Code and lake polygon files used to generate LAGOS-US RESERVOIR, an extension modules of the LAGOS-US platform for studying lakes in the United States. Although naturally-formed lakes and reservoirs are thought to differ in many properties, there is currently no data source that differentiates between lakes and reservoirs in the conterminous U.S. and reservoirs are understudied compared to natural lakes. This absence of data stems from how challenging it is to identify reservoirs at broad scales -- there is a wide variety of dam types and sizes that results in various reservoir shapes and sizes, making a simple classification difficult. The LAGOS-US RESERVOIR data module fills these data and the resulting knowledge gaps by classifying lakes greater than or equal to 4 hectares in the conterminous U.S. (137,465 lakes) into one of two classes: natural lakes (NL) or reservoirs (RSVR). The RSVR and NL classification is based on high resolution imagery and model predictions. Using visual interpretation of imagery, we define RSVRs as lakes that are likely to be either human-made or highly human-altered by the presence of a relatively large water control structure that significantly changes the flow of water. Likewise, NLs are lakes that are likely to be either naturally-formed or do not have a relatively large, apparently flow-altering structure on or near it. We trained machine learning models using 12,162 manually (i.e., visually) classified lakes. We then used these models to assign NL or RSVR predictions to the remaining 77,667 NLs and 59,798 RSVRs. RESERVOIR also includes model-based prediction probabilities and variables that are commonly used when studying reservoirs (e.g., lake shape). These data can be used for studying reservoirs at the regional to conterminous U.S. scale.