Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of background conductive heat flow. Heat flow maps were constructed using a custom-developed iterative process using weighted regression, where convectively influenced outliers were de-emphasized by assigning lower weights to measurements that are very different from the estimated local trend (e.g., local convective influence). The weighted regression algorithm is 2D LOESS (locally estimated scatterplot smoothing; Cleveland et al., 1992), which was used for local linear regression, and smoothness was controlled by varying the number of nearby points used for each local interpolation. Three maps are included in this data release, allowing comparison of the influence of measurement confidence: all wells are equal-weight, and two different published categorizations of measurement quality were used to de-emphasize low-quality measurements. Each map is an estimate of background conductive heat flow as a function of assumed data quality, and a point coverage is also provided for all wells in the compiled dataset. The point coverage includes an important new attribute for geothermal wells: the residual, which can be interpreted as the well?s departure from estimated background heat flow conditions, and the value of residual may be useful in identifying hydrothermal or groundwater influence on conductive heat flow.