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A data-use agreement (DUA) is the operating rulebook for a biomedical collaboration that uses data another organization controls. It defines the approved research question, names who may access the data, sets privacy and security duties, limits onward sharing and re-identification, and determines what happens to copies and derivatives when the project ends. The exact obligations depend on the repository, funder, consent language, institutions, and jurisdiction; NIH’s Data Use Certification (DUC) Agreement is a current example for controlled-access human genomic data, not a universal DUA template.
What a DUA changes in a collaboration
A DUA turns a broad intention to “share data for research” into enforceable operating boundaries. Before anyone downloads a file, the parties agree on four basic questions: which data are covered, who can use them, what work they may perform, and which safeguards and restrictions apply. Those answers affect staffing, computing environments, transfer methods, publication plans, incident response, retention, and close-out.
The agreement usually sits alongside other controls rather than replacing them. A repository’s access conditions, an institutional review or privacy determination, a consent form, a funder policy, and applicable law can all impose additional duties. If those instruments conflict, the collaboration needs its contracts, privacy, research-compliance, and information-security offices to determine which requirement governs. A DUA is therefore operational governance, not just a signature page.
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Purpose is narrower than a general research interest
Most agreements tie use to a defined protocol, project description, disease area, population, or analysis. A team cannot assume that access granted for one question permits a later project using the same files. Some datasets also impose limits such as General Research Use, Health/Medical/Biomedical Research only, a particular disease or ancestry scope, or a Collaboration Required condition. Read the dataset-specific language before adding an analysis that seems scientifically related.
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People, not just institutions, are approved
The agreement normally identifies a principal investigator (PI), approved users, and the organization responsible for their access. Staff who handle files, run pipelines, administer storage, or view outputs may need to be covered even if they are not authors. The requester and approved users are responsible for ensuring that associated research staff understand the terms and follow the required data-use practices.
How the NIH DUC example works
For people accessing data on or after January 25, 2025, NIH’s DUC Agreement sets terms for controlled-access datasets held in NIH-designated repositories under the NIH Genomic Data Sharing Policy, including datasets in dbGaP. It is an example of how a repository-specific DUA operates; it should not be treated as the form used by every biomedical repository or sponsor.
The Data Access Request defines the project
Access is connected to an approved Data Access Request (DAR) and its Research Use Statement. The request identifies the PI and approved personnel and explains the proposed work. The approved statement is the boundary of use: the requested data and derivatives must support that project, rather than an open-ended program of future questions.
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A dataset addendum supplies study-specific rules
The DUC’s dataset addendum identifies the dataset’s use limitations, the sponsoring NIH Institute or Center, the responsible Data Access Committee, a study description, and a suggested publication acknowledgement. Those details can change the practical meaning of access from one dataset to another, so a team should keep the addendum with its protocol and data inventory.
Privacy limits still apply to de-identified data
The NIH example prohibits attempts to identify or contact participants or their families, except where the agreement and applicable, approved research expressly allow it. “De-identified” does not mean risk-free or unrestricted. NIH privacy guidance advises institutions to consider protections for participant data even when the data meet technical or legal definitions of de-identification and could be shared without additional protections under a particular rule.
Operationally, that means analysts should not combine fields with outside information to infer identities, search for participants, or contact relatives. It also means the team should document who can see row-level data, how linkage keys are handled, and which outputs require disclosure review before release.
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External collaborators need their own approval
An external collaborator is someone outside the PI’s direct supervision, such as a person who is not part of the PI’s laboratory. For controlled-access NIH genomic data, that person must be independently approved. The fact that the PI’s institution has access does not automatically authorize a collaborator at another university, company, hospital, or government body.
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Secure transmission within the approved collaborative group is allowed when the collaboration is described in the approved request and each participant is an approved user through their home institution. Distribution to an unapproved individual or entity is otherwise restricted. Before sending a dataset, verify the recipient’s approval, institution, scope of work, and approved transfer channel; keep a record of that check.
What obligations a DUA creates for the whole team
Access and role management
- Maintain a current roster of approved users and remove access promptly when a person leaves the project, changes role, or no longer needs the data.
- Limit permissions to the minimum needed for the approved analysis; separate administrative access from analyst access where practical.
- Train staff on the use limitations, privacy prohibitions, secure handling, incident reporting, and publication requirements before they handle files.
- Require contractors, students, data managers, and cloud operators with access to follow the same restrictions when the agreement covers their work.
Security and transfers
Security duties can cover source files, working copies, backups, temporary pipeline files, and derived datasets. The agreement may specify controls for authentication, encryption, logging, storage location, vulnerability management, or cloud and third-party services. Do not assume that a secured primary server makes an unmanaged laptop, synchronization folder, or debugging export acceptable.
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Transfers should follow the approved method and destination. Record what was sent, to whom, when, for which approved purpose, and how receipt was confirmed. If a file is accidentally emailed, exposed through a misconfigured bucket, or accessed by the wrong person, follow the agreement’s incident process and notify the institutional contact identified for the project; do not wait to see whether misuse occurs.
Derivatives are part of the governance boundary
Derived data can include cleaned tables, genotypes, model features, intermediate files, linked datasets, and outputs that retain participant-level information. Where the agreement says so, these materials inherit the same security, sharing, and retention duties as the original files. A derivative is not automatically free of restrictions because it has a new filename or because some variables were transformed.
Publication and acknowledgement
Check the dataset addendum and agreement before submitting a manuscript, poster, preprint, software release, or public repository deposit. Use the suggested acknowledgement when required, follow any reporting or result-notification terms, and ensure that tables, figures, and code do not reveal prohibited information. Publication review is generally a compliance check, not a right for the data provider to rewrite scientific conclusions; the controlling agreement determines the actual process.
