Automated Author ProfileRobinson, Derek
0000-0002-4293-1095
Robinson, Derek
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: 0.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We built models to compare the relative influence of climate, land cover/land use and topography on wetlands in the Prairie Pothole Region of Alberta. To represent these three drivers, we used 19 variables, which were selected based on a literature review; following, we excluded variables that had more than a 0.8 correlation.
Here, we prove: 1) the data we used for all three models and 2) our R code for parameter optimization and model building. We share the code using a Jupyter notebook, and the data are in he form of an csv file. In t the data, the column "Level" indicates whether the data was used in the training vs Test, and the column Region, indicates which of the three Natural Regions that data belong to.
Authors
- Daniel, Jody ;
- Rooney, Rebecca ;
- Robinson, Derek
We built models to compare the relative influence of climate, land cover/land use and topography on wetlands in the Prairie Pothole Region of Alberta. To represent these three drivers, we used 19 variables, which were selected based on a literature review; following, we excluded variables that had more than a 0.8 correlation.
Here, we prove: 1) the data we used for all three models and 2) our R code for parameter optimization and model building. We share the code using a Jupyter notebook, and the data are in he form of an csv file. In t the data, the column "Level" indicates whether the data was used in the training vs Test, and the column Region, indicates which of the three Natural Regions that data belong to.
Authors
- Daniel, Jody ;
- Rooney, Rebecca ;
- Robinson, Derek