We are pleased to announce that our CMRxRecon-300 Dataset paper has been officially published in the journal Scientific Data (https://www.nature.com/articles/s41597-024-03525-4). We welcome you to cite it as follows: Wang C, Lyu J, Wang S, et al. CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI. Scientific Data, 2024, 11(1): 687. It is worth noting that the dataset published in this Scientific Data paper, while derived from the same population sample as the CMRxRecon2023 challenge, contains different data. The main differences are as follows: 1. The dataset released this time is more raw, as it has not undergone coil combine and POCS reconstruction. 2. The dataset released this time does not truncate the time frames for cine; however, please note that the last few frames may contain artifacts and signal loss issues. We have also posted an announcement on Nature Portfolio, and we would appreciate your help in spreading the word: https://communities.springernature.com/posts/release-of-k-space-mri-dataset-and-benchmark-to-advance-deep-learning-for-cardiac-imaging-cmrxrecon. Additionally, the preprint of our CMRxRecon2024 Dataset paper has been released, providing a detailed description of the data collection and preparation process: Wang Z, Wang F, Qin C, et al. CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI. arXiv preprint arXiv:2406.19043, 2024.