Digitalization of biocatalysis: Best practices to research data management

The digitalization of biocatalysis presents significant opportunities for advancing research by improving data management, fostering transparency, and enabling more efficient, reproducible experiments. However, this transformation brings challenges, particularly in standardizing and sharing data across diverse platforms and laboratory settings. Managing experimental data and metadata in structured, machine-readable formats is fundamental for integrating automation, while mechanistic modeling and artificial intelligence applications further benefit from well-curated datasets. Creating sustainable, reusable software is also key to the long-term success of biocatalysis projects. Yet, efficient data acquisition remains limited by the lack of universally accepted data formats for analytical instruments. To address these barriers, the best practices presented here focus on optimizing biocatalysis workflows for the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. This includes adopting standardized data exchange formats and sharing reproducible datasets in public repositories, thus enhancing interoperability and reusability. By following these guidelines, researchers can contribute to the digitalization of biocatalysis, facilitating the knowledge sharing and data reuse necessary to support the transition of biocatalysis into a more data-driven field.