What is a Data Management Plan?
A data management plan, or DMP (sometimes also called a data sharing plan), is a formal document that outlines what you will do with your data during and after a research project. Many funding agencies, especially government funding sources, require a DMP as part of their application processes. Even if you are not seeking funding for your research, documenting a plan for your data is a best practice and will help your data comply with Harvard's policies for responsible data management.
A DMP is a living document: Research is all about discovery, and the process of doing research sometimes requires you to shift gears and revise your intended path. Your DMP is a living document that you may need to alter as the course of your research changes. Remember, any time your research plans change, you should review your DMP to make sure that it still meets your needs.
Because some funding agencies do not provide specific guidelines, below is a list of typical data management plan elements. You should review specific guidelines for data management planning from the funding agency with which you are working. Elements of your DMP may be reused in your protocols and in the Institutional Review Board (IRB) and methodology descriptions.
- Types of data: What is the source of your data? In what formats are your data? Will your data be fixed or will it change over time? How much data will your project produce?
- Contextual details (metadata): How will you document and describe your data?
- Storage, backup and security: How and where will you store and secure your data?
- Provisions for protection/privacy: What privacy and confidentiality issues must you address?
- Policies for re-use: How may other researchers use your data?
- Access and sharing: How will you provide access to your data by other researchers? How will others discover your data?
- Archiving and providing access: What are your plans for preserving the data and providing long-term access?
- Roles and plan oversight: Who will be responsible for aspects of data management throughout the project and what resources are required for implementation?