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
- thinking about and developing your strategy for issues such as data storage and long-term preservation, handling of sensitive data, data retention and sharing, early on in your research.
- anticipating legal, ethical and commercial exceptions to releasing data; deciding who can have access to data in the short and long term.
- estimating the costs of your research project, which can then be included in your project budget.
- These Plans can also help research students plan ahead for their project
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).
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: