Data Management Plans for Grant Proposals in Life Sciences
By Dr. Zubair Khalid, DVM, MS, PhD ·
Quick Answer
- A data management plan for a life sciences grant proposal must describe how research data will be collected, documented, stored, shared, and preserved in alignment with funder requirements.
- Use the FAIR principles (findable, accessible, interoperable, reusable) as the organizing framework for your plan, and map each section of your proposal to specific funder expectations.
- The most common limitation is assuming one template fits all funders, each agency has distinct data sharing policies, timelines, and allowable costs that must be verified before submission.
At a Glance
| Plan Component | What Funders Expect | Common Gap in Drafts |
|---|---|---|
| Data Description | Types, formats, volume, and origin of data generated or reused | Listing data types without explaining why those formats were chosen |
| Documentation and Metadata | Standards for naming, describing, and organizing data so others can interpret it | Naming a metadata standard without showing how it will be applied |
| Storage and Backup | Secure storage during the project with regular backup procedures | Describing institutional servers without addressing version control |
| Sharing and Reuse | How data will be made available, when, and under what conditions | Promising open sharing without addressing privacy or consent constraints |
| Preservation | Long-term repository selection and retention period beyond the grant | Confusing project storage with archival preservation |
| Roles and Responsibilities | Who manages data at each stage and who is accountable | Naming a lab manager without specifying data curation duties |
| Budget and Resources | Realistic costs for storage, personnel time, and repository fees | Omitting personnel effort for data preparation |
| Ethical and Legal Compliance | How human subjects, animal welfare, or proprietary data are protected | Stating compliance without referencing institutional oversight |
Why Grant Funders Require Data Management Plans
Federal funding agencies in the life sciences treat data as a public asset. When a grant supports research that produces datasets, the agency expects those datasets to be managed so they can be verified, reused, and built upon by other scientists. A data management plan is the document that demonstrates you have thought through the full lifecycle of your data before the work begins.
The National Institutes of Health and the National Science Foundation both require data management plans as part of grant applications, though their specific expectations differ. The NIH Office of Intramural Training and Education describes research careers as involving ongoing professional development and mentoring, which extends to learning how to manage the products of research. The NSF careers page similarly frames scientific work in terms of managing complex projects and their outputs. For a grant applicant, this means the data management plan is not a bureaucratic afterthought but a core part of how you present yourself as a competent investigator.
The practical implication is straightforward. Reviewers read data management plans to assess whether you understand the risks of data loss, the standards in your field, and the practical steps required to make your data useful to others. A weak plan signals that you may not manage the research itself effectively. A strong plan demonstrates that you have considered how the data will be handled from the day you collect the first sample to the point where other researchers can access it years later.
Core Principles of a Fundable Data Management Plan
The FAIR Framework as Your Organizing Structure
The FAIR principles provide a widely accepted structure for data management plans in the life sciences. Findable means your data has a persistent identifier and rich metadata that allows others to locate it. Accessible means the data can be retrieved through a standardized protocol, with clear conditions for access when restrictions apply. Interoperable means the data uses formats and vocabularies that can be combined with other datasets. Reusable means the data is sufficiently documented that another researcher can understand and use it without contacting you.
When you write each section of your plan, ask how that section advances one of these four goals. For example, the documentation section is where you explain your metadata approach, which directly supports findability and reusability. The sharing section is where you address accessibility, including any legitimate restrictions. This framework keeps your plan focused on outcomes instead of a list of activities.
Funder-Specific Requirements Override Generic Templates
Every federal agency publishes its own data management plan guidance, and these documents change over time. The NIH requires a Data Management and Sharing Plan for most research proposals, with specific expectations about the types of data, related tools and code, and the timeline for sharing. The NSF requires a Data Management Plan that is no more than two pages and must be submitted as a supplementary document. Other funders, including private foundations and international agencies, have their own requirements.
The critical decision is to locate the current guidance from your target funder before you write a single sentence. A template that worked for an NIH R01 may not satisfy an NSF standard grant, and neither may match the requirements of a European funder. Build your plan by reading the funder's official guidance document and mapping each requirement to a section of your plan. This approach also helps you avoid the common failure of writing a generic plan that does not reference the specific data your project will produce.
Data Types and Formats Drive Every Other Decision
Your plan must begin with a concrete description of the data your project will generate. In the life sciences, this could include genomic sequences, proteomics mass spectrometry output, microscopy images, clinical measurements, survey responses, or computational models. For each data type, specify the file format, the expected volume, and the rate at which data will accumulate.
The choice of file format has long-term consequences. Open, non-proprietary formats such as CSV, FASTA, or TIFF are generally preferred for preservation because they do not depend on a specific software vendor remaining in business. Proprietary formats may be necessary during active analysis, but your plan should describe how you will convert final datasets to sustainable formats for sharing and preservation. This decision affects your storage estimates, your repository selection, and your budget.
Practical Workflow for Writing Your Data Management Plan
Step 1: Inventory Your Data Before You Write
Start by listing every type of data your project will produce or reuse. For each data type, record the format, the estimated volume, the software needed to read it, and whether it contains sensitive information. This inventory becomes the factual foundation for your entire plan. If you cannot describe your data concretely, you cannot write a credible plan for managing it.
Work through your experimental or observational workflow and identify data at each stage. Raw instrument output, processed intermediate files, analysis scripts, final results tables, and documentation all count as research data. Many applicants make the mistake of describing only the final published dataset, leaving out the intermediate products that are necessary for reproducibility.
Step 2: Match Each Data Type to a Documentation Standard
For each data type in your inventory, identify the metadata standard used in your field. Genomics has standards such as MIAME for microarray experiments and MINSEQE for sequencing data. Proteomics uses MIAPE. Ecological and environmental data may use EML. Clinical research follows CDISC standards. Your plan should name the standard and describe how you will apply it to your specific data.
If no formal standard exists for your data type, describe the documentation approach you will use. This might include readme files, data dictionaries, lab notebooks, or code comments. The key is to show that someone outside your laboratory could interpret your data without asking you questions. The NIH Office of Intramural Training and Education emphasizes that scientific work involves clear communication, and data documentation is a form of communication with future users.
Step 3: Design Storage and Backup That Matches Your Risk Profile
Describe where data will be stored during the project and how it will be backed up. Most institutions provide network storage with automatic backup, but you should verify the backup frequency and retention period. For large genomic or imaging datasets, you may need dedicated storage with a cost estimate. Your plan should name the specific systems you will use and the backup schedule.
Consider the risk of data loss at each stage of your workflow. Raw data from an instrument should be transferred to managed storage immediately and never stored only on the instrument's local drive. Analysis files should be versioned so you can recover previous states. The plan should state who is responsible for performing backups and how often they verify that backups can be restored. A backup that has never been tested is not a backup.
Step 4: Define Your Sharing Approach With Clear Conditions
The sharing section of your plan must state what data will be shared, when it will be shared, and under what conditions. Many funders expect data to be shared at the time of publication or within a specified period after the award ends. Your plan should name the repository where data will be deposited and describe the access level.
Not all data can be shared openly. Human subjects data may require de-identification or controlled access through a data use agreement. Proprietary data from industry partners may have restrictions. Animal studies may have welfare considerations that affect data release. Your plan should acknowledge these constraints and describe how you will maximize sharing within ethical and legal boundaries. Reviewers respond well to plans that recognize legitimate restrictions while still committing to the broadest appropriate sharing.
Step 5: Select a Repository and Describe Preservation
Choose the repository where your data will be preserved for the long term. Domain-specific repositories such as NCBI GenBank, the European Nucleotide Archive, or the ProteomeXchange consortium are often the best choice because they have established metadata standards and preservation commitments. General repositories such as Dryad or Zenodo may be appropriate for data without a domain repository.
Your plan should name the repository, confirm that it accepts your data types, and describe the accession process. Some repositories charge fees, which should appear in your budget. The preservation section should state how long the repository commits to maintaining the data and what happens if the repository ceases operation. This level of specificity distinguishes a credible plan from a vague promise to deposit data somewhere.
Step 6: Assign Roles and Estimate Effort
Data management requires personnel time, and your plan should reflect that reality. Name the person responsible for each data management task, whether that is the principal investigator, a postdoctoral researcher, a graduate student, or a dedicated data manager. For each role, describe the specific duties and the approximate effort required.
The NIH Office of Intramural Training and Education describes the research workforce as including individuals at multiple career stages, each with distinct training needs. A data management plan should acknowledge that the person collecting data may not be the person who prepares it for sharing. Assigning clear responsibility prevents the common failure where everyone assumes someone else is handling data curation.
