Building reaction prediction tools requires access to large datasets for training. Historically, researchers have accessed proprietary in-house datasets or acquired the data through commercial databases such as Reaxys . The advantage of commercial databases is both the scale of the datasets available (often millions of reactions) and the annotation already completed by the publishers. Yet, these datasets are not freely available to ML practitioners, stymieing advances in reaction condition prediction in both academia and industry.