E. coli Printed Arrays: Confocal Microscopy Images and Processing Data

Ravinash Krishna Kumar provides 300 data files supporting a study on bacterial spatial ecology. The files include 143 .tif and 141 .mat files, plus .czi, .jpg, and .png files containing raw micrographs, processed images, and segregation index calculations. The data underpins research using a droplet-based printing method to arrange E. coli genotypes in sub-millimeter arrays.

Use Cases

• Training image segmentation models based on confocal microscopy images of printed bacterial arrays.
• Calculating segregation indices for bacterial competition experiments based on provided Matlab (.mat) data files.
• Benchmarking microscopy image processing pipelines using the provided raw .tif and .czi files alongside processed data.
• Studying the relationship between fine-scale spatial structure and ecological outcomes in synthetic microbial communities.

Strengths

• Includes 300 raw and processed data files supporting all figures in the related manuscript.
• Provides data in multiple formats, with 13 .czi files also available as openly accessible .tif files.
• Contains specific file types for different analysis stages: .mat files for image processing calculations and .tif/.czi for raw microscopy images.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download from the readme file.
• Row count and dataset size are unknown, which may limit suitability assessment for large-scale ML projects.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Data generated from a study using droplet-based printing of bacterial arrays and subsequent confocal microscopy imaging.