A Data Management Plan (DMP) is a formal document that describes how you will handle your data during the course of your research and at the conclusion of your study or project.
A Data Sharing Plan (DSP) is a document that describes how you plan to disseminate your data at the conclusion of a research project.
These two documents may be separate or combined, and DMP and DSP are often used interchangeably.
Writing a Data Management Plan - Informational Bulletin from the University of Michigan Library.
DMP Tool (DMP Creation Wizard)
Guidelines for Effective Data Management Plans
Framework for Creating a Data Management Plan
Elements of a Data Management Plan
UC San Diego Sample NSF Data Management Plans
This Guide from UM includes templates for creating Data Management Plans for many of the important funding agencies in engineering research. Including NSF, DoE, and DOD, NASA, and DOT.
Sharing data is one way that researchers contribute knowledge and build up the scientific record. When data is shared, research can be replicated and new research to be conducted with the data. Additionally, if the data is collected in a standardized way, it can be combined with other data sets to help inform larger-scale research. It is useful to consider if and how you will share your data prior to beginning your project since it can affect the methods you use to collect your data. Here are some things to consider when sharing your data.
What data can/will be shared?
Will de-identified individual participant data be made available?
What other documents need to be shared to make this data usable?
When will the data be available?
Will access to the data be restricted in any way?
How will the data be made available?
Additionally, many funding agencies and respected journals require data to be shared in order to receive funding or have your paper published. Learn more about these requirements under the "Data Management/Sharing Plan" page of this guide.
Summary from Ann Arbor
Although DMP requirements vary by funding agency, your plan will typically need to address the following topics:
Summary copied from https://guides.lib.umich.edu/datamanagement/planning
Roles & Responsibilities: Often a DMP will require an accounting of who will accept responsibility for tasks associated with data management. DataONE offers best practices in assigning roles and responsibilities.
Expected Data: Be able to describe (roughly) how much data your project will generate, at what rate, and if it will be generated as multiple data sets. Note also how much of that data will be retained and why.
Metadata and Standards: Think about how you will describe the data in such a way that you and others will find meaningful. Often there are industry or community standards that should be used to ensure that your data can work with other data and are accessible by other researchers.
Formats: Describe the file formats of your data. Whenever possible, data should be stored in stable, non-proprietary formats, preferably those based on open and published standards.
Retention: Think about how long you want your data to be available. Often there is a minimum requirement by the funding agency of 3, 5, or more years.
Dissemination: Explain if, how, and when your data will be available. You may be able to offer the data from a repository like Deep Blue Data, or you may need to require a request to the project PI.
Preservation: A key part of any DMP is describing how you will preserve your data. It is good to plan on several preservation and back-up options. When putting your data in a repository, note their commitment to long-term preservation and curation of the data.
Summary copied from https://guides.lib.umich.edu/c.php?g=492693&p=3370800