Data availability presents another critical challenge, as highlighted by the partial reliance of the main MIST model on a combination of public and proprietary datasets, which restricts the full replicability of its reported performance. The increasing reliance of state-of-the-art machine learning models on large-scale datasets underscores the pivotal role of data availability in driving progress within the field of metabolomics and beyond. This scenario suggests that future advancements may hinge not only on algorithmic innovation but also significantly on the accessibility of comprehensive and accurate datasets.