A frequently observed barrier to machine learning application is data pre-processing and cleaning. With SURF, reaction data is presented in a structured, both human and machine-readable format. Hence, SURF has shown to be a key enabler for several reaction prediction case studies at Roche . The use of SURF necessitated minimal data cleaning, mainly focusing on structural information validation and the exclusion of non-relevant columns. This approach allowed for the rapid extraction and analysis of reaction data using standard data science libraries. The SURF header convention as shown in Figure ensures reproducibility and allows for easy identification of relevant columns needed for model training.