Records and Measurements for Data Management
What to Track During the Project
Your data management plan should describe the records you will keep to demonstrate compliance. This includes version histories for analysis files, logs of data transfers from instruments, documentation of metadata application, and records of repository deposits. These records serve two purposes. They allow you to verify that your plan is being followed, and they provide evidence if a funder requests an update or a final report.
Track the volume of data stored and the rate of growth. This information helps you adjust storage allocations before you run out of space. Track the time required for data preparation tasks so you can refine your effort estimates for future proposals. Track any data sharing requests you receive and how you responded, as this demonstrates the value of your sharing approach.
Measuring Compliance With Your Plan
Schedule regular reviews of your data management activities, perhaps quarterly or at each major project milestone. At each review, check whether data is being documented according to the standards in your plan, whether backups are current, and whether any data has been deposited in the repository. Document the results of these reviews so you can identify problems early.
If you find that your plan is not working in practice, revise it. A data management plan is a living document, and funders generally expect you to update it as your research evolves. The key is to document the change and the reason for it. This demonstrates that you are actively managing your data instead of filing the plan and forgetting it.
Common Failure Patterns in Data Management Plans
Writing a Generic Plan That Does Not Reference Your Data
The most frequent failure is a plan that could apply to any project in any field. Reviewers can identify these immediately because they contain no specific data types, no named repositories, and no concrete formats. A credible plan must be grounded in the actual data your project will produce. If you cannot name the file formats and approximate volumes, you have not done the necessary planning.
Confusing Storage With Preservation
Project storage on an institutional server is not preservation. Institutional storage may be deleted when you leave the institution or when the project ends. Preservation requires deposit in a repository with a long-term commitment to maintaining access. Your plan must address both active storage during the project and archival preservation afterward, and these are distinct sections with distinct costs.
Overpromising Open Sharing Without Addressing Constraints
Some applicants write that all data will be freely available to everyone, ignoring the reality of human subjects protections, proprietary agreements, or sensitive animal research data. This approach fails because it is not credible. Reviewers know that some data cannot be shared openly. A better plan acknowledges the constraints and describes how you will maximize sharing within them, such as depositing de-identified data or providing access through a controlled mechanism.
Omitting the Budget for Data Management
Data management costs real money. Repository fees, storage allocations, and personnel time for data curation all have costs. A plan that describes ambitious sharing without a corresponding budget line is not credible. Estimate these costs and include them in your budget justification. The effort of preparing data for sharing is often underestimated, and reviewers look for realistic effort commitments.
Welfare and Safety Context in Life Sciences Data
Human Subjects Data
If your research involves human participants, your data management plan must address privacy and confidentiality. Describe how you will de-identify data before sharing, what variables will be removed or altered, and whether you will use a controlled access mechanism. Reference your institutional review board approval and any consent language that affects data sharing. The consent form should have told participants how their data would be used and shared, and your plan must be consistent with that language.
Animal Research Data
For animal studies, your data management plan should reference your institutional animal care and use committee approval and describe how data will be handled in compliance with welfare requirements. This may include documenting animal identifiers, health records, and experimental conditions. The Association of American Veterinary Medical Colleges describes the veterinary profession as grounded in scientific training and ethical responsibility, and this extends to how research data from animal studies is managed and shared.
Select Agents and Controlled Data
Some life sciences data falls under regulatory control, including select agents, certain pathogens, and dual-use research of concern. Your plan must acknowledge these restrictions and describe the security measures in place. This may include restricted access to data, encryption, and compliance with institutional biosafety requirements. Do not attempt to hide these restrictions, instead, describe how you will comply while still sharing as much data as possible.
Professional Escalation Criteria
When to Consult Institutional Experts
You should seek help from your institution's research office, data librarian, or information technology security team when you encounter situations beyond your expertise. Consult these experts if you are unsure whether your data falls under regulatory control, if you need to negotiate a data use agreement, or if you are selecting a repository for sensitive data. These professionals can also help you estimate costs and identify institutional resources you did not know existed.
When to Revise Your Plan
Revise your plan when your research design changes in ways that affect data types, volumes, or sharing approaches. Revise when you discover that a named repository does not accept your data format. Revise when funder guidance changes. A plan that is never updated becomes inaccurate, and an inaccurate plan is worse than no plan because it creates a false sense of compliance.
When to Escalate a Compliance Concern
If you discover that data has been lost, that a backup failed, or that data was shared in violation of a restriction, escalate the issue immediately to your institutional research office and the relevant compliance committee. Do not attempt to conceal the problem. Funders and institutions respond better to prompt disclosure with a corrective plan than to discovery after the fact. Your data management plan should include a brief statement of how you will handle data breaches or losses, which demonstrates that you have considered these risks.
Frequently Asked Questions
What is the difference between a data management plan and a data sharing plan?
A data management plan covers the full lifecycle of data, including collection, documentation, storage, backup, sharing, and preservation. A data sharing plan focuses specifically on how and when data will be made available to others. Some funders use the terms interchangeably, while others, such as the NIH, have shifted to a combined Data Management and Sharing Plan that covers both. Check your funder's current guidance to understand which scope is expected.
How long should a data management plan be?
Length requirements vary by funder. The NSF limits the Data Management Plan to two pages. The NIH does not specify a page limit but expects the plan to address the required elements concisely. Other funders may have their own limits. The goal is to address every required element with specific, concrete information instead of to reach a particular length. A focused two-page plan is generally better than a vague five-page plan.
Can I use the same data management plan for multiple grant applications?
You can reuse the structure and approach, but you must customize the plan for each funder and each project. Different funders have different requirements, and different projects produce different data. A plan that does not reference the specific data, repository, and timeline for a given project will be identified as generic and will weaken your application. Build a template that you adapt for each submission.
What should I do if my data cannot be shared openly?
Describe the restrictions honestly and explain how you will maximize sharing within those constraints. For human subjects data, this may mean depositing de-identified data with controlled access. For proprietary data, this may mean sharing derived results instead of raw data. Reviewers accept legitimate restrictions when the plan explains them and shows that the investigator has thought through the alternatives.
How do I estimate the budget for data management?
Estimate the cost of repository deposit fees, additional storage during the project, and personnel time for data preparation and curation. Check the fee schedules of the repositories you plan to use. Estimate the hours required for metadata application and data cleaning based on the volume and complexity of your data. Include these costs in your budget justification with a brief explanation of how you derived the estimates.
What happens if I do not follow my data management plan?
Funders may request updates on data management activities during the project and may review compliance at the end of the award. Failure to share data as promised can affect future funding decisions and may be considered a violation of the award terms. Institutional policies may also apply. The practical approach is to treat the plan as a commitment and to document your compliance activities throughout the project.
Where can I find training on data management for life sciences?
Your institution's library or research office likely offers workshops on data management planning. Professional societies such as the American Society for Microbiology provide career development resources that may include data management training. The NIH Office of Intramural Training and Education offers professional development resources for researchers at various career stages. Check with your institution and professional organizations for current offerings.
How do I choose a repository for my data?
Start with the repositories used in your field. If you are generating genomic data, use a genomics repository. If you are generating proteomics data, use a proteomics repository. If no domain repository exists, choose a general repository with a preservation commitment. Verify that the repository accepts your data formats, understand its access policies, and check its fees. Your institution's data librarian can help you evaluate repository options.
Using the Evidence
| Source | Best use in this topic | Important limitation |
|---|---|---|
| Occupational Outlook Handbook | official guidance | Check the linked page for current local requirements |
| O*NET OnLine | official guidance | Check the linked page for current local requirements |
| Office of Intramural Training and Education | official guidance | Check the linked page for current local requirements |
Related Articles
- Data Management Plan Template: A Practical Guide for Researchers
- How to Become a Data Scientist in Life Sciences: Education and Career Path
- What Is Data Management? A Primer for Life Science Researchers
- Choosing the Best Online Data Science Master's for Life Sciences Professionals
- Research Data Management Plan: From File Naming to Long-Term Sharing
References and Further Reading
- Occupational Outlook Handbook. U.S. Bureau of Labor Statistics.
- O*NET OnLine. U.S. Department of Labor.
- Office of Intramural Training and Education. National Institutes of Health.
- Careers at NSF. U.S. National Science Foundation.
- Career Services. American Society for Microbiology.
- AAVMC Career Resources. Association of American Veterinary Medical Colleges.
- NCBI Literature Resources. National Center for Biotechnology Information.
- PubMed. National Library of Medicine.
This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.