Automated Author ProfileChang, Hang
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, CA, 94720, Berkeley, USA
Chang, Hang
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.6 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
All data supporting the findings of the publication "NaroNet: Objective-based learning of the tumor microenvironment from highly multiplexed immunostained images", including multiplex images, ground-truth masks, and patient data. The code used to produce the results of this study is available at https://github.com/djimenezsanchez/NaroNet Synthetic_experiments.zip. contains synthetic patient cohorts generated from the synthetic tissue simulator called Synplex. Synplex tissue generator is thoroughly described in the arxiv preprint https://arxiv.org/abs/2103.04617 Endometrial_High_grade_cancer.zip contains tiff stacks and patient data for the endometrial high-grade cancer cohort. Note that the endometrial high-grade cancer images provided are already unmixed.
Authors
- Jiménez-Sánchez, Daniel ;
- Ariz, Mikel ;
- Chang, Hang ;
- Matias-Guiu, Xavier ;
- Andrea, Carlos E. De ;
- Ortiz-De-Solórzano, Carlos
All data supporting the findings of the publication "NaroNet: Objective-based learning of the tumor microenvironment from highly multiplexed immunostained images", including multiplex images, ground-truth masks, and patient data. The code used to produce the results of this study is available at https://github.com/djimenezsanchez/NaroNet Synthetic_experiments.zip. contains synthetic patient cohorts generated from the synthetic tissue simulator called Synplex. Synplex tissue generator is thoroughly described in the arxiv preprint https://arxiv.org/abs/2103.04617 Endometrial_High_grade_cancer.zip contains tiff stacks and patient data for the endometrial high-grade cancer cohort. Note that the endometrial high-grade cancer images provided are already unmixed.
Authors
- Jiménez-Sánchez, Daniel ;
- Ariz, Mikel ;
- Chang, Hang ;
- Matias-Guiu, Xavier ;
- Andrea, Carlos E. De ;
- Ortiz-De-Solórzano, Carlos
All data supporting the findings of the publication "NaroNet: Objective-based learning of the tumor microenvironment from highly multiplexed immunostained images", including multiplex images, ground-truth masks, and patient data. The code used to produce the results of this study is available at https://github.com/djimenezsanchez/NaroNet Synthetic_experiments.zip. contains synthetic patient cohorts generated from the synthetic tissue simulator called Synplex. Synplex tissue generator is thoroughly described in the arxiv preprint https://arxiv.org/abs/2103.04617 Endometrial_High_grade_cancer.zip contains tiff stacks and patient data for the endometrial high-grade cancer cohort. Note that the endometrial high-grade cancer images provided are already unmixed.
Authors
- Jiménez-Sánchez, Daniel ;
- Ariz, Mikel ;
- Chang, Hang ;
- Matias-Guiu, Xavier ;
- Andrea, Carlos E. De ;
- Ortiz-De-Solórzano, Carlos