Influence of Process Parameters on Bioaerosol Emissions in Vortex-Driven Flows: An Experimental and Machine Learning Approach

This dataset supports the manuscript “Influence of Process Parameters on Bioaerosol Emissions in Vortex-Driven Flows: An Experimental and Machine Learning Approach.” It contains anonymised experimental data used to develop and validate Random Forest and XGBoost models for predicting microbial aerosol concentrations. The dataset includes key parameters such as rotational speed (RPM), viscosity (mPa·s), liquid height (mm), temperature (°C), and stirrer size (mm). Data are raw and unscaled; no fixed train/test split is provided since the models employed K-fold cross-validation. The dataset adheres to FAIR principles and may be freely used for research under the Creative Commons Attribution 4.0 (CC-BY-4.0) license.