S. aureus Kinetic Database: an in silico gamma-concept kinetic dataset of Staphylococcus aureus growth, survival and enterotoxin risk across 768 environmental conditions in cheese matrices

This dataset is the S. aureus Kinetic Database, an in silico kinetic table describing the predicted growth, survival and staphylococcal enterotoxin (SE) risk of Staphylococcus aureus across a structured grid of environmental conditions relevant to cheese. It is the companion kinetic resource to the Heterogeneous Matrix Profile Card (DOI 10.5281/zenodo.20678453) and was created as part of a study on biology-informed hybrid quantum-classical machine learning (HQML) for predicting S. aureus behaviour in heterogeneous cheese matrices. The table contains 9216 rows and 21 columns. Each row is one time point of one environmental condition. There are 768 unique environmental conditions, formed by crossing 12 temperature levels, 8 pH levels and 8 water-activity levels (12 x 8 x 8 = 768). Each condition is sampled at 12 time points spanning 0 to 720 hours (0 to 30 days), giving 768 x 12 = 9216 rows. The kinetic predictions are generated with a cardinal-parameter gamma-concept model for S. aureus. Environmental suitability is expressed through gamma factors for temperature, pH and water activity, whose product scales the maximum specific growth rate. Population trajectories are propagated from the initial inoculum to the maximum population density, with a limit of detection applied to flag decline below the detection threshold and a population threshold applied to flag SE toxin risk. The values are not raw laboratory measurements; they are model-generated using cardinal parameters and assumptions drawn from the predictive-microbiology literature. The dataset is provided in CSV and XLSX formats. Please note that we cannot guarantee accuracy of the information provided. We do our best to keep the dataset consistent but we do not take responsibility if any of the provided information turns out to be incorrect or incomplete. We do not recommend using this dataset for analyses or projects that require complete accuracy. Researchers are welcome to reuse the data for any project. Please cite this record upon use or when published. We encourage reuse under the same CC BY 4.0 License.