The LAGOS-US RESERVOIR data module (hereafter, RESERVOIR) classifies all 137,465 lakes > 4 hectares in the conterminous U.S. into one of the following three categories using a machine-learning predictive model based on visual interpretation of lake outlines and a classification rule based on lake shape. Natural Lakes (NLs) are defined as lakes that are likely to be entirely or mostly naturally-formed and that do not have large, flow-altering structures on or near them; Reservoir Class A’s (RSVR_A) are defined 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 appears to significantly change the flow of water; and Reservoir Class B’s (RSVR_Bs) are lakes that are likely to be entirely human-made based on isolation from rivers and a highly angular shape that is rarely, if ever, seen in natural lakes also often. We trained the machine learning models on 12,162 manually-classified lakes to assign probabilities of a lake being in 1 of 2 of the categories (NL or RSVR), then we further classified the RSVR classification into either A or B based on NHD Fcodes, isolation, and angularity. The data module includes a detailed User Guide, metadata tables, and a data table that includes information such as location, lake geometry, surface water connectivity class, and official name. Using our definition, our classification indicates that over 46 % of lakes > 4 ha in the conterminous U.S. are reservoir lakes. These data can be combined with other LAGOS-US data modules and U.S. national databases using unique lake identifiers to study both reservoir lakes and natural lakes at broad scales.