Preparing Data Management Plans for Veterinary Research

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-11

Key Takeaways

• A robust Data Management Plan (DMP) is scientifically imperative for veterinary research, ensuring data integrity, reproducibility, and compliance with reporting standards like ARRIVE and EQUATOR, by meticulously defining procedures for data collection, storage, documentation, sharing, and preservation.
• Veterinary research data's heterogeneity (clinical records, genomics, imaging, field observations) necessitates specific DMP considerations, including client confidentiality, owner consent, production animal traceability (intersecting with WOAH standards), and sensitive wildlife data management.
• Data ownership and governance are complex in veterinary research, requiring the DMP to clearly identify custodians, usage rights, and establish role-based access permissions and version control procedures to maintain data provenance and prevent unauthorized alterations.
• Secure, redundant storage architecture is critical, employing a tiered approach with encrypted, access-controlled primary, secondary, and archival copies, alongside meticulous documentation of file formats (prioritizing open standards like CSV or DICOM), backup verification, and timely access revocation for departing personnel.
• Data sharing must balance funder mandates and journal policies with ethical constraints, client consent, and potential embargo periods (typically 12-24 months post-data collection) to protect primary analysis timelines while ensuring eventual discoverability and reusability.
• Preservation requires defining a minimum preservable data package (raw data, processed data, metadata, analysis scripts) and selecting appropriate repositories, considering their preservation commitment, file format acceptance, and data citation mechanisms, with a clear pathway for format conversion to long-term stable formats.

Veterinary research generates heterogeneous data: clinical records, laboratory results, imaging files, genomic sequences, and field observations from production animals, companion animals, and wildlife. A data management plan (DMP) is the document that defines how those data will be handled during the research lifecycle and after the project ends. This article explains the components of a DMP for veterinary research, including storage, sharing, and preservation, and is written for veterinary researchers designing studies across species and settings. It answers the practical question of what a fundable, executable DMP must contain and how to tailor it to the realities of veterinary data.

The DMP serves two masters. It satisfies funder and institutional requirements, and it protects the scientific value of the data themselves. Poorly managed data are a leading cause of unreproducible research, and veterinary medicine has additional obligations: client confidentiality, animal welfare records, and, in production species, traceability requirements that may intersect with trade standards published by the World Organization for Animal Health in the WOAH terrestrial animal health code. A DMP written before data collection begins prevents costly reorganisation later and ensures that the data can answer the questions they were collected to address.

At a Glance

The Scientific Rationale for Data Management

Data management is not an administrative afterthought. It is a scientific method. The integrity of a veterinary study depends on the ability to trace every recorded value back to its source, to verify that measurements were taken under the conditions the protocol specifies, and to confirm that no data were altered, lost, or selectively excluded. A DMP operationalises these requirements by assigning responsibility, defining procedures, and establishing verification points.

The connection between data management and research quality is explicit in reporting standards. The ARRIVE guidelines, published by the NC3Rs, specify the minimum information required for transparent and reproducible animal research, including details of experimental design, sample size calculation, and data handling. The ARRIVE guidelines 2.0 for reporting animal research require authors to state how data were collected and analyzed, which presupposes that a management plan existed. Similarly, the EQUATOR Network reporting guidelines library provides study-type specific checklists, such as CONSORT for randomised trials and STROBE for observational studies, that include data-related items. A DMP aligned with these standards at the planning stage produces a manuscript that satisfies them at the publication stage.

Data Types and Sources in Veterinary Research

Veterinary data are more heterogeneous than those in most human clinical research. A single project may combine electronic health records from a teaching hospital, laboratory outputs from a diagnostic service, owner-reported outcome measures, and environmental samples from a farm. Each data type carries distinct management requirements.

Clinical data from practice records raise confidentiality obligations that vary by jurisdiction and by the relationship between the researcher and the practice. Owner consent for use of records in research must be documented, and the DMP should state how consent was obtained and how owners can withdraw data. Production animal data may be linked to herd health schemes, milk recording services, or slaughterhouse traceability systems, and these linkages create both opportunities for enrichment and risks of re-identification. Wildlife data may involve permits, endangered species restrictions, and sensitive location information that must not be publicly mapped.

The choice of animal model also affects data management. Studies using porcine models for translational research, such as the characterization of urethral tissue for lower urinary tract investigations described in a recent institutional publication on porcine urethra characterization, generate imaging data, histological sections, and immunohistochemical images that require standardized naming conventions and storage formats. The same principle applies across species: the DMP must anticipate the full range of data outputs before the first sample is collected.

