Data used for training in paper "A New Cosmic Ray Rejection Routine for HST WFC3/UVIS4 via label-free training of deepCR"

deepCR is a deep-learning-based cosmic ray (CR) rejection framework originally presented by Zhang & Bloom (2020). The original approach requires a dedicated training set that consists of multiple frames of the same fields, enabling automatic CR labeling through comparison with their median co-adds. Here, we present a novel training approach that circumvents the need for a dedicated training set, but instead utilizes dark frames and the science images requiring CR removal themselves. During training, CRs present in dark frames are added to the science images, which the network is then trained to identify. In turn, the trained deepCR model can then be applied to identify CRs originally present in the science images. Using this approach, we present a new deepCR model trained on a diverse set of Hubble Space Telescope (HST) images taken from resolved galaxies in the local group, which is universally applicable across all WFC3/UVIS filters. We further evaluate the performance of the deepCR model on the Panchromatic Hubble Andromeda Southern Treasury (PHAST) survey and provide insights into how users can robustly determine the threshold for generating binary cosmic-ray masks from deepCR probability map predictions.