Unfortunately, creating a high quality gold standard set of labeled spectra can be tricky. One challenge is ensuring that the search strategy employs appropriate parameters. For instance, one widely used benchmark dataset used a search strategy that failed to account for missassigned isotopic peaks during the acquisition stage. This error led to frequently assigning a deamidation modification, when the observed mass shift was better explained by an isotopic mass shift on the precursor m/z . A second challenge relates to the notion of train/test leakage, in which information used to train the model leaks into the evaluation procedure. In the de novo setting, a common mistake is to randomly segregate a given set of labeled spectra into training and test sets, without regard to the associated peptides. As a result, spectra generated by the same peptide sequence may occur in both the training and test sets. Such duplicated peptides give an unfair advantage to the sequencing method, and the leakage will be even more useful to parameter-rich methods that are capable of memorizing many features of the training data.