The datasets used in this study and their respective sizes, given as the raw dataset size without filtering are shown in Table . To our knowledge, the combined dataset is the largest reported to date. To enable clarity in the task specific curation process, the reduction in size through extraction and validation, followed by duplicate removal has been shown. Extraction refers to the extraction of reaction templates from the reaction SMILES, and validation refers to the application of the extracted template to the product of the reaction from which it was extracted, to determine if the corresponding reactants can be generated. Duplicates were identified as reaction SMILES consisting of identical reactants, agents, and products, using an order invariant hashing scheme accounting for variance in atom-mapping as developed by the authors. Unique reaction templates were also identified in the same manner. The overlap of reaction templates extracted from the respective datasets was ascertained by using the in-built set methods in Python. We have observed that some of the noise associated with automatic template extraction originates from incorrect mapping, text-mining errors, and human-error from manual curation. There are several variations of these cases including, incorrect recording of functional groups, incorrect mapping of reactive components (i.e. substructures present in the reactive center may also be present in the solvent or reagents, for instance the incorrect mapping of an amine in both the reactant and base), accidental extension of alkyl chains, representation of catalysts and incomplete reactions, examples of which can be found in the supplementary information. Whilst our approach to curation can identify such inconsistencies and disregard their associated reactions, further efforts are required to improve catalyst representation, text-mining, template SMIRKS generation and atommapping.