Data Ownership and Governance

Ownership of veterinary research data is rarely simple. The institution employing the researcher, the funder, the clinical practice contributing records, and the client who owns the animal may all assert interests. The DMP should identify the data owner or custodian, the parties who hold usage rights, and the conditions under which data can be accessed by others.

Governance structures should name specific roles: a principal investigator with overall responsibility, a data steward who manages day-to-day operations, and, for larger projects, a data access committee. Access permissions should be defined at the level of individual files or datasets, not at the level of the whole project. Version control procedures must be specified so that analyzes can be reproduced from the exact data files used. For multi-site studies, data transfer agreements should be in place before any data move between institutions, and the DMP should reference those agreements instead of duplicate their terms.

Documentation and Metadata Standards

Documentation is what makes data interpretable by anyone other than the person who collected them. At minimum, the DMP should specify a codebook defining every variable, its units, its permitted values, and its coding scheme. Free-text fields in clinical records are notoriously difficult to analyze, and the DMP should state whether such fields will be standardized, mapped to controlled vocabularies, or analyzed qualitatively.

Metadata should be captured at the point of collection, not reconstructed later. Laboratory instruments should export files with embedded metadata where possible. Imaging studies should follow the naming and header conventions of the modality. For observational studies, the DMP should specify how field notes are recorded, whether they are digitised, and how they are linked to quantitative data. The level of documentation should be proportionate to the expected reuse value of the data, but a reasonable default is that a researcher outside the original team should be able to understand and analyze the data from the documentation alone.

Storage, Backup, and Security Architecture

A data management plan must specify where data reside during the active research phase and how those locations change over time. The choice of storage tier depends on data sensitivity, file size, access frequency, and the number of collaborators. For most veterinary studies, a three-tier structure works well: a primary working copy on a managed institutional server or cloud repository, a secondary copy on a different physical medium, and an archival copy destined for long-term preservation.

File format decisions belong in this section of the plan. Proprietary formats from imaging platforms, histology scanners, and laboratory analyzers often cannot be reopened without the vendor's software. The plan should identify which formats are native, which are convertible to open standards, and when conversion occurs. For image data, consider whether the raw file, a compressed derivative, or both will be retained. For tabular data, plain-text formats such as CSV or TSV with a documented encoding scheme outlast most spreadsheet applications.

Backup frequency should match the rate of data generation. A study collecting continuous physiological telemetry requires nightly or real-time replication, whereas a survey study may only need weekly snapshots. The plan should name the responsible person for each backup action and the verification method, such as checksum validation or test restores. A backup that has never been restored is a hypothesis, not a safeguard.

Access control lists must be defined at the project level, not left to individual file permissions. Each collaborator's role determines their access tier: read-only, read-write, or administrative. When a team member leaves the project, their access should be revoked on the date of departure, not at the next quarterly review. Multi-site studies require particular attention, because institutional firewalls and national data transfer regulations may restrict where data can be stored or processed.

Data Sharing Agreements and Embargoes

Sharing provisions in a data management plan address two distinct questions: who may access the data during the project, and who may access it after publication. The first question is governed by collaboration agreements and material transfer agreements. The second is governed by the data sharing section of the plan, which should state the intended repository, the license under which data are released, and any embargo period.

Embargoes are common in veterinary research because of the interval between data collection and primary publication. A typical embargo runs 12 to 24 months from the end of data collection, allowing the originating team to publish their primary analyzes before competitors or other groups access the data. The plan should specify the embargo trigger date, the event that ends the embargo, and the mechanism by which the repository enforces it.

Some funding bodies require immediate data access, while others permit delayed release. The plan should reconcile these requirements with the legitimate interests of the research team. Where a funder mandates immediate sharing, the plan can specify that metadata and documentation are released immediately while raw data files follow a staged release schedule. This approach preserves discoverability without surrendering analytical priority.

Data sharing agreements should also address secondary use restrictions. Some datasets contain information that cannot be fully anonymised, such as radiographs with identifiable microchip numbers or genetic sequences from rare breeds. The plan should state whether secondary users must obtain animal ethics approval for their own analyzes and whether the originating institution retains a review role. The ARRIVE guidelines for reporting animal research emphasize that methods sections should describe how data were handled, and the same transparency should extend to sharing conditions.

