Automated Organization Profile

Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China

Current S-Index

7.7

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

1.5

Average Dataset Index per dataset

Total Datasets

5

Total datasets in this organization

Average FAIR Score

75.0%

Average FAIR Score per dataset

Total Citations

0

Total citations to the organization's datasets

Total Mentions

0

Total mentions of the organization's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Simulated dataset for P-body detection model training

A simulated dataset tailored for training the P-body detection model in article "PB-scope: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds."The dataset is meticulously structured in YOLO format, a standardized annotation framework widely adopted for object detection tasks, ensuring compatibility with cutting-edge deep learning pipelines. The pre-trained model weights for P-body detection in the article, are stored in the file detect_weight.pt.

Authors

  • Tsuboi, Tatsuhisa ;
  • Shen, Dexin
0 Citations0 Mentions77% FAIR1.9 Dataset Index
10.5281/zenodo.139585852025

P-body-phenotype drug screening dataset

Dataset for "PB-scope: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds."

Authors

  • Tsuboi, Tatsuhisa ;
  • Shen, Dexin
0 Citations0 Mentions79% FAIR0.3 Dataset Index
10.5281/zenodo.145911572025

P-body-phenotype drug screening dataset

Dataset for "PB-scope: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds."

Authors

  • Tsuboi, Tatsuhisa ;
  • Shen, Dexin
0 Citations0 Mentions73% FAIR1.8 Dataset Index
10.5281/zenodo.145911582025

Simulated dataset for P-body detection model training

A simulated dataset tailored for training the P-body detection model in article "PB-scope: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds."The dataset is meticulously structured in YOLO format, a standardized annotation framework widely adopted for object detection tasks, ensuring compatibility with cutting-edge deep learning pipelines. The pre-trained model weights for P-body detection in the article, are stored in the file detect_weight.pt.

Authors

  • Tsuboi, Tatsuhisa ;
  • Shen, Dexin
0 Citations0 Mentions73% FAIR1.8 Dataset Index
10.5281/zenodo.152021032025

Simulated dataset for P-body detection model training

A simulated dataset tailored for training the P-body detection model in article "PB-scope: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds."The dataset is meticulously structured in YOLO format, a standardized annotation framework widely adopted for object detection tasks, ensuring compatibility with cutting-edge deep learning pipelines. The pre-trained model weights for P-body detection in the article, are stored in the file detect_weight.pt.

Authors

  • Tsuboi, Tatsuhisa ;
  • Shen, Dexin
0 Citations0 Mentions73% FAIR1.8 Dataset Index
10.5281/zenodo.139585862024