Data Management Plan Resources
Read our guide on how to use DMPTool (a Penn supported tool) to collaborate on, organize, and improve your DMPs!
Slides from an introductory course on data management for scientific research. It outlines good practices for data management and answers questions such as "Who owns the project and its data?" via case studies. Takes a scientific approach to data management, but the concepts are applicable to anyone doing collaborative research.
This tutorial provides more information on ownership of data, access to data, retention, and potential problems.
Data Management Plan Features
While the requirements of each funding agency are different, there are several common elements to all. Listed are common elements that you should address in your DMP:
- Type(s) of data will you collect
- Format(s) of your data
- Amount of data
- Roles and responsibilities of the team for data management tasks
- Storage method & location during the project
- Security of the data (if applicable)
- Related software/tools/code necessary to make the data usable
- Documentation procedures to make data understandable to future users
- Metadata schemas or standards utilized (example: DublinCore; Common Data Elements)
- Data sharing/publishing plan
- Reuse restrictions - intellectual property rights & licensing
- Location of data post-project (example: subject specific data repository)
- Length of data retention post-project