Preservation and Repository Selection

Preservation planning begins with the question of what future users will need. A raw dataset without its accompanying codebook, laboratory protocols, and instrument calibration files is often unusable. The plan should therefore define the minimum package that constitutes a preservable unit: raw data, processed data, metadata, analysis scripts, and a readme file describing the file structure.

Repository selection criteria include the repository's preservation commitment, its file format acceptance policy, its data citation mechanism, and its geographic jurisdiction. Institutional repositories offer continuity with the host institution but may lack specialised veterinary domain expertise. Subject-specific repositories, such as those maintained by veterinary or agricultural research consortia, offer better discoverability for discipline-specific datasets. Generalizt repositories accept any data type and often provide the most flexible licensing options.

The plan should specify the repository as a named entity, not a category. If the repository is uncertain at the planning stage, the plan should list candidate repositories with selection criteria and a decision date. The decision date should fall before data collection begins, because repository requirements can influence file naming conventions and metadata capture during the active phase.

Preservation formats differ from working formats. A working spreadsheet in Excel may be preserved as CSV with a separate documentation file describing formulas and data validation rules. A proprietary imaging format may be preserved alongside a DICOM or TIFF derivative. The plan should state the conversion pathway for each data type and the person responsible for verifying that converted files retain all information present in the original.

Data Management Plan Template

The following template provides a structured starting point for veterinary research projects. Each section should be completed with project-specific detail, generic statements such as "data will be stored securely" do not satisfy funder requirements or institutional review.

Compliance Checklist and Monitoring

A data management plan is a living document, not a filing requirement. The plan should include a compliance review schedule that aligns with project milestones: after the first month of data collection, at each annual progress report, and before any major data transfer or publication submission. The review should verify that the named storage locations exist, that backups are running, that metadata are current, and that access permissions match the current team roster.

The checklist below can be adapted to individual projects. Each item should be verified by a person other than the one responsible for the underlying task.

• Storage location matches the plan and is accessible to all authorised team members.
• Backup logs show successful completion within the stated frequency.
• Metadata records are updated within the interval specified in the plan.
• File naming conventions are followed across all team members and sites.
• Access permissions reflect the current team roster, with departed members removed.
• Data sharing agreements are signed and filed for all collaborating institutions.
• Repository selection decision has been made or is on schedule.
• Preservation format conversions have been tested on a sample of each data type.
• Budget lines for storage and repository fees are being spent as planned.

Species and production system differences affect several of these decisions. Wildlife studies involving endangered species may face additional restrictions on data release because of poaching or collection risks, and the plan should address these constraints explicitly. Production animal studies may involve commercially sensitive data owned by a producer or breed association, which changes the sharing terms. Clinical studies in referral hospitals must reconcile data sharing with client confidentiality obligations, and the plan should state how owner consent forms authorise or limit data release. The MSD Veterinary Manual and AVMA practice resources provide profession-specific guidance on confidentiality expectations that should inform these decisions.

Where international collaboration is involved, the WOAH terrestrial animal health standards may govern data related to notifiable diseases and trade-relevant health status. The plan should identify whether any data elements fall under such reporting obligations and how those obligations interact with publication embargoes. In some jurisdictions, disease reporting timelines take precedence over research publication schedules, and the plan should acknowledge this hierarchy instead of ignore it.

The compliance review should also verify that the plan itself remains accurate. Research projects change direction, and a plan written before data collection may describe procedures that no longer match practice. When the plan is revised, version control should track the changes and the date of each revision. Funders and ethics committees expect the plan that was approved to remain the operative document, so revisions should be communicated to those bodies where the changes affect the commitments made in the original application.

Recognized Failure Modes in Data Management Plans

Data management plans fail in predictable ways. The most common failure is treating the plan as a documentation exercise instead of a working protocol. A plan that is filed and never consulted will not survive the first unexpected event, whether that is a hard drive failure, a departing staff member, or a change in study protocol.

The second most common failure is underestimating the volume and variety of data that veterinary research generates. Clinical records, imaging files, laboratory outputs, and field observations each have different formats, sizes, and retention requirements. A plan that addresses only the primary dataset while ignoring supporting materials leaves critical context vulnerable. The ARRIVE guidelines for reporting animal research specify that publications must describe how data were handled, and reviewers increasingly check whether the plan matches the executed workflow.

The third failure mode is ambiguity about roles. When the plan does not name a specific individual responsible for each data task, tasks are deferred or duplicated. This is particularly damaging in multi-site studies where each site assumes another site is performing backups or version control.

Common Errors and Corrective Actions

Less experienced researchers frequently confuse data storage with data preservation. Storage is the active working copy, preservation is the long-term, access-controlled archive. A plan that describes only the laboratory server or cloud drive has not addressed preservation. Corrective action: specify a separate preservation pathway with its own access controls and format standards.

A second error is writing metadata standards that are too generic to be actionable. Stating that files will be "well labelled" does not tell anyone what labels to use. The corrective action is to include a naming convention with explicit date formats, animal identifiers, and treatment group codes, and to provide one worked example in the plan itself.

A third error is failing to reconcile data sharing commitments with the consent or approval documents. A plan that promises open sharing of clinical images or owner-provided information may conflict with the original client consent. The corrective action is to review the plan against the ethics approval and any data use agreements before the study begins, not after data collection is complete.

A fourth error is neglecting version control for protocols and analysis scripts. Veterinary studies often span years, and the protocol in effect when the first animal was enrolled may differ from the version in effect at study close. The corrective action is to timestamp all protocol versions and record which version applies to each data batch.

Limitations of the Current Evidence

The evidence base for data management practices in veterinary research is thinner than in human clinical research. Much of the guidance is adapted from human medicine and may not account for species-specific data structures, such as herd-level production records or pathology image archives. The EQUATOR Network reporting guidelines include instruments relevant to veterinary studies, but they were developed primarily for human health research and their applicability to veterinary contexts is still being assessed.

Expert opinion differs on several points. One contested area is the appropriate retention period for raw imaging data versus derived measurements. Some specialists argue that raw files must be kept indefinitely because re-analysis methods improve over time, others hold that the derived dataset plus the analysis code is sufficient. A second contested area is whether cloud storage is acceptable for sensitive clinical data. Institutional policies vary, and some funding bodies require that data remain on servers within a specific jurisdiction. A third area of divergence is the level of detail required in metadata for field studies versus clinical trials. Field studies with observational data may need more extensive location and environmental descriptors than a controlled trial, but no consensus standard exists.

The MSD Veterinary Manual provides species-specific guidance on clinical data recording, but it does not address research data management directly. Researchers should therefore treat species-specific data conventions as a starting point and document their own decisions explicitly.

Escalation and Referral Pathways

Certain situations require escalation beyond the research team. If a data breach exposes owner-identifiable information, the institutional privacy officer and the relevant ethics committee must be notified without delay. The American Veterinary Medical Association practice resources provide guidance on professional obligations regarding client confidentiality, and institutional policies will define the reporting timeline.

If the study involves notifiable diseases or animals moving across borders, the WOAH terrestrial animal health standards may impose data reporting obligations that override the research data management plan. Researchers should confirm whether their study species or pathogens fall under these standards before data collection begins.

Laboratory involvement is warranted when data formats are unfamiliar or when the research team lacks expertise in a specific data type. For example, a study using scanning electron microscopy or immunohistochemistry may require the laboratory to specify its own file formats and quality thresholds, as described in recent porcine urethral tissue research characterizing tissue preparation methods for lower urinary tract studies. The plan should record these laboratory specifications verbatim instead of paraphrasing them.

Specialist consultation is appropriate when the study design includes data types that the team has not managed before, such as continuous physiological monitoring or genomic sequence data. A data librarian or informatics specialist can review the plan before submission and identify gaps that a clinician would not anticipate.

Troubleshooting Table

Frequently Asked Questions

How much should a data management plan cost for a small-scale veterinary study?

Budget between 5% and 10% of total project funds for data management activities, though small studies with existing institutional infrastructure may spend less. Storage subscriptions, secure transfer tools, repository deposit fees, and personnel time for documentation and curation constitute the major expenses. Open-access repositories often charge per deposit, while institutional servers may offer free storage with limited capacity. For practice-based research, consider whether existing practice management software can export de-identified records in a usable format. If funds are constrained, prioritize backup redundancy and metadata creation over elaborate data visualization tools. The ARRIVE guidelines specify minimum reporting elements that influence documentation costs, so review them during budget planning instead of after data collection.

What should I do when my institution lacks secure storage infrastructure?

Use a tiered approach that matches data sensitivity to available resources. For anonymised aggregate data, encrypted cloud storage with two-factor authentication may suffice. For identifiable clinical records or proprietary data, institutional servers or commercial services with signed data processing agreements are preferable. If neither exists, store data on encrypted portable drives kept in locked cabinets, with a second encrypted copy off-site. Document these limitations in the data management plan and state the risk acceptance explicitly. The AVMA practice resources address client confidentiality expectations that apply to research data derived from clinical cases. Reassess infrastructure needs at each project milestone, and escalate to institutional information technology leadership if data volume exceeds the planned solution.

How do data management requirements differ for wildlife versus companion animal research?

Wildlife studies introduce logistical constraints that companion animal research rarely faces. Field conditions may preclude immediate data entry, so paper forms and offline devices require synchronisation protocols. GPS coordinates and biological samples carry conservation sensitivity that may justify restricted access, even after de-identification. Sample tracking becomes critical when specimens move between field sites, diagnostic laboratories, and biorepositories. The WOAH terrestrial animal health standards outline surveillance data expectations that may apply to wildlife disease studies with trade implications. Conversely, companion animal research typically involves client-owned animals, requiring consent documentation that specifies data reuse boundaries. Plan for different retention periods as well, since wildlife specimen collections may remain valuable for decades while clinical datasets often have shorter analytical lifespans.

What documentation must accompany raw data files for another researcher to reproduce my analysis?

Provide a data dictionary defining every variable name, code, unit, and missing value indicator. Include the protocol version that generated the data, equipment calibration records, and any deviations from the approved protocol. For clinical data, document inclusion and exclusion criteria applied at each stage. Record the date and time of each data extraction or transformation step. The EQUATOR Network reporting guidelines list the methodological details reviewers expect in publications, and your documentation should support those same details. Name files systematically so that the processing order is evident, and include a README file describing the directory structure. For multi-site studies, document site-level differences in data collection procedures. This documentation layer often requires more time than data collection itself, so allocate personnel hours accordingly.

How should I respond when a funding agency requests data I planned to keep confidential?

Distinguish between data that cannot be shared due to client confidentiality or proprietary agreements and data that you prefer not to share. For the former, provide the agency with the relevant consent form language, institutional review board approval, or material transfer agreement that restricts sharing. For the latter, propose a controlled access model where interested researchers submit a brief analysis plan and agree to a data use agreement. Many agencies accept this compromise. The ARRIVE guidelines emphasize transparency about data availability, so state your sharing restrictions clearly in the plan instead of remaining silent. If the agency requires open deposition, negotiate an embargo period that protects your primary analysis timeline. Document all correspondence about data access decisions, as this record protects you if disputes arise later.

What record-keeping practices protect me during an audit or research misconduct investigation?

Maintain a versioned log of every data file, including creation dates, modification dates, and the person responsible for each change. Preserve original unmodified files separately from working copies. Record all communication about data decisions, including email correspondence with collaborators about analysis choices. Keep consent forms, approval letters, and data sharing agreements in a single project file. The MSD Veterinary Manual notes that clinical records must meet professional standards of accuracy and completeness, and research data should meet the same standard. If you correct an error, document the correction instead of overwriting the original entry. Retain this documentation for at least the period required by your institution or funder, typically five to seven years after publication. Automated audit trails in electronic systems strengthen your position, so enable them where available.

Related Clinical & Scientific Guides

• Conducting Systematic Reviews of Veterinary Diagnostic Test Accuracy
• Bias in Veterinary Research: Types, Sources, and Mitigation
• Cluster Randomized Trials in Veterinary Research: Design and Analysis

References and Further Reading

• Preparing intensive care for the next pandemic influenza.. 2019.
• Advancing urethral health research: Characterization of a male porcine urethra for lower urinary tract investigations.. 2025.
• ARRIVE Guidelines 2.0 for Reporting Animal Research. PLOS Biology, 2020.
• EQUATOR Network Reporting Guidelines. EQUATOR Network.
• MSD Veterinary Manual, Professional Edition. MSD Veterinary Manual.
• American Veterinary Medical Association Practice Resources. American Veterinary Medical Association.
• WOAH Terrestrial Animal Health Code. WOAH.

Related Articles

• Data Management in Veterinary Research: Collection, Storage, and Sharing
• Handling Missing Data in Veterinary Clinical Research
• Designing Questionnaire Studies for Veterinary Research
• Developing Monitoring Plans for Veterinary Clinical Trials
• Using Mixed Methods in Veterinary Research

This article is educational professional reference material for veterinary audiences. It is not a substitute for veterinary diagnosis, individual clinical judgment, current product labeling, or applicable regulatory requirements.