Automated Author ProfileBaccarella, Alyssa
Baccarella, Alyssa
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: 9.0 (sum of 20 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Interactive figure for comparison of performance metrics. (A) Absolute precision and recall for each workflow. (B) Relative ranks of precision and recall for each workflow. Grey dots show performance for all workflows for the selected read depth(s) and sample number(s); red dots highlight the selected workflow(s). (XLSX 536 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Original read depth of individual samples. (XLSX 12 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Original read depth of individual samples. (XLSX 12 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Sample combinations for each iteration at varying sample numbers. The same sample combinations were run at all read depths and for all workflows. (XLSX 21 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Sample combinations for each iteration at varying sample numbers. The same sample combinations were run at all read depths and for all workflows. (XLSX 21 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Table of software tools, with versions and runtime parameters. (XLSX 15 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Table of software tools, with versions and runtime parameters. (XLSX 15 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Literature survey citations and average sample number. 200 studies containing RNA-Seq differential expression analysis, either from all species or limited to primary human samples. Average sample number from these studies is also displayed in Fig. 4. (XLSX 52 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Literature survey citations and average sample number. 200 studies containing RNA-Seq differential expression analysis, either from all species or limited to primary human samples. Average sample number from these studies is also displayed in Fig. 4. (XLSX 52 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles
Interactive figure for comparison of performance metrics. (A) Absolute precision and recall for each workflow. (B) Relative ranks of precision and recall for each workflow. Grey dots show performance for all workflows for the selected read depth(s) and sample number(s); red dots highlight the selected workflow(s). (XLSX 536 kb)
Authors
- Baccarella, Alyssa ;
- Williams, Claire ;
- Parrish, Jay ;
- Kim, Charles