Automated Author ProfileWeiss-Schneeweiss, Hanna
Weiss-Schneeweiss, Hanna
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: 21.8 (sum of 14 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
No description available
Authors
- McCann, Jamie ;
- Jang, Tae-Soo ;
- Macas, Jiri ;
- Schneeweiss, Gerald M. ;
- Matzke, Nicholas J. ;
- Novak, Petr ;
- Stuessy, Tod F. ;
- Villaseñor, Jose L. ;
- Weiss-Schneeweiss, Hanna
No description available
Authors
- McCann, Jamie ;
- Jang, Tae-Soo ;
- Macas, Jiri ;
- Schneeweiss, Gerald M. ;
- Matzke, Nicholas J. ;
- Novak, Petr ;
- Stuessy, Tod F. ;
- Villaseñor, Jose L. ;
- Weiss-Schneeweiss, Hanna
No description available
Authors
- McCann, Jamie ;
- Jang, Tae-Soo ;
- Macas, Jiri ;
- Schneeweiss, Gerald M. ;
- Matzke, Nicholas J. ;
- Novak, Petr ;
- Stuessy, Tod F. ;
- Villaseñor, Jose L. ;
- Weiss-Schneeweiss, Hanna
No description available
Authors
- McCann, Jamie ;
- Schneeweiss, Gerald M. ;
- Stuessy, Tod F. ;
- Villaseñor, Jose L. ;
- Weiss-Schneeweiss, Hanna
No description available
Authors
- McCann, Jamie ;
- Schneeweiss, Gerald M. ;
- Stuessy, Tod F. ;
- Villaseñor, Jose L. ;
- Weiss-Schneeweiss, Hanna
No description available
Authors
- McCann, Jamie ;
- Schneeweiss, Gerald M. ;
- Stuessy, Tod F. ;
- Villaseñor, Jose L. ;
- Weiss-Schneeweiss, Hanna
No description available
Authors
- McCann, Jamie ;
- Schneeweiss, Gerald M. ;
- Stuessy, Tod F. ;
- Villaseñor, Jose L. ;
- Weiss-Schneeweiss, Hanna
A large proportion of genomic information, particularly repetitive elements, is usually ignored when researchers are using next-generation sequencing. Here we demonstrate the usefulness of this repetitive fraction in phylogenetic analyses, utilising comparative graph-based clustering of next-generation sequence reads, which results in abundance estimates of different classes of genomic repeats. Phylogenetic trees are then inferred based on the genome-wide abundance of different repeat types treated as continuously varying characters; such repeats are scattered across chromosomes and in angiosperms can constitute a majority of nuclear genomic DNA. In six diverse examples, five angiosperms and one insect, this method provides generally well-supported relationships at interspecific and intergeneric levels that agree with results from more standard phylogenetic analyses of commonly used markers. We propose that this methodology may prove especially useful in groups where there is little genetic differentiation in standard phylogenetic markers. At the same time as providing data for phylogenetic inference, this method additionally yields a wealth of data for comparative studies of genome evolution.
Authors
- Dodsworth, Steven ;
- Chase, Mark W. ;
- Kelly, Laura J. ;
- Leitch, Ilia J. ;
- Macas, Jiří ;
- Novák, Petr ;
- Piednoel, Mathieu ;
- Weiss-Schneeweiss, Hanna ;
- Leitch, Andrew R.
No description available
Authors
- Dodsworth, Steven ;
- Chase, Mark W. ;
- Kelly, Laura J. ;
- Leitch, Ilia J. ;
- Macas, Jiří ;
- Novák, Petr ;
- Piednoel, Mathieu ;
- Weiss-Schneeweiss, Hanna ;
- Leitch, Andrew R.