Imaging Cytometry and Kinetic Modelling Repository This repository contains the complete processed dataset, trained machine learning models, and reproducible analysis framework supporting the study: “Image Cytometry and Kinetic Modelling Reveal How Aged Leaf Biomass Dose Regulates Microbial Physiology and PAH Degradation.” Study Overview This dataset integrates: First-order PAH degradation kinetics Imaging flow cytometry–derived single-cell morphological features Supervised machine learning classification of microbial physiological states Probability calibration and deployment thresholds The repository enables full reproduction of: Viable vs debris classification Single vs aggregate discrimination Fluorescence-defined state classification Morphology–kinetic correlation analyses Repository Contents ✔ Processed feature dataset (~4.5 × 10⁵ segmented cellular objects) ✔ Trained models (.joblib) ✔ Threshold calibration file ✔ Inference script (predict.py) ✔ Reliability calibration outputs ✔ Version-locked dependency file Computational Environment Python 3.11 scikit-learn 1.4.2 NumPy 1.26 Pandas 2.2 Matplotlib 3.8 Purpose This repository provides a reproducible framework linking amendment dosage, microbial physiological integrity, and PAH degradation kinetics through morphology-derived phenotyping.