This upload contains open source AAPM thoracic auto-segmentation data (http://aapmchallenges.cloudapp.net/competitions/3) augmented with physics-based data augmentation technique introduced in this paper:https://iopscience.iop.org/article/10.1088/1361-6560/abe2eb . The original data contained thoracic planning CT along with organs-at-risk segmentation masks for Esophagus, heart, lungs and spinal cord. The physics-based augmentation pipeline was used to convert planning CT images to pseudoCBCT (psCBCT) images which are routinely used in weekly radiotherapy treatment sessions for cancer patients. Further geometric data augmentations are also applied to convert one planning CT/OAR dataset into 23 perfectly paired CT/psCBCT/OAR pairs. This dataset has been used to train deep learning models for organs-at-risk segmentation from psCBCT images and multitask simultaneous CBCT to CT translation and segmentation tasks.