The dataset consists of cine and real-time images from 15 healthy volunteers (7 males; 8 females). All images were acquired in supine position using a 32-channel cardiac surface receiver coil at 3 T (Skyra, Siemens Healthineers, Germany). Conventional imaging at rest included a balanced steady-state free precession (bSSFP) ECG-gated cine sequence to create a short-axis stack covering the entire heart including both ventricles and atria. Real-time CMR data acquisition was performed during free-breathing and without ECG-synchronization at rest and under two different levels of exercise stress. The dataset includes automatically created contours (comDL) using Medis (version 4.0.56.4, QMass® 8.1, Medical Imaging Systems, Leiden, Netherlands) for all images, as well as manually corrected (mc) contours based on the comDL contours for all cine and real-time measurements at rest and under exercise stress for end-diastolic (ED) and end-systolic phases (ES). The dataset also includes segmentation masks in NIfTI format for cine and real-time CMR at rest and under exercise stress created with nnU-Net (DOI:10.1038/s41592-020-01008-z) with freely available weights based trained on the dataset of the cardiac segmentation challenge "Automated Cardiac Segmentation Challenge" (ACDC) (DOI:10.1109/TMI.2018.2837502). To minimize the influence of respiratory motion on clinical measures, images in the ED and ES phase of the cardiac cycle during end-expiration were manually selected for each slice. The dataset includes indices for these images for real-time CMR measurements at rest and under exercise stress. For intra-observer variability, manually corrected contours for the derivation of the clinical measures were created three to six months after the initial segmentation. Single images in the ED and ES phase during end-expiration were once again chosen from each slice. Image data is provided in a file format used by the BART toolbox. DOI:10.5281/zenodo.7110562