We present the training dataset of the MICCAI-MIDOG 2021 challenge. The task of the challenge is the generalization of the detection of mitotic figures to multiple microscopy whole slide image scanners. The data set consists of 200 cases of human breast cancer. Each of the images was cropped from a whole slide image. The region for cropping was selected by a pathologist according to current guidelines. All images come from the same lab (UMC Utrecht) and have thus similar staining intensity, i.e. all visible differences in representation can be attributed to a different digital representation by the acquisition device. The images 001.tiff to 050.tiff were acquired with a Hamamatsu XR scanner. The images 051.tiff to 100.tiff were acquired with a Hamamatsu S360 scanner. The images 101.tiff to 150.tiff were acquired with an Aperio CS2 scanner. The images 151.tiff to 200.tiff were acquired with a Leica GT450 scanner. The cases of all scanners represent a similar distribution of tumor grades. The complete collection of cases represents consecutive cases from the archive that were qualified according to inclusion criteria. The file MIDOG.json represents all annotations in MS COCO format. Please note that we did include not only mitotic figure annotations but also annotations where experts disagreed or that qualify as hard examples for machine learning. The challenge description can be found at http://doi.org/10.5281/zenodo.4573978.