In response to the reproducibility crisis, the scientific community is developing and adopting new guidelines for reporting experimental protocols and statistical analysis. Scientific journals are responding accordingly , and there has been a recommendation to modify the academic reward system by recognising scientists who aligned with best practices for reproducible research . Initiatives such as the German National Research Data Infrastructure develop an infrastructure for standardised research data exchange , the Standards in Laboratory Automation consortium (SiLA) provide a framework for the exchange, integration, sharing, and retrieval of electronic laboratory information (), and data repositories such as Zenodo and Dataverse enable data sharing . Efforts in standardization and data reproducibility have been long established in other 'omics fields, with standard exchange formats for transcriptomics , proteomics , and metabolomics data becoming increasingly developed and adopted over the last twenty years. However, in biocatalysis and enzymology exchange standards or software support to aid data analysis, management, and sharing is still absent, and raw experimental data such as the time dependency of substrate or product concentration, derived data such as kinetic parameters, and metadata such as reaction conditions or the kinetic model are typically reported in plain text, figures, or tables .