Droplet microfluidics with image texture quantification for detection of rare antibiotic-resistant subpopulations from the bloodstream infections dataset doi-10-17044-scilifelab-31452181-0 https://doi.org/10.17044/SCILIFELAB.31452181 Swedish National Data Service Svensk nationell datatjänst Landing page Droplet microfluidics with image texture quantification for detection of rare antibiotic-resistant subpopulations from the bloodstream infections dataset doi-10-17044-scilifelab-31452181-0 https://doi.org/10.17044/SCILIFELAB.31452181 Tenje, Maria 2024-06176_VR 2021-02091_VR 2024-00460_Vinnova 101043985 2019-00207_VR Swedish National Data Service Svensk nationell datatjänst Landing page This dataset contains experimental image data associated with the study “Droplet microfluidics with image texture quantification for detection of rare antibiotic-resistant subpopulations from bloodstream infections.” The data were generated at Uppsala University to support reproducibility, validation, and reuse of the experimental workflows and image analysis methods described in the associated publication. The dataset consists of time-lapse TIFF image sequences acquired from droplet microfluidic experiments designed to detect rare antibiotic-resistant and heteroresistant bacterial subpopulations at single-cell resolution. Experiments were performed using both Gram-positive and Gram-negative bacterial species under antibiotic exposure conditions. Quantitative image texture analysis was applied to monitor bacterial growth dynamics within droplets over time. The repository is organized into folders corresponding to the figures presented in the manuscript. Each folder contains the time-lapse TIFF images used to generate the associated figure. These image datasets can be used directly as input to the image processing and texture Associated Publication: “Droplet microfluidics with image texture quantification for detection of rare antibiotic-resistant subpopulations from bloodstream infections,” published in npj Digital Medicine. This work was supported by the Swedish Research Council (grant 2024-06176 to MT and grant 2021-02091 to DIA), Sweden’s Innovation Agency Vinnova (grant 2024-00460 to MT), and the European Research Council (ERC) under the European Union’s Horizon Europe research and innovation programme (PHOENIX grant agreement No. 101043985 to MT). The authors acknowledge Myfab Uppsala for facilities and experimental support. Myfab is funded by the Swedish Research Council (2019-00207) as a national research infrastructure. Access to data through an external actor. Åtkomst till data via extern aktör.