A data management plan will save time and confusion throughout your research process by anticipating potential challenges and recording your decisions. It will also help stakeholders, such as funders, collaborators, and future researchers, understand your process.
The exact contents and format of a data management plan will vary depending on the funding agency, provided the research is grant funded. Some funders even provide their own templates. In general, a plan contains information such as:
Funders may also require that you follow FAIR Principles. These guidelines ensure that research data is Findable, Accessible, Interoperable, and Reusable. The costs associated with conducting research are high. Sharing your research data and its documentation not only ensures reproducibility of your own results but also enables your data to be used in future studies.
"The DMP Tool is a free, open-source, application that helps researchers create data management plans (DMPs). These plans are required by many funding agencies as part of the grant proposal submission process. The DMP Tool provides a click-through wizard for creating a DMP that complies with funder requirements. It also has direct links to funder websites, help text for answering questions, and data management best practices resources."
"The Turing Way is an open science, open collaboration, and community-driven project. We involve and support a diverse community of contributors to make data science accessible, comprehensible and effective for everyone. Our goal is to provide all the information that researchers, data scientists, software engineers, policymakers, and other practitioners in academia, industry, government and the public sector need to ensure that the projects they work on are easy to reproduce and reuse."
"This section forms the core of this handbook. It gives concrete, detailed advice on how data holders can open up data. We’ll go through the basics, but also cover the pitfalls. Lastly, we will discuss the more subtle issues that can arise."
"Best Practice: Plan - Map out the processes and resources for the entire data life cycle. Start with the project goals (desired outputs, outcomes, and impacts) and work backwards to build a data management plan, supporting data policies, and sustainability plans."