Supporting Data for Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy

Supporting data and images for the manuscript Corbetta, E, et al., Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy. Nat Commun 17, 225 (2026). https://doi.org/10.1038/s41467-025-68150-0 The data folder contains the relevant dataset for the results of the manuscript, organized as follows: 01_Manual_assessment: single-channel images with increasing uneven illumination used for manual assessment with the quality metrics (Figure 3 of the paper) 02_Training: training dataset for the classification model. (Original dataset from [2] available here: https://doi.org/10.1002/cem.2901) 03_Prediction_Dataset1-Head-and-neck: multimodal nonlinear measurements of human head and neck tissue (prediction dataset 1), provided by [3]. 03_Prediction_Dataset2-Cells: multimodal measurements of stained HEK293 cells (prediction dataset 2). 03_Prediction_Dataset3-Basic-Cell-culture-Demo: multimodal measurement of cell culture (prediction dataset 3), provided by [4]. 03_Prediction_Dataset4-Basic-Brain-Demo: single channel measurements of brain sections (prediction dataset 4), provided by [4]. 03_Prediction_Dataset5-Basic-Timelapse-Demo: single channel timelapse measurements of differentiating cells (prediction dataset 5), provided by [4] Folders contain a Source images subfolder with the original images used for evaluation and prediction, and a Source data subfolder with the EVEN output. Some folders contain additional data from the supporting infomation. The scan_size files contain the information on the tile size of each dataset for the correct computation of the metrics.