This repository contains all the raw image or quantification datasets used for building or validating scCamAge, a transfer learning-based AI method for chronological age prediction of single yeast cells using phase contrast microscopic images. Folder Descriptions scCamAge Predictor Module: Main_camAge.tar.zip This folder contains refined phase contrast images of yeast single cells at ten critical time points, ranging from day 2 (young) to day 20 (aged) of chronological aging. Molecular Dyes-Based: Dyes.zip This folder is divided into three subfolders containing refined phase contrast images for TMRE, H2DCFDA, and FM™ 4-64FX. These images cover ten critical time points of chronologically aging yeast cells from day 2 (young) to day 20 (aged). Treatment-Based Method: Drugs.zip This folder includes three subfolders with refined phase contrast images of yeast cells treated with etoposide, azacytidine, and MG132 at different concentrations. Pro-Longevity Drugs and Knockouts: KOs.zip This folder contains refined micrographs of yeast cells treated with pro-longevity drugs (metformin, spermidine, and spermine) and genetic knockouts (pro or anti-longevity) at ten critical time points from day 2 (young) to day 20 (aged). Human Datasets: Human.zip This folder includes refined micrographs of human datasets, specifically the Camptothecin (CPT)-induced human fibroblast senescence dataset and replicative senescence dataset taken from Strum et al. (2022; DOI: 10.1038/s41597-022-01852-y). Trained Models: Model.zip This folder contains trained models for CamAge, molecular dyes-based methods, and treatment-based methods. Raw and Normalized Matrix: Raw_and_Normalized Matrix_RNA seq_And_Raw_metabolomics_data.xlsx This Excel file includes raw and normalized matrices for RNA sequencing and raw matrices for metabolomics analysis. Segmentation Weights: yeast_segmentation-master.zip This folder contains weights and scripts for yeast cell segmentation.