Data Management Plans
What is a Data Management Plan (DMP)?
A DMP (or DMSP, Data Management and Sharing Plan) 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:
- DMP Requirements (PAPPG, Chapter 2, Proposal prep instructions)
- Data sharing policy (PAPPG, Chapter 11, Post-award requirements)
- Links to directorate-specific requirements
For NIH (recently updated)
- The new policy for Data Management and Sharing Plans takes effect January 25, 2023
- Data Management and Sharing Policy Overview
- Research Covered Under the Data Management and Sharing Policy
- Writing a Data Management and Sharing Plan
- Final NIH Policy for Data Management and Sharing
- NOTE: Some specific NIH Institutes, Centers, or Offices have additional requirements for DMSPs. For example, applications to NIMH require a data validation schedule. Please check with your institute and your funding announcement to ensure all aspects expected are included in your DMSP.
- Prior policies:
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?
With recent and upcoming changes to the research landscape, it can be tricky to determine what information is needed for your Data Management (and Sharing) Plan. As a Princeton researcher, you have several ways of obtaining support in this area
Writing a Plan
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 be able to use and adapt templates, example DMPs, and Princeton-specific guidance. You can find some helpful public guidance on using DMPTool created by Arizona State University.
You are also welcome to schedule an appointment with a member of the PRDS team. While we are unable to write your DMP for you, we are happy to review your funding call and guide you through the information you will need to provide as part of your DMP
Having a Plan Reviewed
PRDS also offers free and confidential feedback on draft DMPs. If you would like to request feedback, we require:
- Your draft DMP (either via email [email@example.com] or by selecting the “Request Feedback” option on the last page of your DMP template in the DMPTool).
- Your funding announcement.
- Your deadline to submit your grant proposal.
NOTE: Reviewing DMPs is a process and may involve several rounds of edits or a conversation between you and our team. The timeline for requesting a DMP review is as follows:
- Single-lab or single-PI grants: no fewer than 5 business days;
- Complex, multi-institution grants, including Centers: no fewer than 10 business days.
We will make every effort to review all DMPs submitted to us, however, we cannot guarantee a thorough review if submitted after our requested time frame.
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.
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.
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.