Provided by MSU Library Data Services
Last updated: 27 June 2018

 

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.

 

Types of Data

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

  • How will you capture, create, and/or process the data? (Identify instruments, software, imaging, etc. used)

 

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

  • What file formats and naming conventions will you be using?

  • What metadata standards do you plan to use to describe your data?
  • Preferred file formats: non-proprietary, openly documented formats encourage long-term 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. Files should only be compressed and/or archived when it is necessary. We encourage the use of structured organization (e.g. BagIt) within a compressed file

  • Dataset readme file template

  • Guide to codebooks from ICPSR

 

Storage, Backup and Security

 

 

Provisions for Protection/Privacy

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

 

 

Policies for re-use

  • What restrictions need to be placed on re-use of your data?

  • How will the data be licensed?

 

 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. Our research suggests that archiving data in a disciplinary repository promotes discovery and reuse of research data. For more information on recommended repositories, see our Data Discovery pageFor general open access data, we recommend archiving in Zenodo or OpenICPSR. Boilerplate language for both repositories 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?

  • 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?