(1) Retention time. Due to retention time playing a major role in modern peptide identification workflows, it is one of the most explored properties in predictive proteomics. This is why we have provided multiple retention time datasets from various sources. We have combined several previously released ML-ready datasets -such as the Sharma et al. dataset from Kaggle and the DLOmix dataset -with a newly compiled multi-tiered dataset from the ProteomeTools synthetic peptide library. From the latter, we have generated datasets of three sizes: 100,000 data points (small), well suited for newcomers; (ii) 250,000 data points (medium), and (iii) 1 million data points (large), well suited for larger-scale ML training or benchmarking. As amino acid modifications can complicate the application of ML in proteomics, these three tiers do not contain any modified peptides. Nevertheless, to train models for more real-life applications, we have also included an additional dataset tier containing 150,000 oxidized peptides, as well as a mixed dataset containing 150,000 oxidized and 150,000 unmodified peptides. These datasets require minimal data preparation, although we still provide two distinct tutorials on methods to incorporate these datasets into deep learning (DL) based models. In addition to preprocessed data, we also provide a detailed tutorial that combines and aligns retention times between runs from MaxQuant evidence files. The output of this tutorial is a fully ML-ready file for retention time prediction.