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). - 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: