Automated Organization ProfileThe University of Cambridge
The University of Cambridge
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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: 1.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Carbon Capture Storage (CCS) relevant Reactive Transport Modelling (RTM) of microfractures in basaltic rock; emulated using Gradient Boosted Decision Trees (GBDT) and subsequently optimised using Bayesian Optimisation (BO) framework. This project's code is hosted on Github at https://github.com/ThomasDodd97/CCS-RTM-GBDT-BO. This upload on Zenodo contains the dataset used to train four XGBoost GBDT surrogate models, whose model files are also uploaded here.
Authors
- Højlund-Dodd, T.
Carbon Capture Storage (CCS) relevant Reactive Transport Modelling (RTM) of microfractures in basaltic rock; emulated using Gradient Boosted Decision Trees (GBDT) and subsequently optimised using Bayesian Optimisation (BO) framework. This project's code is hosted on Github at https://github.com/ThomasDodd97/CCS-RTM-GBDT-BO. This upload on Zenodo contains the dataset used to train four XGBoost GBDT surrogate models, whose model files are also uploaded here.
Authors
- Højlund-Dodd, T.