Funders are increasingly requiring grant applicants to submit data management plans as part of their proposals. This guide describes what data management and data management plans are; how to write a data management plan; and how to execute it. Read on for a brief introduction to data management and use the tabs in the sidebar to get practical guidance.

What does it mean to manage your data?

Data management is a set of practices and infrastructures for collecting, storing, accessing, preserving, and sharing data that allow it to be used effectively and ethically. 

• Scenario 1: You are editing a draft manuscript from your lab, and one data point in a graph looks wrong. For this use case, well-managed data includes 1. a file storage system that makes it easy to find the data behind the figure and the code that produced the figure, and 2. documentation that describes exactly how that data was collected or generated.
• Scenario 2: You are writing a manuscript based on field work you completed last summer where you interviewed subjects and took pictures and video. Suddenly your computer crashes and won't turn back on. For this use case, well-managed data includes a physical or cloud-based copy of the data that is 1. in formats that you can transfer to and open on another computer, and 2. secure, so that participants' personal information is not compromised.

The details of data management are always discipline- and project-specific.

Why should you have a plan for managing your data?

• For your future self: Knowing where your data are and what they mean facilitates follow-up research and can dramatically decrease the burden of rerunning experiments, replying to reviewer comments, or otherwise revisiting research.
• For your collaborators: If you work with students or other collaborators, an ongoing time investment in well organized and documented data and workflows for sharing data will facilitate communication, training, and hand-off of research, as well as making team members more independent by decreasing reliance on a single individual's knowledge of their process.
• For the community: Funders increasingly require data management and sharing plans to encourage reproducible research and to make data available and usable by other researchers, in order to maximize the knowledge that can be derived from the data.
• For the public: Funders increasingly require data management and sharing plans to ensure research data is available and understandable to the public, who have often paid for the research and whom, in the case of human subject research, it is about.

Karen Hanson, Alisa Surkis and Karen Yacobucci. Data Sharing and Management Snafu in 3 Short Acts. YouTube. https://youtu.be/66oNv_DJuPc