Droplet Microfluidics Image Data for Detecting Antibiotic-Resistant Bacteria

Experimental time-lapse TIFF image sequences from droplet microfluidic experiments conducted at Uppsala University. The 227.3 GB dataset supports reproducibility of methods for detecting rare antibiotic-resistant bacterial subpopulations at single-cell resolution. It was published in npj Digital Medicine and is organized into folders corresponding to manuscript figures.

Use Cases

• Validating image texture analysis workflows for monitoring bacterial growth based on the described quantitative methods.
• Reproducing experiments on heteroresistant subpopulations under antibiotic exposure based on the described Gram-positive and Gram-negative species tests.
• Developing machine learning models for single-cell resolution detection of antibiotic resistance based on time-lapse image sequences.
• Benchmarking droplet microfluidic experimental setups for bloodstream infection studies based on the provided experimental data.

Strengths

• Dataset size is 227.3 GB, indicating a substantial volume of high-resolution image data.
• Explicitly linked to a peer-reviewed publication in npj Digital Medicine, providing academic context.
• Includes data from experiments with both Gram-positive and Gram-negative bacterial species, suggesting comparative potential.
• Data is organized by manuscript figure, which may facilitate direct validation of published results.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain quantitative analyses.

Provenance

Source

Uppsala University

Collection Method

Generated from droplet microfluidic experiments with time-lapse imaging.

Freshness

Last updated 2026-05-28 07:24:39; freshness should be verified.