Two main types of mass spectral databases are available, public open datasets , like the public mass spectral libraries within the GNPS platform , and private libraries, like the NIST library (NIST). Whilst private libraries can be used for library searching if available within a research group, they typically do not share the library spectra in a format that can directly be used to train machine learning models. Even in cases where such libraries are used for model training, the lack of publicly shareable training data makes it hard for others to recreate the models or to analyse potential biases in the used training library. Open-source machine learning models are therefore created by using public libraries. Whilst we appreciate all the community efforts to build and expand public metabolomics libraries; unfortunately, these public libraries are often not of high enough, consistent quality to directly use for training machine learning models. This hampers an efficient and reliable training of machine learning models and demands considerable efforts related to data preparation. Currently, most machine learning endeavors lack reproducibility and build their cleaning pipelines from the ground up, often without providing sufficiently reusable code. Given the critical nature of library data curation, there is a pressing need for robust and reproducible procedures. Furthermore, a higher-quality mass spectral library will also provide higher-quality hits for library matching and analogue searching.