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MIFE and MIFD: Minimum information for fermentation experiments and devices

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Abstract Background As the technological advancements of the early 21st century are pushing industrial biotechnology (IB) into the realm of Big Data–driven innovation, the requirement for trustworthy …Big Data–driven innovation, the requirement for trustworthy data management, annotation, and standardization is emerging as a necessity. Minimum information models (MIMs) have long been used across disciplines as the backbone of good data management practices by providing the scaffold upon which standardized recording of metadata can adequately and succinctly describe an understudied phenomenon. Findings Here we present a minimum set of metadata, named the minimum information for fermentation experiments (MIFE) and devices (MIFD), that has been specifically designed to accommodate the data management and annotation needs of IB-related fermentation experiments. Although the proposed schema is tailored to IB applications, MIFE and MIFD build upon well-established models and community standards to facilitate easier integration with existing infrastructure and easier adoption by the community, as well as aim to integrate Findable, Accessible, Interoperable, and Reproducible (FAIR) principles in the IB field. In addition, the integration with FAIR Data Station (FAIR DS), a tool that offers metadata validation and enables the automated uptake of (meta)data from data management repositories such as FAIRDOM-SEEK, is showcased. The proposed models are accompanied by a Python package that enables their programmatic use by creating a Linked Data Modeling Language (LinkML) schema that can fuel subsequent analyses. Conclusions Through the promotion and simplification of knowledge discovery, we believe that MIFE and MIFD can accelerate the application of state-of-the-art artificial intelligence (AI) methods and the adoption of explainable AI to better understand bioprocesses at scale.

Authors: Georgios K Georgakilas, Brett Metcalfe, Ariane Bize, Matthew Crowther, Emilie Fernandez, Susana Maria Alonso Villela, Stuart Owen, Rudolf Wittner, David Camilo Corrales, Anselm von Gladiss, Peter Blomberg, Munazah Andrabi, Cesar Arturo Aceves Lara, Hans Mattila, Marily Wiebe, Theodore Dalamagas, Jasper J Koehorst

Date Published: 2026

Publication Type: Journal

STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models

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STAMM (Soft sensor moniToring and mAintenance framework for Machine learning Models) is an open-source MLOps framework for industrial machine-learning soft sensors. Unlike general-purpose MLOps platforms … (MLflow, Kubeflow, Metaflow, ClearML), STAMM targets the regime where ground-truth labels arrive offline hours or days late, processes exhibit slow non-stationary dynamics, and models are multi-language. The framework comprises five loosely coupled components: a time-series database, workflow orchestrator, language-agnostic REST model registry, dashboard with human-in-the-loop labelling, and an extensible drift-detection package. STAMM was validated on an industrial-scale fed-batch penicillin fermentation (IndPenSim) with seven coexisting R and Python soft sensors served through the model registry.

Authors: Carlos Suarez, Alexander Astudillo, Brett Metcalfe, Matthew Crowther, Jasper J. Koehorst, Esteban Castillo, Ariane Bize, David Camilo Corrales

Date Published: 1st Sep 2026

Publication Type: Journal

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