The SURF enables direct data loading into machine learning models, as structural molecular features are captured through public compound identifiers, i.e., Chemical Abstracts Service (CAS) numbers, simplified molecular input line entry system (SMILES) or international chemical identifier (InChI) strings. This feature enables forward reaction prediction, supports the determination of useful reaction conditions and training of retrosynthesis prediction tools. Due to its structure, reaction databases and corresponding visualization can be easily built and harnessed. Moreover, SURF files enable scientists in the laboratory to efficiently track their reaction data, directly work with their data by conducting analyses, and make data-driven decisions for designing new experiments based on previous outcomes.