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Data Management Plans (DMP)

learning-objectives

• Describe the purpose of a data management plan
• Describe the important elements of a data management plan
• Use a self-assessment to design a data management plan

“A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed.[1] The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future.”

(Source: https://en.wikipedia.org/wiki/Data_management_plan)

Example DMP

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Why bother with a DMP?

How would you answer?

Do you have a data management plan? If so, how do you use it?

“Those who fail to plan, plan to fail”

Returning to the assertion that data (and its value) is at the foundation of your science, working without a data management plan should be considered scientific misconduct.

Those are strong words. And while we might have an intuition of the boundaries of research ethics - data mismanagement seems more like an annoyance than misconduct. However, if your mismanagement leads to error in your research data, or the inability to make publicly-funded research open to the public, these are serious consequences. Increasingly, funders realize this.

Stick:

• You have to make one
• Reviewers definitely look at them, but they may not be enforced.

Carrot:

• Make your life easier
• Planning for you project makes it run more smoothly
• Avoid surprise costs

Elements of a good DMP

• Information about data & data format(s)

  • data types
  • data sources
  • analysis methods
  • formats
  • QA/QC
  • version control
  • data life cycle

• Metadata content and format(s)

  • format
  • standards

• Policies for access, sharing, and re-use

  • funder obligations
  • ethical and privacy issues (data justice)
  • intellectual property, copyright, citation
  • timeline for releases

• Long-term storage, data management, and preservation

  • which data to preserve
  • which archive/repository

• Budget (PAPPG)

  • each of the above elements cost time/money
  • Personnel time for data preparation, management, documentation, and preservation (including time)
  • Hardware and/or software for data management, back up, security, documentation, and preservation (including time)
  • Publication/archiving costs (including time)

Not only what, but who (roles).

Extra challenges for collaborative projects.

Machine actionable DMPs

• DMPs describe research methods that will evolve over the course of a project
• to be a useful tool for researchers and others, the content must be updated to capture the methods that are employed and the data that are produced

(Source: https://doi.org/10.1371/journal.pcbi.1006750.g002)

Tools for DMPs

Exercise

Thinking about a dataset you work with, complete the Data Stewardship Wizzard.

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References and Resources

• NSF Guidelines on DMPs
• https://dmptool.org/general_guidance
• https://dmptool.org/public_templates
• Professional and scholarly societies, e.g., theEcological Society of America http://www.esa.org/esa/science/data-sharing/resources-and-tools/
• DataOne - https://www.dataone.org/best-practices
• Data Carpentry - http://datacarpentry.org/
• The US Geological Survey http://www.usgs.gov/datamanagement/index.php
• Repository registry (and search) service: http://www.re3data.org/
• Your university library

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Fix or improve this documentation:

• On Github: Github Repo Link
• Send feedback: Tutorials@CyVerse.org

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