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Learn about creating a data management plan

With each new research project, it is critical to develop a data management plan (DMP) that details your methods for collecting, organizing, backing up and storing the data you will be creating. The following is a general template that includes questions to address; however, specific funding agencies such as the National Science Foundation have their own requirements on what information to include in a data management plan.

Please note that this worksheet is intended to help you gather your data for your DMP. It is not customized by agency, so be sure to check for specific agency requirements. Consult Funding Agency Guidelines for more information.

Consider using the DMPTool for a more precise DMP.

V1 last updated MM-DD-YYYY

Name of student / researcher(s) Your Name
Name of group / project Project Name or Research Lab (for group plan), Mississippi State University
Funding body(ies)  
Partner organizations  
Project duration Start: MM-DD-YYYY     End: MM-DD-YYYY
Date written MM-DD-YYYY

Table of Contents

  1. Introduction
  2. Data Types
  3. Data Organization, Documentation and Metadata
  4. Data Access and Intellectual Property
  5. Data Sharing and Reuse
  6. Data Preservation and Archiving

1. Introduction

The research project described in this data management plan (DMP) ...

2. Data Types and Storage

The types of data generated and/or used in this project include ...

Section 2 Checklist

  • What type of data will be produced?
  • How will data be collected? In what formats?
  • How to document data collection?
  • Will it be reproducible? What would happen if it got lost or became unusable later?
  • How much data will it be, and at what growth rate? How often will it change?
  • Are there tools or software needed to create/process/visualize the data?
  • Will you use pre-existing data? From where?
  • Storage and backup strategy?

3. Data Organization, Documentation and Metadata

The plan for organizing, documenting, and using descriptive metadata to assure quality control and reproducibility of these data include ...

Section 3 Checklist

  • What standards will be used for documentation and metadata?
  • What is the project and data documentation format/standard?
  • What directory and file naming convention will be used?
  • What project and data identifiers will be assigned?
  • Is there a community standard for metadata sharing/integration?

4. Data Access and Intellectual Property

The data have the following access and ownership concerns ...

Section 4 Checklist

  • What steps will be taken to protect privacy, security, confidentiality, intellectual property or other rights?
  • Does your data have any access concerns? Describe the process someone would take to access your data.
  • Who controls it (e.g., PI, student, lab, University, funder)?
  • Any special privacy or security requirements (e.g., personal data, high-security data)?
  • Any embargo periods to uphold?

5. Data Sharing and Reuse

The data will be released for sharing in the following way ...

Section 5 Checklist

  • If you allow others to reuse your data, how will the data be discovered and shared?
  • Any sharing requirements (e.g., funder data sharing policy)?
  • Audience for reuse? Who will use it now? Who will use it later?
  • When will I publish it and where?
  • Tools/software needed to work with data?

6. Data Preservation and Archiving

The data will be preserved and archived in the following ways ...

Section 6 Checklist

  • How will the data be archived for preservation and long-term access?
  • How long should it be retained (e.g., 3-5 years, 10-20 years, permanently)?
  • What file formats? Are they long-lived?
  • Are there data archives that my data is appropriate for (subject-based? Or institutional)?
  • Who will maintain my data for the long-term?


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