What You Need to Know About the NIH Data Management Plan

The NIH closely scrutinizes the Data Management Plan (DMP) as a fundamental part of the grant application, for it demonstrates how data will be handled and generated during the research project. A well-crafted DMP reveals that the researcher has considered the full life cycle of the research data and is committed to sharing valuable resources with the scientific community, which enhances the impact and reproducibility of the research.

The following is a step-by-step guide on how to write the NIH DMP:

Understanding the NIH Data Sharing Policy:

Familiarize yourself with the specific data sharing requirements and policies outlined in the Funding Opportunity Announcement (FOA) or the agency-specific guidelines. Each NIH institute may have slightly different expectations for data sharing, so be sure to tailor your plan accordingly.

Starting early:

Begin working on your DMP early in the grant writing process. This detailed document requires thoughtful consideration, and rushing through it could lead to oversight or errors.

Types of data to be collected:

Clearly outline the different types of data that will be generated in your research project. This can include experimental results, survey responses, clinical data, genomic data, imaging data, etc.

Data collection procedures:

Explain how you will collect and generate the data. Describe the instruments, protocols, and methodologies you will use. Address issues like data quality control and validation.

Data organization and documentation:

Detail how you will organize and document your data during the research project. This can involve establishing a standardized naming convention, using metadata, and creating data dictionaries or codebooks.

Data storage and backup:

Describe how you will store your data securely during the project. This may involve using institutional servers, cloud-based storage, or other dedicated data repositories. Include information on data backup procedures to prevent data loss.

Data sharing:

Clearly state your intentions regarding data sharing. If you plan to share data, specify the timing and conditions under which the data will be made available to other researchers and through which platforms or repositories.

Data access controls and restrictions:

Address any sensitive or confidential data and explain how you will protect the privacy and confidentiality of human subjects, as well as any intellectual property considerations.

Data preservation:

Outline your plan for the long-term preservation of the data beyond the project’s duration. Identify suitable data archives or repositories for depositing the data and explain how you will provide access to the data after the project ends.

Data sharing agreement (if applicable):

If data sharing involves collaborations with other institutions or researchers, mention any data sharing agreements that will be established.

Roles and responsibilities:

Clearly define the roles and responsibilities of team members regarding data management. This includes data ownership, access permissions, and data custodianship.

Compliance and ethical considerations:

Address any compliance requirements related to data management, such as data use agreements or Institutional Review Board (IRB) approvals.

Budget:

If data management activities require additional funding, ensure that you include a well-justified budget for these expenses.

The DMP should ultimately be a comprehensive plan along with a timeline that identifies the data types and resources that will be generated, where they will be stored, and who will have access to them. Given the innovative and sensitive nature of the research that agencies such as the NIH and the NSF fund, this is a closely scrutinized part of the proposal.

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Mathilda Harris

Director at Grant Training Center

Over the past 18 years, she has written grants, conducted capital campaigns, developed strategic plans for grant procurement, and assisted individuals and institutions to write winning proposals for various donors.

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