28 April 2026
Introduction
In recent work, new metadata standards, Minimum Information for Fermentation Experiments (MIFE) and Devices (MIFD), are introduced to address a growing challenge in industrial biotechnology: how to make complex fermentation data FAIR (Findable, Accessible, Interoperable, and Reusable).
As fermentation processes generate increasingly large and heterogeneous datasets, structured and standardized metadata becomes essential for ensuring transparency, reproducibility, and reuse.
Why FAIR data matters
The FAIR principles, Findable, Accessible, Interoperable, and Reusable, have become a cornerstone of modern scientific data management. In industrial biotechnology, their importance is particularly evident.
Fermentation experiments often involve multiple variables, including microbial strains, process conditions, sensors, and sampling strategies. Without standardized metadata, comparing results across experiments, or even reproducing a single study, can be extremely challenging.
MIFE and MIFD address this by defining structured metadata schemas tailored specifically to fermentation workflows. By clearly distinguishing between mandatory, recommended, and optional metadata, these models ensure that essential information is consistently recorded while still allowing flexibility for researchers.
In addition, the models are designed to align with existing ontologies (such as SOSA, SSN, and OBI), promoting interoperability and supporting broader adoption across the research community.
Their integration with tools such as the FAIR Data Station and FAIRDOM-SEEK further enables automated metadata validation and more seamless data sharing.
Figure 1: Flow diagram of how a user can utilise MIFE
From metadata to FAIR-by-design research
Standardized metadata goes beyond organization, it enables integration, scalability, and advanced data-driven approaches such as machine learning, digital twins, and explainable AI. By combining MIFE and MIFD with tools like the FAIR Data Station, researchers can work with consistent, machine-readable data across experiments and platforms.
Together, these developments mark a shift toward FAIR-by-design industrial biotechnology, where data management is embedded directly into the research workflow. This improves reproducibility, transparency, and collaboration, while unlocking the full potential of fermentation data in an increasingly data-driven field
Ultimately, these frameworks support researchers and industry in standardizing, sharing, and analyzing fermentation data at scale, accelerating innovation in bioprocess development.
Read the full paper: https://doi.org/10.1093/gigascience/giag038
