Data Management Plan (DMP), is a formal document that outlines how data will be handled during and after a research project. Many funding agencies, especially government sources, require a DMP as part of their application processes.
DMP making your work efficient and creating more value for your data for yourself and others, during and after your research, for instance by working towards FAIR (findable, accessible, interoperable, reusable) data. Thinking about data management at the start of your project will ensure that you are well-prepared. Data Management Plan (DMP or DMSP) details how data will be collected, processed, analysed, described, preserved, and shared during the course of a research project.
DMP should include the following information:
You have to describe how new data will be collected or produced, and/or if you plan to use existing data; what standards, methodologies or software will be used if new data are collected or produced.
Describe how the data will be organized during the project, what metadata and documentation (e.g., methodology, methods of acquisition) will accompany the data in the project. Describe how the data will be collected, what procedures will be used to analyse the data, assumptions so that subsequent researchers can replicate the research. Provide potential users with the information needed to read and to interpret the data in the future.
Describe how and where the data will be stored, what backup procedures will be used to avoid data loss, what the security and protection of sensitive data looks like (procedures for access to data, define the data protection policy that has been implemented in the institution, designate a group of users with special rights to access data).
Specify information about owners of the copyright and Intellectual Property Right (IPR), requirements and procedures related to ethical aspects of the collected data (if the research was conducted with the participation of humans), rules of access to data. Determine which licences will be applied to the data.
Specify how and when data will be shared (as soon as possible, but at the latest at the time of publication of the respective scientific output). Explain how data will be selected for preservation, and where data will be preserved long-term (for example, a data repository or archive).
Specify who will be responsible for data management and what resources will be dedicated to data management and ensuring that data will be FAIR. Explain how the necessary resources to prepare data for sharing/curation have been costed in.