FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science

Abstract

Biocatalysis is entering a promising era as a data-driven science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and data-driven modeling to design improved biocatalysts and bioprocesses. However, our laboratory routines and our scientific practice of communicating scientific results are insufficient to ensure the reproducibility and scalability of experiments, and data management has become a bottleneck to progress in biocatalysis. In order to take full advantage of rapid progress in experimental and computational technologies, biocatalytic data should be findable, accessible, interoperable, and reusable (FAIR). FAIRification of data and software is achieved by developing standardized data exchange formats and ontologies, by electronic lab notebooks for data acquisition and documentation of experimentation, collaborative platforms for developing software and analyzing data, and repositories for publishing results together with raw data. The EnzymeML platform provides reusable and extensible tools and formats for FAIR and scalable data management in biocatalysis. FAIRification of data and software and the digitalization of biocatalysis are expected to improve the efficiency of research by automation and to guarantee the quality of biocatalytic science by reproducibility. Most of all, they foster reasoning and creating hypotheses by enabling the reanalysis of previously published data, and thus promote disruptive research and innovation.

The Development and Opportunities of Predictive Biotechnology

“…These benefits include performing analytics, standardizing processes, digitizing, and virtualizing results and data sets, and storing them in the cloud, enabling their simultaneous global use. [62] This can successfully shorten product development times, as was the case for biopharmaceuticals and vaccines in the COVID-19 pandemic.…”