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Writing a DMP Step by Step

A data management plan (DMP) is a maximum of two-pages.

NSF Data Management Plan Guide for Boston College Researchers (with suggested language)

Writing a DMP Step by Step

Step 1: Description of Data

  1. What data will be created or used?
    Tips: what data will be used as inputs in this project, e.g., existing data, survey data, scraping data, physical samples.
  2. What data will be produced?
    Tips: the data outputs in your research project, e.g., spreadsheet, images, lab notebooks, audio/video files.
  3. How will you produce the data?
    Tips: what tools will be used to produce data in this project, e.g., software, methods, devices, web-based platforms.
  4. What is the size of your data?

Step 2: Metadata Standards

  1. What metadata standard will you use for your data?
    Tips: Data formats and metadata standards vary by discipline. For suggested standards, including minimum ISO recommendations when no standards are indicated, please visit our metadata guide.
  2. How will metadata be generated and in what form?
    Tips: Think about a readme file or codebook. How will it be generated and in what file format in your project?
  3. Why have you chosen this particular standard and approach for your metadata?
    Tips: The reason(s) can be conventions, community-accepted, widespread usage, software generated or etc.

Step 3: Policies for access, sharing, and privacy

  1. How will you make the data available?
    Tips: resources needed to make the data accessible: equipment, systems, expertise, data repository e.g., Boston College DataVerse, ICPSR, etc.
  2. When will you make the data available?
    Tips: immediately upon capture, at time of publication, after an embargo period, etc.
  3. Are there ethical and privacy issues? How will you address the issue and provide access to it?
    Tips: e.g., remove identifiers from human subject area, aggregate the dataset, use a access restricted data repository.
  4. What have you done to comply with your obligations in your IRB Protocol (if required)?
  5. Is the dataset covered by copyright? How will the data be licensed if rights exist?
    Tips: Intellectual property rights, e.g. patent applications; proprietary data own by another party, or regulated by policy or law, e.g., classified data, export controls, specific handling requirements.

Step 4: Policies for re-use, re-distribution, derivatives

  1. Will any permission restrictions need to be placed on the data?
    Tips: You may outline how your sharing policy in Step 3 can be applied to re-use/re-distribution.
  2. What and who are the intended or foreseeable uses /users of the data?
  3. Are there any reasons not to share or re-use data?
    Tips: If you are planning on restricting access, use or dissemination of the data, you must explain in this step, and how you will codify the restrictions. E.g., remove identifiers if privacy issues involved, provide access after embargo period expires.

Step 5: Plans for archiving and preservation

  1. What is the long-term strategy for maintaining, curating and archiving the data?
  2. What archive/repository/database have you identified as a place to deposit data?
    Tips: Boston College encourages local long-term archiving and preservation of data in the Institutional Repository for data, Boston College Dataverse. Disciplinary specific repositories may provide the best support for scientific data and larger datasets, with a link to that data in the BC Institutional Repository for best access.
  3. How long will data be kept beyond the life of the project?
  4. Who will manage and administer the preserved data?
    Tips: PI or other with designated role will be responsible for retention of the project data.