(2) Fragmentation intensity. While it is easy to calculate the m/z values of theoretical peptide spectra, fragment ion peak intensities follow complex patterns that can be hard to predict. Nevertheless, these intensities can play a key role in accurate peptide identification. For this reason, fragment ion intensity prediction is likely the second most explored topic, and which is why we choose to implement comprehensive datasets and tutorials for this data type. Since there are many attributes of peptides that affect their fragmentation patterns, the preprocessing steps of fragmentation data are more complex, and can be substantially different from lab to lab. For this reason, we have composed two separate tutorials, one that mimics the Prosit data processing approach on the ProteomeTools datasets, and one that mimics the MS 2 PIP data process on a consensus human HCD dataset. For datasets in this category it is difficult to provide a simple format with unified columns, as the handling and preprocessing steps differ significantly from model to model. Currently, there is one tutorial available on ProteomicsML describing the data processing pipeline from raw file to Prosit-style annotation, and we believe that with future additions we can provide users with tutorials for additional processing approaches.