Automated Author ProfileWalters, William
Walters, William
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: 11.9 (sum of 9 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This data includes the RAPID whole core (BEAVRS benchmark and small core model) transport calculation results with different collapsing options, tolerances of power iterations and precision in floating numbers. It also includes a estimate of the iterative error compared to the real one. Finally, RAPID with all acceleration and numerical techniques are applied on BEAVRS benchmark and compared to Serpent 2 Monte Carlo reference calculation.
Authors
- Walters, William
This data includes the RAPID whole core (BEAVRS benchmark and small core model) transport calculation results with different collapsing options, tolerances of power iterations and precision in floating numbers. It also includes a estimate of the iterative error compared to the real one. Finally, RAPID with all acceleration and numerical techniques are applied on BEAVRS benchmark and compared to Serpent 2 Monte Carlo reference calculation.
Authors
- Walters, William
This data includes the RAPID whole core (BEAVRS benchmark and small core model) transport calculation results with different collapsing options, tolerances of power iterations and precision in floating numbers. It also includes a estimate of the iterative error compared to the real one. Finally, RAPID with all acceleration and numerical techniques are applied on BEAVRS benchmark and compared to Serpent 2 Monte Carlo reference calculation.
Authors
- Walters, William
These data contain the Serpent and RAPID transport calculation results on the BEAVRS benchmark with four distributions of temperature, including: 1. Only heterogeneous fuel temperature 2. Only heterogeneous moderator temperature 3. Mixed heterogeneous fuel temperature (highest 1300K, lowest 600K) 3. Mixed heterogeneous fuel temperature (highest 900K, lowest 600K). The uploaded files also include the R script to read-in RAPID and Serpent data, compare them, and plot the fission rates and relative error.
Authors
- Walters, William
These data contain the Serpent and RAPID transport calculation results on the BEAVRS benchmark with four distributions of temperature, including: 1. Only heterogeneous fuel temperature 2. Only heterogeneous moderator temperature 3. Mixed heterogeneous fuel temperature (highest 1300K, lowest 600K) 3. Mixed heterogeneous fuel temperature (highest 900K, lowest 600K). The uploaded files also include the R script to read-in RAPID and Serpent data, compare them, and plot the fission rates and relative error.
Authors
- Walters, William
Per-sample metadata mapping file used throughout the QIIME pipeline. (XLSX 61Â kb)
Authors
- Giloteaux, Ludovic ;
- Goodrich, Julia ;
- Walters, William ;
- Levine, Susan ;
- Ley, Ruth ;
- Hanson, Maureen
Feature Importance Scores for genus-level supervised learning. The feature importance score is the percentage increase in error rate when the given feature is permuted while other values remain constant. As there are only two categories, the increased error rate is equal for both categories. (XLSX 42Â kb)
Authors
- Giloteaux, Ludovic ;
- Goodrich, Julia ;
- Walters, William ;
- Levine, Susan ;
- Ley, Ruth ;
- Hanson, Maureen
Per-sample metadata mapping file used throughout the QIIME pipeline. (XLSX 61Â kb)
Authors
- Giloteaux, Ludovic ;
- Goodrich, Julia ;
- Walters, William ;
- Levine, Susan ;
- Ley, Ruth ;
- Hanson, Maureen
Feature Importance Scores for genus-level supervised learning. The feature importance score is the percentage increase in error rate when the given feature is permuted while other values remain constant. As there are only two categories, the increased error rate is equal for both categories. (XLSX 42Â kb)
Authors
- Giloteaux, Ludovic ;
- Goodrich, Julia ;
- Walters, William ;
- Levine, Susan ;
- Ley, Ruth ;
- Hanson, Maureen