Budgeting for Data Management

Nashia Hussain
May 29
5 min read

Updated: Jun 4

Effective budgeting for data management helps ensure that institutions can meet policy requirements and sustain data over the long term. Higher education institutions and research organizations are increasingly recognizing that managing research data is not just a technical challenge but also a financial one.

With agencies like the National Institutes of Health (NIH) implementing new Data Management and Sharing (DMS) policies, organizations must plan and allocate resources for data handling from the beginning of their research projects. By budgeting for data storage, curation, and compliance, institutions can avoid costly surprises, maintain compliance with funder requirements, and protect their research data.

NIH Policy on Budgeting for Data Management

The NIH’s Data Management and Sharing Policy (DMS), effective January 25, 2023, requires researchers to plan for and include data management costs in their grant applications. The policy recognizes that making data accessible and shareable can involve expenses. Researchers are encouraged to request these funds in their budgets, as long as they fit with their proposed DMS Plan and are incurred during the project’s performance period.

Allowable Costs:

Curating data and developing supporting documentation
Formatting data according to community standards
De-identifying sensitive data
Preparing metadata to enhance discoverability and reusability
Unique infrastructure needs for local management before repository deposit
Repository deposit fees for preserving and sharing data (single or multiple repositories)

If a project plans to store and share scientific data for 10 years in an approved repository that requires a fee, the total cost for the entire 10-year period must be paid before the project is completed.

Unallowable Costs:

Costs related to infrastructure that fall under institutional overhead, such as Facilities and Administrative expenses
Routine costs associated with conducting research or collecting data
Expenses that are charged twice or charged inconsistently as both direct and indirect costs

Justifying Costs in NIH Applications

Budgets must identify DMS-related costs in the appropriate categories, such as personnel, equipment, and other expenses. Justifications should be included under the label "Data Management and Sharing Justification" and contain the following:

A summary of the data to be preserved and shared
Repositories to be used
Estimated costs with brief explanations by category

This section should be no longer than half a page and must be attached to either the R&R Detailed Budget or the PHS 398 Modular Budget Form, depending on the funding request:

R&R Detailed Budget Form: Used when requesting more than $250,000 in direct costs per year. This form provides a full breakdown of all costs. It is also required for foreign organizations and any applications involving human fetal tissue.
PHS 398 Modular Budget Form: Used for domestic organizations requesting $250,000 or less in direct costs per budget period. Costs are submitted in $25,000 increments with less detailed breakdowns. When using this format, the DMS justification must be included in the “Additional Narrative Justification” section.

Only one of these budget forms may be used per application. The appropriate format is determined by your funding level, institution type, and any special conditions outlined in the Funding Opportunity Announcement (FOA).

Typical Data Management Cost Categories

1. Storage and Preservation

Cloud storage subscriptions or institutional infrastructure expansion
Long-term archiving costs post-project completion

2. Curation and Metadata Development

Time and tools needed to curate, document, and standardize data
Staff costs for data librarians, RAs, or specialized contractors

3. Repository Deposit Fees

One-time or ongoing fees for data hosting in established repositories
Additional fees for use of multiple repositories, if applicable

4. Compliance and Monitoring

De-identification services and privacy compliance measures
Monitoring tools, periodic audits, and policy enforcement

5. Personnel and Training

Data stewards or managers allocated for part of their time, such as 0.4 FTE for R01
Training expenses for staff or researchers using data tools and platforms

Each project may require a different mix of these components depending on the type and volume of data, regulatory needs, and institutional capacity. Budgeting early ensures these needs are covered without last-minute expenses or gaps in data handling.

Common Challenges in Budgeting for Data Management

Lack of Cost Frameworks

There is no universal standard for estimating data management costs. Investigators must often make assumptions or develop their models. Some resources, like the UK Data Service and OpenAIRE tools, offer guidance, but many institutions still navigate this without formal frameworks.

Underestimating Long-Term Needs

It is common to underestimate the cost of long-term storage, preservation, and compliance. Data volume may grow unexpectedly, or researchers might overlook future repository costs. This leads to budgeting gaps or poor-quality data storage solutions.

Timing and Funding Constraints

The NIH requires all costs incurred within the grant’s award period, even if services, such as 10-year repository storage, extend beyond it. This requires upfront payment planning, which many researchers are unaccustomed to. Funding pressures may also push teams to under-budget data management needs.

Low Awareness

Data management may still be viewed as secondary to research activities. Without internal education and policy support, teams may cut corners or leave out budget lines altogether. Finance and research offices must work together to reinforce that data stewardship is an essential, fundable component of the research process.

Proactive Strategies for Success

Incorporate data budgeting early into the proposal process
Use data lifecycle mapping to estimate when and where costs will arise
Compare repository fees and explore shared infrastructure options
Regularly review and adjust the budget to reflect actual usage and needs
Evaluate commercial repository providers with simplified subscription models to simplify and streamline the estimating and forecasting process

How myLaminin Supports Effective Budgeting

myLaminin is an integrated, secure Research Data Management (RDM) platform designed to help institutions navigate budgeting, compliance, and stewardship challenges. By providing long-term, secure research data subscription services, researchers and institutions can accurately forecast those costs for inclusion in grant submissions and reduce gaps in omission.

Key Benefits

Centralized tools for curation, metadata generation, and repository integration
End-to-end encryption and secure audit trails to meet regulatory standards
Role-based access controls and e-signature agreements for team collaboration, including support for research, legal services, and research librarians in the data gathering and curation process
Scalable pay-per-use pricing model to match costs to project needs
Built-in analytics and tracking dashboards to inform future budgeting

By streamlining the RDM process and reducing reliance on fragmented systems, myLaminin helps institutions improve efficiency, simplify budgeting, and stay compliant with funder mandates such as the NIH DMS Policy.

Conclusion

Budgeting for data management is no longer optional. As funders increase expectations for transparency, accessibility, and long-term preservation, institutions must be ready to plan, justify, and optimize their data-related expenses. The NIH’s guidance gives clear direction on what is allowed and what is not, and platforms like myLaminin are stepping in to support those goals. By adopting proactive planning strategies and modern infrastructure, research organizations can ensure their data not only meets compliance standards but also delivers ongoing value to the broader scientific community.

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