This dataset accompanies the paper titled Data Models for Dataset Drift Controls in Machine Learning with Images that appeared in the Transactions on Machine Learning Research https://openreview.net/forum?id=I4IkGmgFJz @article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} } We make available two datasets. Raw-Microscopy: 940 raw bright-field microscopy images of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels). 5,640 variations measured at six additional different intensities (microscopy/images/raw_scale001-raw_scale0075) 11,280 images of the raw sensor data processed through twelve different pipelines (microscopy/images/processed_views) Raw-Drone: 548 raw drone camera images for car segmentation (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472. 3,288 variations measured at six additional different intensities (drone/images_tiles_256/raw_scale001-raw_scale075). 6,576 images of the raw sensor data processed through twelve different pipelines (drone/images_tiles_256/processed_views). Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper. The code repository for this project can be found at https://github.com/aiaudit-org/raw2logit