Cell Painting Image Collection

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/cell-painting-image-collection-dataset")

fs.listdir("s3://cytodata")

Description:

The Cell Painting Image Collection is a collection of freely downloadable microscopy image sets. Cell Painting is an unbiased high throughput imaging assay used to analyze perturbations in cell models. In addition to the images themselves, each set includes a description of the biological application and some type of "ground truth" (expected results). Researchers are encouraged to use these image sets as reference points when developing, testing, and publishing new image analysis algorithms for the life sciences. We hope that the this data set will lead to a better understanding of which methods are best for various biological image analysis applications.

Contact:

The Cell Painting Image Collection is a collection of freely downloadable microscopy image sets. Cell Painting is an unbiased high throughput imaging assay used to analyze perturbations in cell models. In addition to the images themselves, each set includes a description of the biological application and some type of "ground truth" (expected results). Researchers are encouraged to use these image sets as reference points when developing, testing, and publishing new image analysis algorithms for the life sciences. We hope that the this data set will lead to a better understanding of which methods are best for various biological image analysis applications.

Update Frequency:

irregularly

Managed By:

The Broad Institute

Resources:

1. resource:
   • Description: Images, extracted features and aggregated profiles are available as a S3 bucket
   • ARN: arn:aws:s3:::cytodata
   • Region: us-east-1
   • Type: S3 Bucket

Tags:

aws-pds, microscopy, biology, life sciences, imaging, high-throughput imaging, cell imaging, cell painting, fluorescence imaging

Publication:

1. publication:
   • Title: Example submission for the 2018 CytoData Hackathon (in R and Python)
   • URL: https://github.com/cytodata/cytodata-hackathon-2018/tree/master/cytodata-toolkit/
   • AuthorName: Juan Caicedo, Tim Becker
   • AuthorURL: broadinstitute.org