsibility is needed for decisions, updates and publication. |
| The dataset has a clear title and defined scientific purpose. | Users must be able to identify the object and understand why it was created. | |
| Files are organised using a consistent folder structure and naming convention. | Consistent organisation reduces ambiguity and supports processing. | |
| A current README or equivalent documentation is available. | Documentation explains the content, methods and use of the dataset. | |
| The principal files and formats have been inventoried. | A file inventory supports packaging, verification and preservation. | |
| Data quality checks and known limitations have been documented. | Users need evidence about accuracy, completeness and uncertainty. | |
| Findable | A structured metadata record can be prepared for the dataset. | Structured metadata enables search, indexing and discovery. |
| Creators and contributors can be identified consistently. | Clear attribution supports citation and responsibility. | |
| ORCID identifiers are available for creators where possible. | Persistent person identifiers reduce ambiguity. | |
| Institutional affiliations can be identified, preferably with persistent identifiers. | Organisation identifiers support reliable attribution and aggregation. | |
| Keywords and a scientific domain have been selected. | Subject terms improve discovery and classification. | |
| A suitable repository capable of assigning a persistent identifier has been identified. | A DOI or another persistent identifier provides a stable reference. | |
| Accessible | The intended access category has been defined. | The dataset should be clearly marked as open, restricted, embargoed or closed. |
| Any embargo, registration or approval requirement has been documented. | Users need clear instructions for obtaining access. | |
| Legal, ethical, contractual and confidentiality restrictions have been reviewed. | Access must comply with applicable obligations. | |
| Public metadata can remain available even when files are restricted. | Restricted data should remain discoverable where appropriate. | |
| A contact point for access or support has been identified. | Users need a reliable route for clarification or access requests. | |
| Interoperable | Open, documented or commonly used file formats are used where possible. | Widely supported formats improve technical reuse. |
| Proprietary formats are accompanied by export or conversion options where possible. | Alternative formats reduce dependence on specific software. | |
| Units, parameters, variables and codes are clearly defined. | Values cannot be interpreted reliably without their meaning and units. | |
| Controlled vocabularies or recognised terminology are used where relevant. | Shared terminology improves semantic interoperability. | |
| Relationships to publications, software, projects and other datasets can be recorded. | Linked research outputs provide scientific and provenance context. | |
| Metadata can be represented in a structured or machine-readable form. | Machine-readable records support exchange and automated processing. | |
| Reusable | A licence or rights statement can be assigned to the dataset. | Users must know what forms of reuse are permitted. |
| The rights holder and authority to publish the data have been confirmed. | A licence should be assigned only by an authorised rights holder. | |
| Methods of data creation, collection or processing are documented. | Methods are needed to interpret and reproduce the data. | |
| Software, instruments, models or workflows used to create the data are identified. | Technical provenance supports interpretation and reproducibility. | |
| Input, intermediate and derived data can be distinguished. | Processing levels and derivations should remain transparent. | |
| Provenance and relationships between principal files are documented. | Users need to understand how results were produced. | |
| Sufficient documentation is available for a researcher outside the original team. | Reuse should not depend entirely on undocumented knowledge. |
How to record the result
Use the same response categories throughout the checklist.
Yes
The criterion has been fully satisfied and supporting evidence is available.
Partly
The criterion has been addressed, but additional work is required.
No
The criterion has not yet been addressed.
Not applicable
The criterion does not apply to the dataset or its publication context.
Needs clarification
Methodological, legal or technical advice is required before answering.
How to use the checklist
1. Define scope
Identify the dataset, responsible persons and planned publication or preservation outcome.
2. Review
Compare each criterion with the actual files, metadata and documentation.
3. Record gaps
Document missing information, unresolved decisions and required corrections.
4. Assign actions
Assign a responsible person and target date to each unresolved issue.
5. Recheck
Repeat the review before forming the final package or depositing the dataset.
Interpreting the result
Interpret the checklist as an action-oriented readiness review rather than a formal numerical FAIR score.
Ready to proceed
Core documentation, metadata, access and reuse decisions are available. Proceed to package preparation.
Minor gaps
Several criteria are partly satisfied, but the missing information can be completed during packaging.
Major gaps
Important documentation, rights, access or metadata decisions remain unresolved.
Consultation required
Legal, ethical, technical or methodological issues require specialist advice.
Next step: prepare the FAIR data package
When the principal readiness issues have been resolved, organise the files, prepare the README, create the manifest, complete the metadata record and apply the FAIR package pre-check checklist.
Important notes
- Apply the checklist to a clearly defined dataset or data package.
- Record evidence or comments rather than marking criteria without verification.
- Do not treat every criterion as having the same importance.
- Resolve legal, ethical and confidentiality questions before public deposit.
- A restricted dataset may still satisfy many FAIR-related criteria when its metadata and access procedure are clearly documented.
- Do not include passwords, access credentials or confidential technical information in the checklist.
- Review the checklist again when the dataset, licence, access conditions or publication plan changes.
- Use repository-specific requirements in addition to this general readiness review.