Second, while some ML-ready datasets are available on platforms such as Kaggle 10 or in supplementary tables of publications, they are often difficult to find and lack long term maintenance and support post-publication. While there is no formal dataset consensus in the field, there are certain datasets that are often used for training such as ProteomeTools. Nevertheless, there are no widely used datasets used to compare the performance of tools developed by different researchers, making it difficult for new algorithms to be evaluated and compared to older tools. This issue is only further exacerbated by individual groups relying on different pre-and post-processing protocols, such as normalization of measurements and re-scoring of PSMs. As an outcome of the 2022 Lorentz Center workshop on Proteomics and Machine Learning, we have created a web platform to facilitate the application of ML approaches to the field of MS-based proteomics. The resource is intended to provide a central focal point for curating and disseminating datasets that are ready to use for ML research, providing benchmark datasets for comparing different approaches, and encouraging new entrants into the field through expert-driven tutorials and other teaching materials.