Combining and curating data from multiple open natural product collections for COCONUT 2.0 has highlighted several issues with circular imports between databases. Many of these aggregate data from other collections and are often, in turn, aggregated themselves by other collections as well. This way, misclassified structures, like synthetic compounds or drug derivatives, or compounds with structural problems, such as valence errors, propagate across datasets and database versions. The incorporation of an audit trail feature and well-annotated provenance information in COCONUT enables users to identify and effectively resolve these problems through community curation. To assess the current curation state of COCONUT 2.0 (September 2024), the NPLikeness score distribution for all compounds listed in the dataset is given in Figure . The distribution is characterised by a multimodal pattern, with prominent peaks observed in the NPLikeness score ranges of -2 to 0, 0 to 1, and around 2. This suggests the coexistence of both natural products and non-natural products, or rather synthetic compounds, within the dataset. The substantial density below zero and the complex distribution pattern underscore the need for a comprehensive curation process to refine the database and remove non-natural products, address potential misclassifications, and ultimately improve COCONUT's reliability as a comprehensive natural product resource.