Description

Scientific progress increasingly relies on data that can be shared, interpreted, and reused across disciplines, institutions, and digital tools. Yet much experimental data in chemical and bioprocess engineering remains difficult to reuse because it is poorly documented, inconsistently structured, or lacks contextual metadata. In this challenge-based micro-module, students will explore how research data can be transformed into FAIR (Findable, Accessible, Interoperable, Reusable) and XAIR (Explainable and AI-Ready) data. Using a real case study from biocatalysis, participants will work with experimental datasets and learn how to structure, document, and enrich them using emerging data standards and reproducible workflows. Working in interdisciplinary teams, students will identify weaknesses in current data practices and develop strategies to turn heterogeneous laboratory data into reusable research assets. The challenge is to create datasets that are not only FAIR, but also interpretable by humans and machines, enabling modeling, collaboration, and future AI-driven scientific discovery.