Data Management Plans

What is a Data Management Plan (DMP)?

A DMP describes what data will be acquired or generated as part of a research project, how the data will be managed, described, analyzed, and stored, and what mechanisms will be used to at the end of your project to share and preserve the data. 

One of the key advantages to writing a DMP is that it helps you think concretely about your process, identify potential weaknesses in your plans, and provide a record of what you intend to do. Developing a DMP can prompt valuable discussion among collaborators that uncovers and resolves unspoken assumptions, and provide a framework for documentation that keeps graduate students, postdocs, and collaborators on the same page with respect to practices, expectations, and policies. 

Data management planning is most effective in the early stages of a research project, but it is never too late to develop a data management plan.

How can I find out what my funding agency requires?

Most funding agencies require a DMP as part of an application for funding, but the specific requirements differ across and even within agencies. Many agencies, including the NSF and NIH, have requirements that apply generally, with some additional considerations depending on the specific funding announcement or the directorate/institute. 

Here are some resources to help identify what you’ll need:

For NSF 


What about other government agencies?

Need help figuring out what your agency needs? Ask a PRDS team member!

Where can I get help with writing a DMP?

As a Princeton researcher, you have free access to an online tool for writing DMPs: DMPTool. You just need to sign in as a Princeton researcher, and you’ll have access to templates, example DMPs, and Princeton-specific guidance. 

Additionally, PRDS offers free and confidential feedback on draft DMPs. To get feedback or help with your DMP, email your draft, the funding announcement, and the deadline to us, or select the “Request Feedback” option on the last page of your DMP template in the DMPTool.

What generally goes into a DMP?

Details will vary from funder to funder, but the Digital Curation Centre’s Checklist for a Data Management Plan provides a useful list of questions to consider when writing a DMP:

  • What data will you collect or create?
         Type of data, e.g., observation, experimental, simulation, derived/compiled
         Form of data, e.g., text, numeric, audiovisual, discipline- or instrument-specific
         File Formats, ideally using research community standards or open format (e.g., txt, csv, pdf, jpg)
  • How will the data be collected or created?
  • What documentation and metadata will accompany the data?
  • How will you manage any ethical issues?
  • How will you manage copyright and intellectual property rights issues?
  • How will the data be stored and backed up during research?
  • How will you manage access and security?
  • Which data should be retained, shared, and/or preserved?
  • What is the long-term preservation plan for the dataset?
  • How will you share the data?
  • Are any restrictions on data sharing required?
  • Who will be responsible for data management?
  • What resources will you require to implement your plan?

Additional key things to consider

Anticipate the storage, infrastructure, and software needs of the project

Consider the types of data that will be created or used in the project. For example, will your project…

  • generate large amounts of data?
  • require coordinated effort between offsite collaborators?
  • use data that has licensing agreements or other restrictions on its use?
  • Involve human or non-human animal subjects?

Answers to questions like these will help you accurately assess what you’ll need during the project and prevent delays during crucial stages. 

Create or adopt standard terminology and file-naming practices

Decide on file and directory naming conventions and stick to them. Document them (either independently or as part of a standard operating procedure (SOP) document) so that any new graduate students, post-docs, or collaborators can transition smoothly into the project.

Set a schedule for your data management activities

Plan and implement a back-up schedule onto shared storage in order to ensure that more than one copy of the data exists.  Periodic file and/or directory clean-ups will help keep “publication quality” data safe and accessible.

Assign responsibilities

Make it clear who is responsible for what. For example, assign a data manager who can check that backup clients are functional, monitor shared directories for clean-up or archiving maintenance, and follow up with project members as needed.

Think long-term

Decide where your data will go after the end of the project. Data that are associated with publications need to be preserved long-term, and so it’s good to decide early on where the data will be stored (e.g. a discipline or institutional repository) and when and how it will get there. Other data may need this level of preservation as well. PRDS can help you find places to store your data and provide advice about what kinds of data to plan to keep.