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How to write a data management plan

How to write a data management plan

Data management plans are an essential component of the Planning and Design Phase of the Data Lifecycle. A well crafted data management plan provides researchers with an opportunity to think about and develop a strategy for issues such as data storage and long-term preservation, handling of sensitive data, data retention and sharing

Components of a data management plan:

  • Data type 
    Briefly describe the scientific data to be managed or shared as well as a summary of the types and amounts of data to be generated. 
  • Related tools, software, and/or code 
    Indicate whether specialized tools are needed to access or manipulate the data and include the name(s) of the tool(s), as well as how to access the tool(s).
  • Standards
    Describe the standards that will be applied to the scientific data and associated metadata. Find an INCF endorsed standard for your work here.
  • Data preservation, access, and associated timelines
    State the plans and timelines for data preservation and access including:
    • The name of the repositories where data and metadata arising from the project will be archived. Find a FAIR repository to store your data and models here.
    • How the scientific data will be findable and identifiable
    • When the scientific data will be made available to others and for how long
  • Access, distribution, or reuse considerations 
    Describe any applicable factors affecting access, distribution, or reuse of scientific data related to:
    • Informed consent
    • Privacy and confidentiality protections consistent with laws and regulations
    • Whether access to human derived data will be controlled
    • Any restrictions imposed by laws or existing agreements
    • Any other considerations that may limit the extent of data sharing
  • Oversight of data management and sharing
     Indicate how compliance with the DMS will be monitored and managed

Important considerations for each phase of the data lifecycle:


Plan and design phase
  • Data management plans
  • Onboarding checklist
  • Documentation and metadata
  • Data use agreement
Collect and create phase
  • File naming conventions
  • Directory structure
  • Version control
  • Readme files
Analyze and collaborate phase
  • Electronic notebooks
  • Analysis ready datasets
  • Image management
  • Collaborative tools and software
  • Compute infrastructure
Evaluate and archive phase
  • Data destruction
  • Data retention
  • Intellectual property
  • Archives and records management
Sharing and dissemination phase
  • Data repositories
  • Data sharing
  • Scholarly products
  • Pre-prints and publishing
Access and reuse phase
  • Open access
  • Offboarding checklist
  • Reproducibility
Storage and management phase
  • Storage security
  • Data storage compliance
  • Technology, vendors and products