Findable
Make your data (and metadata) findable by ensuring it:
- Has a globally unique, persistent identifier (PID) (e.g., a DOI)
- Has rich, machine-readable discovery (citation/descriptive) metadata (e.g., title, creators, abstract, keywords, dates, methods, license)
- Is registered/indexed in a searchable resource (typically a repository/catalog) so it is discoverable by people and machines
Persistent identifiers (PIDs) are important because they unambiguously identify your data and support reliable citation and linking across systems. A common PID for datasets is a Digital Object Identifier (DOI). Choose a repository that mints/registers PIDs and exposes the record through a stable landing page (e.g., Zenodo registers a DOI for uploads via DataCite).
The metadata describing your data supports findability, citation, and reuse, and, critically supportsmachine-actionability (so services can index, link, and reuse your records at scale). Follow community metadata standards where available, or widely used cross-domain standards (e.g., Dublin Core, DCC/DataCite Metadata Schema), and prefer standards and vocabularies that are maintained and widely implemented by repositories.
To identify appropriate standards and vocabularies, consult:
DCC metadata guidance (disciplinary and general metadata resources)
Accessible
Make your data accessible by ensuring it:
- Is retrievable by its identifier using standardized, open protocols (e.g., HTTPS; APIs where relevant)
- Supports authentication/authorization where necessary (for sensitive, embargoed, or restricted data)
- Keeps metadata publicly accessible even if the data are restricted or no longer available
Remember: not all data must be open to be FAIR. Data can be restricted and still be FAIR, as long asthe metadata are openly accessible and the access conditions are described clearly.
This aligns with the European Commission’s principle: “as open as possible, as closed as necessary.”
As Open as Possible, As Closed as Necessary
Where can I keep my data (for the long term)?
Not necessarily “open to everyone,” but safe, preservable, and persistently accessible. Look for a repository that:
- Preserves data for the long term (including format-risk and preservation planning)
- Makes (meta)data findable (searchable record pages; indexing/harvesting)
- Supports rich, standardized, machine-readable metadata
- Captures access conditions and reuse terms (license) in the record metadata
You can deposit data to a general repository (e.g., Zenodo, Dataverse installations) or a subject-specific repository (e.g., Dryad, domain repositories). Prefer discipline repositories when they exist, because they often enforce domain standards and community metadata.
To identify suitable repositories for your discipline, search:
- re3data (registry of research data repositories; searchable by discipline and features)
- FAIRsharing (also indexes repositories and which standards/policies they align with)
Interoperable
Make your data interoperable by using:
- Open, documented file formats (avoid proprietary-only formats where feasible)
- Community-agreed schemas/standards and machine-readable structures (so tools can parse and integrate your data)
- Controlled vocabularies, thesauri, and ontologies for consistent meaning (semantics) across systems
- Qualified links to related entities via PIDs (e.g., link dataset DOI ↔ article DOI ↔ ORCID iD; include funder/organization IDs where applicable)
Interoperable data can be integrated with other data, applications, and workflows. Think about not creating data that can only be read in proprietary software, and when you must use proprietary tools export/share an interoperable version (e.g., CSV/TSV plus a data dictionary; open exchange formats) so others can reuse it without specialized software.
Reusable
Make your data reusable by ensuring it:
- Is well-documented (so others—and your future self—can interpret it correctly)
- Has clear reuse terms via a license, recorded in the metadata
- Includes provenance and context (how data were generated/processed; versions; assumptions)
- Meets domain-relevant community standards where they exist
Create documentation, e.g., a README (plain text or Markdown preferred for machine-use; PDF only if formatting is essential) that includes:
- For each filename: what it contains and how it relates to figures/tables/publications
- For tabular data: definitions of columns/rows, codes (incl. missing values), and units
- Processing/cleaning steps that affect interpretation
- Links to related datasets stored elsewhere (with PIDs where possible)
- Contact point (and ideally ORCID iD) for questions
Source (README guidance): Dryad’s “Creating a README.”
Data should have a clear license to govern the terms of reuse. If reuse is intended, avoid “no license” (which creates legal ambiguity and often blocks reuse). Practical guidance from the DCC can help you choose an appropriate data license and understand trade-offs. You can also check this practical guide on licensing research data.
Where possible, to maximize reuse, consider widely recognized licenses such as CC BY 4.0 (attribution) or CC0 (public domain dedication/waiver), and document any restrictions in both the metadata and DMP.
Check out: EUDAT’s License Selector wizard to support consistent license choice, especially when balancing derivatives, share-alike, and commercial reuse questions.
Find out more on HE online manual
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