Existing tools for cleaning reaction data are primarily targeted at retrosynthesis and forward prediction tasks and have somewhat limited extensibility, given that they are built to take as inputs CSV files or the stationary XML files of the US patent (USPTO) dataset instead of the outputs of continuously updated databases such as ORD . Furthermore, in the original publications, there is little to no discussion of how decisions made during cleaning (e.g. restricting the number of components in a reaction or the minimum frequency of occurrence) impact the datasets being cleaned or performance of models trained on the datasets. We believe that this is in part due to data cleaning historically being viewed as a "low value" task, and therefore not adequately discussed and published on. USPTO, being the largest open-source chemical reaction dataset, has been cleaned a number of times for different learning tasks. For example, the USPTO-50K and USPTO-MIT datasets are commonly used for benchmarking single-step retrosynthesis and forward predictions models , and these benchmarks are available in aggregate benchmarking sets such as the Therapeutics Data Commons (TDC) . However, the code used to process the raw data to generate the aforementioned USPTO benchmarks was not published and, there is no publicly available benchmark for reaction condition prediction extracted from these datasets.