Recent work in image deblurring aided by inertial sensor data has shown promise. Separate work has also shown that deep learning techniques are useful for the image deblurring problem. Due to a lack of a proper dataset, however, deep learning techniques have not yet to be successfully applied to image deblurring when inertial sensor data is also available. This paper proposes to generate a synthetic training and testing dataset that includes groundtruth and blurry image pairs as well as inertial sensor data recorded during the exposure time of each blurry image. To simulate the real situations, the proposed dataset called DeblurIMUDataset considers synchronization issue, rotation center shift, rolling shutter effect as well as inertial sensor data noise and image noise. This dataset is available online.