Automated Author ProfileCui, Kaiming
University of Warwick0000-0003-1535-5587
Cui, Kaiming
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: 0.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This is the sample of our TESS-SPOC exoplanet occurrence rate posteriors. The deail of the occurrence rate estimation can be found in our paper.Example of usage with Python:import h5pywith h5py.File("occurrence_rate_posteriors_10x10.h5", "r") as f: sample = f["occurrence_rate_posteriors_10x10"][:]Then, the sample gives you a numpy ndarry with shape of (6960, 10, 10). This is the sample of occurrence rate (number of planets per star in each bin) posteriors of each 10-by-10 bins.We have an online interactive plot allows users to easily select any region and obtain its total occurrence rate.
Authors
- Cui, Kaiming
This is the sample of our TESS-SPOC exoplanet occurrence rate posteriors. The deail of the occurrence rate estimation can be found in our paper.Example of usage with Python:import h5pywith h5py.File("occurrence_rate_posteriors_10x10.h5", "r") as f: sample = f["occurrence_rate_posteriors_10x10"][:]Then, the sample gives you a numpy ndarry with shape of (6960, 10, 10). This is the sample of occurrence rate (number of planets per star in each bin) posteriors of each 10-by-10 bins.We have an online interactive plot allows users to easily select any region and obtain its total occurrence rate.
Authors
- Cui, Kaiming