Enzyme catalysis and enzymology provide a powerful toolbox for sustainable synthesis routes and innovative solutions for bio-based chemistry. A better understanding of cellular biochemistry and the comprehensive biochemical characterization of the desired enzymecatalyzed reaction enable novel approaches in enzyme engineering and process development. Standardization of reporting of enzymatic data and metadata is considered to be pivotal to accelerating bioprocess development and reducing costs , facilitating sharing, analysis, and reuse of data and thus enabling quality control and reproducibility of experiments . Therefore, a major challenge for enzymology and biocatalysis lies in the current practices of dealing with experimental data in academic laboratories . In most academic research groups, data acquisition, curation, and documentation are performed manually without a universally accepted standard across laboratories. Data and metadata are typically stored in ad hoc repositories, such as paper lab notebooks, spreadsheets in different formats, and semi-structured text files containing custom annotations. Experimental or computational data is often poorly annotated, lacking a complete description of the acquisition and analysis procedures, or associated metadata. Despite previous efforts to address these issues , raw data are rarely available in machine-readable, even less in machine-actable format, preventing their further analysis and third-party validation. As it stands, the process of data acquisition, data analysis, and documentation is time consuming and error-prone, as is the recovery and interpretation of legacy data in most academic laboratories. Consequently, both the quality and the completeness of data and metadata solely relies on the experimenter's expertise and care.