(4) Protein detectability. Modern proteomic methods and instrumentation are now routinely detecting and quantifying the majority of proteins thought to be encoded by the genome of a species. Yet even after gathering enormous amounts of data, there is always a subset of proteins that remains refractory to detection. For example, even through tremendous effort focused on the human proteome, the fraction of unobserved proteins has been pushed just below 10%. It remains unclear why certain proteins remain undetected, though machine learning has been applied to explore which properties most strongly influence detectability (as reviewed within). One can compute a set of properties for a proteome and then train a model using those properties based on real world observations of the proteins that are detected and the proteins that aren't detected. The model can be trained to learn which properties separate the detected from the undetected. Such a model has further utility to highlight proteins that have properties that should make them belong to the detected group, but yet are not, as well as proteins that should belong to the undetected group, and yet they are detected. To facilitate this we have included a dataset that is based on an extensive study of a proteome: the Arabidopsis PeptideAtlas. This dataset is based on the 2021 build, which has 52 datasets reprocessed to yield 40 million peptide-spectrum matches and good coverage of the Arabidopsis thaliana proteome. Proteins in the dataset are categorized as either "canonical", the strongest evidence of detection, or "not observed" if known peptides are not identified. Along with these class labels, the dataset contains various protein properties such as molecular weight, hydrophobicity, and isoelectric point that could be crucial for classification purposes. The dataset has an accompanying tutorial that illustrates how to analyze the data with a multilayer perceptron model to classify the observability of peptides.