Automated Organization ProfileWatermark Numerical Computing
Watermark Numerical Computing
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: 1.7 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This is the data used to produce the results of the paper "Data space inversion for efficient uncertainty quantification using an integrated surface and subsurface hydrologic model" in GMD.
Authors
- Delottier, Hugo ;
- Doherty, John ;
- Brunner, Philip
This is the data used to produce the results of the paper "Data space inversion for efficient uncertainty quantification using an integrated surface and subsurface hydrologic model" in GMD.
Authors
- Delottier, Hugo ;
- Doherty, John ;
- Brunner, Philip
The existing three-dimensional groundwater flow model (MODFLOW-2005) of the Mississippi Embayment Regional Aquifer system (MERAS), South-Central United States, was updated with: 1) higher stream density; 2) more spatially refined recharge; 3) better estimates of water use; 4) more recent time period simulated; 5) more realistic storage conceptualization; and 6) more robust handling of dry nodes through use of MODFLOW-NWT. For this study, the MODFLOW-NWT groundwater flow model was used to evaluate four parameter estimation algorithms with lower computational burdens. This work was performed to update the previous version of the MERAS groundwater flow model for decision making in the Mississippi Alluvial Plain (MAP), USA.This USGS data release contains all of the input and output to run the model simulations described in the associated Hunt et al. (2021) journal article (https://doi.org/10.1111/gwat.13106).
Authors
- Hunt, Randall J ;
- Duncan, Leslie L ;
- Haugh, Connor J ;
- White, Jeremy T ;
- Doherty, John J