Automated Author ProfileC, RAJALAKSHMI
0000-0003-2491-5945
C, RAJALAKSHMI
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: 0.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset supports the study “Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.” It includes curated protein sequences from 1,758 virus species across 18 families, analyzed for the SPHAK method. SPHAK implements a unified framework to estimate virus family and cross-host transmission risk by identifying host-specific sequence patterns. This dataset provides the foundation for reproducible modeling of host spillover events across both animal and plant viruses.
Authors
- Nambipareechi Gangadharan, Vinni ;
- Prakash, Ananya ;
- S, Kavya ;
- C, RAJALAKSHMI ;
- Mariam Paul, Prisho ;
- Thomas, VIBIN IPE
This dataset supports the study “Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.” It includes curated protein sequences from 1,758 virus species across 18 families, analyzed for the SPHAK method. SPHAK implements a unified framework to estimate virus family and cross-host transmission risk by identifying host-specific sequence patterns. This dataset provides the foundation for reproducible modeling of host spillover events across both animal and plant viruses.
Authors
- Nambipareechi Gangadharan, Vinni ;
- Prakash, Ananya ;
- S, Kavya ;
- C, RAJALAKSHMI ;
- Mariam Paul, Prisho ;
- Thomas, VIBIN IPE