2. Project Data Management Planning

A data management plan (DMP) describes how you will manage data during a research project and what you will do with the data after the project ends. Research sponsors may have very specific requirements for what should be included in a DMP. In lieu of or in addition to those requirements, good plans address 11 key issues: (1) research context (e.g., what questions or hypotheses will be examined); (2) how the data will be collected and acquired (e.g., human observation, in situ or remote sensing, surveys); (3) how the data will be organized (e.g., spreadsheets, databases); (4) quality assurance and quality control procedures; (5) how the data will be documented; (6) how the data will be stored, backed up and preserved for the long-term; (7) how the data will be integrated, analyzed, modeled and visualized; (8) policies that affect data use and redistribution; (9) how data will be communicated and disseminated; (10) roles and responsibilities of project personnel; and (11) adequacy of budget allocations to implement the DMP. Several tips are offered in preparing and using the DMP. In particular, researchers should start early in the project development process to create the DMP, seek input from others, engage all relevant project personnel, use common and widely available tools, and adopt community practices and standards. The best DMPs are those that are referred to frequently, reviewed and revised on a routine basis, and recycled for use in subsequent projects.