Computational predictions of analyte behavior in the context of mass spectrometry (MS) data have been explored for nearly five decades, with early rudimentary predictions dating back to 1983. With the rise of technology and computational power, machine learning (ML) approaches were introduced into the field of proteomics in 1998 and ML-based models quickly overtook human accuracy. Since then, dozens of articles have described efforts to train models for a range of physio-chemical properties associated with the field of highthroughput proteomics, as reviewed by Neely et al. (submitted, this issue). While many efforts are still in the realm of basic exploratory research, ML approaches are increasingly being incorporated into mainstream tools and standalone predictive resources. When training any ML model it is key to have suitable training and evaluation datasets. Likewise, in many fields of research where ML is applied, it is common to have a range of educational datasets, such as MNIST or IRIS, allowing newcomers to the field to easily learn common ML methodologies. Likewise, state-of-the-art (SOTA) models can use benchmarking datasets such as ImageNet or those available on the UCI Machine Learning Repository to compare their predictive capabilities. Similar to how the length of iris petals or the numbers of survivors of the Titanic have been modeled close to 50000 times 7 , we seek to define proteomics datasets that can provide an entry point for ML modeling.