Choose a repository by working from binding requirements to practical fit: check your funder and project rules, look first for an organism-, discipline-, or data-type-specific repository, and confirm that its access model protects any restricted data. If no suitable specialist repository accepts your dataset, compare generalist repositories for stewardship, discoverability, and eligibility. The best choice is the one that satisfies the project’s obligations and lets the intended community find and reuse the data safely.
1. Check funder, award, and project requirements first
Before comparing repositories, review the funding opportunity, award conditions, institute or center policies, journal rules, data-sharing commitments, and any restrictions imposed by consent, contracts, law, or institutional and community governance. A specific repository requirement takes precedence over a general preference for a specialist or generalist service. For NIH-supported work, start with the NIH repository-selection guidance; NIH notes that some initiatives and funding opportunities specify repositories, and its repository inventory is under review.
NIH’s guidance applies to NIH-supported research, not to every funder or jurisdiction. Check the terms that govern your particular project rather than assuming another agency follows NIH policy.
2. Look for a repository that fits the biology
If the project does not name a repository, begin with options designed for the organism, biological discipline, or data type. A domain repository is more likely to use community-relevant metadata and curation practices that make the data easier to discover and interpret. NIH says, “Primary consideration should be given to data repositories that are discipline or data-type specific to support effective data discovery and reuse.”
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A specialist label alone does not make a repository suitable. Confirm that it accepts your particular data and submitter, supports the required access conditions, and does not conflict with your project’s rules. For NIH projects, the agency’s repository inventory is a useful place to identify candidates.
3. Decide what access the data can have
Classify the dataset before choosing a service: it may be openly available, released after an embargo, or shared only through controlled or mediated access. Restrictions can arise from participant consent, confidentiality, security, legal duties, or Tribal sovereignty and other community commitments. Open download is not automatically the right outcome for biological data.
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For human participant data
For data derived from people, including de-identified data, check that the repository can enforce the access conditions promised to participants and required by the IRB, institution, and applicable agreements. NIH’s privacy guidance for sharing human research data identifies safeguards to consider, including tiered access, user credentialing, review of requests, breach response, procedures for misuse, and download controls or auditing where downloads are allowed. Confirm that repository terms and controls match the consent and governance obligations for the dataset; some data may require controlled access or a secure enclave.
4. Compare whether each candidate can handle the deposit
Check operational fit directly with the repository, especially for large, complex, mixed-format, or sensitive data. NIDDK recommends reviewing format and metadata requirements early and confirming eligibility and accepted data types. Its Repository Selection Considerations Tool also helps organize the choice around requirements, domain fit, and controlled access.
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- Mandate and eligibility: Is the repository required or preferred by the funder, journal, institute, or project agreement? Does it accept both this data type and your submission?
- Scale and workflow: Can it accommodate dataset size and complexity, expected request volume, and practical upload and download needs? Does it require associated documentation or code?
- Timing and cost: What are the current fees, deposit turnaround, embargo or release options, and schedule? Confirm these details with the repository; official guidance does not provide a comprehensive current comparison across services.
- Metadata and formats: Are the required fields and accepted formats appropriate for the data and its users? Can the data and documentation be exported in common, usable formats?
- Access and reuse terms: Are licensing, user terms, request review, and any access controls clear and compatible with the project’s commitments?
5. Assess stewardship, discoverability, and preservation
A repository should do more than accept an upload. NIH’s desirable repository characteristics offer a useful checklist. Look for:
- A persistent identifier and a stable landing page that can be cited.
- Searchable, appropriate metadata and curation or quality assurance that support discovery and interpretation.
- Provenance records, integrity safeguards, and clear retention and preservation policies.
- Long-term sustainability and contingency arrangements if the service changes or closes.
- Transparent access and reuse terms, with security controls appropriate to the data.
These are selection criteria, not a certification that every repository meets each one. Assess what the candidate actually documents and provides for your dataset.
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6. Use a generalist repository if no specialist option fits
A generalist repository can be a sound choice when there is no suitable discipline- or data-type-specific option. NIH lists Dataverse, Dryad, Figshare, IEEE Dataport, Mendeley Data, Open Science Framework, Synapse, Vivli, and Zenodo as examples. The list is not exhaustive, and NIH explicitly says it does not recommend one generalist repository. Verify each service’s current eligibility, terms, capacity, preservation approach, and access controls before deciding. The NLM directory of generalist repositories described 111 directory records when last reviewed on August 5, 2026; that dated count is neither a quality ranking nor a guarantee that any listed service currently accepts a particular deposit.
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In a data management and sharing plan, identify the repository for each data type and briefly explain why it fits, including its access model, metadata, identifiers, preservation, and release timing. NIDDK advises including a short justification for each data type. For a complex, sensitive, unusually large, or mixed-format submission, contact the repository and consult institutional librarians or data managers before finalizing the plan.
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NIH sharing deadlines to check
For NIH applications with receipt dates on or after January 25, 2023, the NIH Data Management and Sharing Policy guidance says repository choice should account for data sensitivity, dataset size and complexity, and anticipated request volume. Under that policy, scientific data underlying peer-reviewed journal articles should be accessible no later than the article’s first publication. Data underlying findings not disseminated through peer-reviewed articles should generally be shared by the end of the performance period, or the extended performance period if a no-cost extension applies. Project-specific award terms, agreements, or other policies may require earlier sharing, so check the rules that apply to the award.
Quick Recap
A quick decision sequence
- Read the funding opportunity, award, institute, journal, consent, and governance requirements.
- Identify repositories specific to the organism, discipline, or data type, then confirm acceptance and eligibility.
- Determine whether the dataset can be open, embargoed, controlled, or otherwise restricted; for human data, verify consent-compatible safeguards.
- Compare scale, formats, metadata, identifiers, reuse terms, preservation, retention, cost, and timing against the project’s needs.
- If no specialist repository fits, evaluate generalist options and confirm current terms directly.
- Document why each selected repository fits each data type, and seek institutional or repository advice for edge cases.
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