Provided by MSU Library
Last updated: 17 December 2019


This template is based on NSF data management plan requirements. PIs should tailor the information to their research, and adapt the template to fit the policies and requirements of their specific NSF directorate or other funding agency.

A May 2019 Dear Colleague letter from NSF encourages the creation of machine-readable DMPs. Use DMPTool (link below right) to draft your DMP and download as HTML, CSV, Text, Word, or PDF. 

 Roles and Responsibilities

  • Who is responsible for implementing and maintaining this data management plan? 
  • Who will analyze or otherwise have access to the data?
  • For projects with collaborators, how will the collaborative data collection and analysis be implemented? Will you have documented shared guidelines?

Types of Data

  • What types of data will you be creating or capturing? (experimental measures, observational or qualitative, model simulation, existing)

  • What file formats will you use?
    • Non-proprietary, openly documented formats encourage long-term readability and preservation.
      • Text: plain text (ASCII, UTF-8), PDF/A, CSV, TSV, XML
      • Image: PDF/A, JPEG/JPEG2000, PNG, TIFF, SVG (no Java)
      • Audio: FLAC, AIFF, WAVE
      • Video: AVI, M-JPEG2000
      • Compressed/archived formats: GZIP/TAR, ZIP
    • If data must be saved in a proprietary format, provide information about the software, including what organization develops and maintains the software, and free substitutes if available
  • How will you capture, create, and/or process the data? (instruments, software, imaging, etc.) 

Contextual Details (Metadata) Needed to Make Data Meaningful to others

  • What naming conventions or controlled vocabularies will you be using?

  • Will you use metadata standards to describe your data?



Storage, Backup and Security


Provisions for Protection and Privacy

  • How are you addressing any ethical or privacy issues (IRB, anonymization of data)?


Policies for re-use


Policies for Access and Sharing

MSU Library recommends that datasets be archived in trustworthy data repositories, especially those that are commonly used in your discipline. For more information on recommended repositories, see our Data Publication pageFor general open access data, we recommend archiving in Dryad. MSU's institutional membership allows MSU researchers to archive data free of charge. Cut-and-paste language for Dryad is linked below.

  • Will you share your data publicly? How?

  • Will you keep the data private for a period before releasing it publicly?

  • If you won’t be sharing publicly, what is the process for gaining access to your data? For instance, will you publish in a repository that can provide restricted access?

  • If you won’t be sharing publicly, how long will you retain the data?


Plan for Archiving and Preservation of Access

  • What is your long-term plan for preservation and maintenance of the data?

  • Will you allocate funds in your grant budget to support long-term archiving and preservation access?