“Data management is the compilation of many small practices that make your data easier to find, easier to understand, less likely to be lost, and more likely to be usable during a project or ten years later.”– Kristin Briney, Data Management for Researchers (2015)

Effective data management takes conscious planning at each step of the data lifecycle, from data creation through use, storage, and access. Data management is important to ensure research generated data doesn’t become forgotten, lost, or inaccessible. Making data available after publication allows other researchers to verify the analyses or use it in pursuit of novel discoveries. It also increases the impact of the research (1) and improves public trust in science (2,3). Moreover, there is a federal mandate for public access to data generated from research funded with federal dollars, and journals increasingly require data sharing as a condition of publication.

During project design, investigators should decide who will be responsible for the data generated every step of the way. For instance, if a student collects data, what happens to that data once the student has graduated? Who can access the data and is it understandable to novice users? Plans should be made for who will be in charge of storage, protection, and access to data, including the device(s) where the data is stored. This is where a data management plan comes into play.

A sound data management plan answers the following questions: