Title: Dataset Curation Beyond Accuracy. Abstract: Neural networks are known to be data-hungry, and collecting large labeled datasets is often a crucial step in deep learning deployment. Researchers have studied dataset aspects such as distributional shift and labeling cost, primarily using downstream prediction accuracy for evaluation. In sensitive real-world applications such as medicine and self-driving cars, not only is the accuracy important, but also the calibration -- the extent that model uncertainty reflects the actual correctness likelihood. It has recently been shown that modern neural networks are ill-calibrated. In this work, we take a complementary approach -- studying how dataset properties, rather than architecture, affects calibration. For the common issue of dataset imbalance, we show that calibration varies significantly among classes, even when common strategies to mitigate class imbalance are employed. We also study the effects of label quality, showing how label noise dramatically increases calibration error. Furthermore, poor calibration can come from small dataset sizes, which we motive via results on network expressivity. Our experiments demonstrate that dataset properties can significantly affect calibration and suggest that calibration should be measured during dataset curation.