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A practical workflow for starting a DUA-covered project
- Identify the controlling instruments. Collect the DUA, repository terms, dataset addendum, DAR and Research Use Statement, consent or ethics conditions, sponsor policy, and institutional rules. Note which version and effective date apply.
- Map the permitted purpose. Translate the approved research statement into a short scope document listing datasets, populations, analyses, outputs, and prohibited activities such as re-identification or recontact.
- Build the approved-user roster. List the PI, analysts, clinicians, students, administrators, contractors, and external collaborators who may encounter the data. Obtain independent approval for people who require it, especially collaborators outside the PI’s supervision.
- Design the data environment. Decide where raw files, working data, derivatives, backups, and logs will reside. Apply the agreement’s access, encryption, authentication, monitoring, and cloud or third-party requirements before downloading.
- Set transfer and incident procedures. Specify the approved transfer channel, recipient verification, logging, escalation contacts, and the steps for containment, notification, investigation, and documentation if data are exposed or misused.
- Control outputs. Review exports, figures, row-level results, code, and linked data for privacy, re-identification, and use-scope issues. Confirm that collaborators and repositories receiving an output are permitted recipients.
- Track changes. A new dataset, collaborator, institution, analysis purpose, cloud service, or publication route may require an amendment, a new approval, or a new request. Do not rely on an informal email as a substitute unless the agreement explicitly permits that process.
How to compare two data-use agreements
When a collaboration involves different repositories or institutions, compare the operative clauses rather than assuming that both documents use “DUA” to mean the same thing.
| Comparison point | Questions to answer | Why it matters |
|---|---|---|
| Purpose and permitted use | Is use limited by project, disease, ancestry, population, or analysis? Are new questions allowed? | Determines whether a proposed aim fits the approval or requires a new request. |
| Approved users | Who qualifies, who approves them, and how are external collaborators added? | Institutional access does not automatically authorize every person on the team. |
| Onward sharing and derivatives | May files, code, features, linked data, or results be sent to another party? Do derivatives inherit restrictions? | Prevents an apparently harmless export from becoming an unauthorized disclosure. |
| Privacy, re-identification, and recontact | What attempts to identify or contact participants are prohibited or expressly permitted? | De-identification alone may not remove privacy obligations. |
| Security and cloud use | Which technical controls, locations, vendors, logs, and access reviews are required? | Storage and processing choices can create a breach even when the research purpose is proper. |
| Incidents and reporting | Who must be notified, how quickly, and what records must be retained? | Fast, documented response limits harm and demonstrates compliance. |
| Publication and acknowledgement | What acknowledgement, reporting, review, or result-sharing language is required? | Missing a required acknowledgement or disclosure can delay publication or violate terms. |
| Renewal and close-out | When does access expire? Must copies and derivatives be destroyed? What may be retained? | Projects often remain noncompliant after the final analysis if close-out is ignored. |
Close-out, retention, and renewal
Plan the end of the project before the first download. Under the NIH DUC example, close-out generally requires destroying copies, versions, and derivatives on local systems and cloud infrastructure. The agreement allows retention only of encrypted minimum data needed for institutional retention, legal, or scientific-transparency requirements. Retained material remains subject to the agreement and may be used only to support findings from the approved research; it cannot become a source for new questions without a new approved request.
Because retention language varies, create a close-out checklist that names every storage location, backup, export, notebook, and collaboration workspace. Obtain confirmation from each approved user, document destruction or permitted retention, and preserve only the records your institution requires. If a project will continue, check the renewal deadline early; an expired approval is not a safe basis for ongoing analysis.
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NIH states that updated security standards apply effective February 25, 2026, to users accessing NIH controlled-access repositories under new or renewed agreements. That date is not a blanket rule for every biomedical DUA. Users approved before February 25, 2026 follow the security standards in their existing agreement until close-out or renewal, unless NIH or the controlling agreement requires an earlier change. Teams should verify the live NIH requirements and their own agreement before modifying an environment or renewing access.
Common mistakes and how to correct them
- Calling the NIH DUC universal: Treat it as one repository and policy example; read the agreement that actually controls your dataset.
- Adding a collaborator informally: Pause the transfer, verify independent approval and institutional coverage, and amend or update the request when required.
- Assuming de-identification ends the duty of care: Keep controlled access, prohibit re-identification and recontact, and assess linkage risks.
- Ignoring derivatives and cloud copies: Inventory intermediate files, backups, model features, and exports; apply the same restrictions where the agreement does.
- Using data for a new question: Compare the proposed work with the approved Research Use Statement and obtain a new approval if it falls outside scope.
- Leaving close-out until deletion day: Start an inventory and retention decision early so encrypted legal or scientific records are preserved correctly and other copies are destroyed.
- Overlooking institutional review: Have contracts, privacy, security, and research-compliance specialists review unusual terms, cross-border work, third-party processing, or conflicts with local policy.
What a collaboration should document
A concise governance file should contain the signed agreement and addenda; the current DAR and Research Use Statement; approved-user and institution lists; training records; data-flow and storage diagrams; transfer logs; access reviews; incident records; publication acknowledgements; amendments and renewals; and close-out evidence. Keeping these records together makes it possible to answer who accessed what, for which approved purpose, under which controls, and what happened to the data afterward.
No single checklist resolves every jurisdictional or institutional issue. The controlling agreement and current repository requirements prevail, and the parties should obtain their institution’s contracts, privacy, and information-security advice when the terms or risk are unclear.
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