We present a comprehensive, time-resolved dataset of yeast cultivation dynamics to support data-driven bioprocess engineering and process analytical technology development. The dataset comprises 52 controlled bioreactor cultivations of two industrially relevant yeast species, Saccharomyces cerevisiae and Saccharomyces pastorianus, each conducted over 72 – 76 h at two scales (0.7 L and 10 L working volume) under systematic parameter variation. A total of 43 variables are provided, comprising 30 inline variables (30 s resolution) and 13 offline variables sampled up to three times per day, enabling multimodal characterization of cultivation dynamics. Importantly, the dataset preserves real experimental conditions, including measurement noise, outliers, and different types of missing data arising from sensor failures, transmission errors, and process interruptions. By retaining these non-ideal characteristics, the dataset enables realistic evaluation of modeling, monitoring, and control approaches. It provides a valuable resource for dynamic process analysis, system identification, soft sensor development, fault detection, and validation of advanced data-driven methods under practical operating conditions.