Owens, C. B.; Morse, B. S.; Darley, A. J.; Hart, T. J.; Webb, R.; Maggi, S.; Jensen, G. J.; Ward, M. M.; Reade, W. C.; Kaplan, M.; Hart, G. L. W.

Understanding bacterial nanomachines like flagellar motors, which are crucial for pathogenic bacteria motility, is vital for microbiological and therapeutic research. Cryogenic electron tomography (cryo-ET) enables visualization of these structures within cells at near-native conditions. But manual identification remains challenging due to low contrast, limited resolution, and crowded in vivo environments. To address this, we introduce MotorBench, an expert-annotated dataset of bacterial flagellar motors that has been curated as part of a Kaggle competition BYU - Locating Bacterial Flagellar Motors 2025, engaging data scientists globally to create automated detection algorithms. MotorBench and its accompanying tools are intended to serve as a benchmark for evaluating and comparing future algorithms in automated cryo-ET analysis.