This project provides a large-scale training dataset for DDA-BERT, an end-to-end transformer-based deep learning model developed for PSM rescoring in data-dependent acquisition (DDA) proteomics. The dataset comprises curated PSMs generated using established database search engines, including FragPipe, Sage, and AlphaPept, and is intended to support the training of the DDA-BERT model.