Automated Author ProfileArul, Monica
Arul, Monica
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: 0.5 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This project employs zero-shot text classification with pre-trained BART-large models to efficiently extract and analyze critical data on infrastructure and community impacts from wind disaster reconnaissance reports. While primarily focused on wind, the methodology can be applied to various hazards, including earthquakes. It utilizes advanced NLP models including BART-large MNLI and CNN, which eliminates the need for extensive labeled datasets. This approach enables the rapid synthesis of impact information from historical damage reports, essential for informed decision-making and resilience planning. The primary audience includes researchers, disaster managers, and natural hazard engineers, focusing on disaster resilience and response.For more details and access to the project resources, visit the GitHub repository: Impact-Data-Mining.
Authors
- Pham, Huy ;
- Arul, Monica