Adding new dataset CHAMMI-75

datasets/chammi.yaml

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+Name: CHAMMI-75
+Description: | 
+  Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. 
+  However, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. 
+  This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), 
+  or because the target experimental conditions are out of distribution. We have created CHAMMI-75, a large-scale dataset containing 2.8 million multi-channel, 
+  high-resolution images curated from 75 diverse, publicly available biological studies. This dataset is useful to investigate and develop channel-adaptive models, 
+  which could process microscopy images of varying technical specifications and regardless of the number of channels. By breaking the limitations of existing models, 
+  CHAMMI-75 is an invaluable resource for creating the next generation of foundation models for image-based biological research.  
+Documentation: https://github.com/CaicedoLab/CHAMMI-75
+Contact: Contact via email Juan Caicedo, [email protected]
+ManagedBy: Morgridge Institute for Research
+UpdateFrequency: Every 2 years
+Tags:
+  - microscopy
+  - machine learning
+  - biology
+  - life sciences
+  - imaging
+  - high-throughput imaging
+  - cell imaging
+  - fluorescence imaging
+License: CC BY 4.0 License
+Citation: 
+Resources:
+  - Description: Images, training set and evaluation set available in an S3 bucket
+    ARN:
+    Region:
+    Type:
+    Explore:
+DataAtWork:
+  Tutorials:
+    - Title: Get To Know A Dataset: CHAMMI-75
+      URL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/
+      NotebookURL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/get-to-know-a-dataset-template.ipynb
+      AuthorName: Vidit Agrawal, Juan Caicedo
+      AuthorURL:
+      Services: Getting to know a dataset
+    - Title: Running CHAMMI-75 Evaluation Benchmarks
+      URL:  https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/
+      NotebookURL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/running-benchmarks.ipynb
+      AuthorName: Vidit Agrawal, Juan Caicedo
+      Author URL:
+      Services: It will enable researchers to run state of the art benchmarks in the exploration of single cell self-supervised learning foundation models.
+  Tools & Applications:
+    - Title: CHAMMI-75 Source Code
+      URL: https://github.com/CaicedoLab/CHAMMI-75
+      AuthorName: Vidit Agrawal
+      AuthorURL:
+    - Title: CHAMMI Benchmarking Source Code
+      URL: https://github.com/chaudatascience/channel_adaptive_models
+      AuthorName: Chau Pham
+      AuthorURL: 
+  Publications:
+    - Title: CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
+      URL: https://neurips.cc/virtual/2023/poster/73620
+      AuthorName: Zitong Sam Chen, Chau Pham, Siqi Wang, Michael Doron, Nikita Moshkov, Bryan Plummer, Juan C. Caicedo
+      AuthorURL:
+DeprecatedNotice:
+ADXCategories:
+  -    Healthcare & Life Sciences Data