Land managers increasingly need to collect, store, and analyze monitoring and assessment data sets that include multiple methods and occur over multiple years. For this reason, databases are becoming increasingly important for managing large monitoring and assessment data sets. The Database for Inventory, Monitoring, and Assessment (DIMA) is a highly customizable software tool for data collection, management, and interpretation. DIMA is a free Microsoft Access database that can be used easily without extensive knowledge of Access. i All that is needed to run DIMA is a PC computer with a copy of Microsoft Access. Data can be entered for common, nationally accepted, vegetation- and soilmonitoring methods in the fi eld using a tablet PC (touchscreen entry) or in the offi ce on a standard computer (keyboard entry) with user-specifi ed choice lists. DIMA can easily be customized to suit the user’s needs. DIMA was originally designed as an accompaniment to the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems 1,2 (i.e., the Monitoring Manual). Just as the Monitoring Manual outlined a consistent approach to many fi eld methods, DIMA provides a consistent approach to the data collection of those methods. Initially, DIMA supported only a few core vegetation- and soil-monitoring methods, but it has since grown to hold the quantitative data collected according to the standard methods outlined in the Monitoring Manual, the qualitative data collected according to Interpreting Indicators of Rangeland Health, 3 and data collected using other nationally recognized methods (Table 1). Finally, data for developing Natural Resources Conservation Service (NRCS) ecological site descriptions can be collected using DIMA starting at a low intensity (e.g., general site characteristics, waypoints, and photos) and building to a high intensity (e.g., detailed soil and vegetation data). In this article, we explain how DIMA facilitates the collection, management, and interpretation of fidata (Fig. 1), helping users make informed decisions, think about data in new ways, and link that data to other sources.