Automated Author ProfileColtman, David L.
University of Alberta
Coltman, David L.
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: 2.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Polar bears (Ursus maritimus) in the Western Hudson Bay subpopulation have been declining in size and body condition for decades, as climate change causes earlier sea ice breakup, reduced hunting time on the ice, and an increasingly long fasting season. As Western Hudson Bay females have decreased in size, rates of litter production and average litter size have also decreased, while cub mortality and average time to independence have increased. Although these changes have potential evolutionary consequences, little is yet known about the adaptive genetic variation in body size or fat accumulation that would have to underlie any such change. In this study, we used high-throughput Illumina sequencing to develop SNPs from pooled blood and fat transcriptomes, using samples from five adult female polar bears and five (unrelated) dependent cubs. In total, we generated 371,258 transcripts of which 36,755 were deemed to be “full length” (i.e., covered more than 90% of their best BLAST hit), and we identified 63,020 SNPs. Since this study was conducted, we have used a subset of these SNPs to develop an Illumina BeadArray for quantitative genetics research in Western Hudson Bay.
Authors
- Malenfant, René M. ;
- Coltman, David L. ;
- Richardson, Evan S. ;
- Lunn, Nicholas J. ;
- Davis, Corey S. ;
- Coltman, David W. ;
- Lunn, Nick J.