Automated Author ProfileZhou, Xianming
Zhou, Xianming
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: 0.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Genetic diversity is fundamental to sustainable crop improvement. This study uses 1,755 recombinant inbred lines from the SoyNAM population that were phenotyped for yield across nine environments to explore how training core composition based on genetic diversity affects the predictive ability of genomic selection models. Using supersaturated designs (SSDs), we quantified population diversity and generated training populations that were optimized conditionally for fixed test sets. We deployed two alternative strategies, one that maximizes and one that minimizes genetic diversity between training and test populations. Our results show that maximizing genetic diversity improves prediction accuracy compared to a random baseline in simple GBLUP models. However, this advantage disappears when explicit family pedigree information is available. We also found that maximizing genetic diversity preserves at least one individual from as many different families as possible, while minimizing diversity eliminates or overrepresentes them.
Authors
- Jarquin, Diego ;
- Garcia-Abadillo, Julian ;
- Zhou, Xianming
Genetic diversity is fundamental to sustainable crop improvement. This study uses 1,755 recombinant inbred lines from the SoyNAM population that were phenotyped for yield across nine environments to explore how training core composition based on genetic diversity affects the predictive ability of genomic selection models. Using supersaturated designs (SSDs), we quantified population diversity and generated training populations that were optimized conditionally for fixed test sets. We deployed two alternative strategies, one that maximizes and one that minimizes genetic diversity between training and test populations. Our results show that maximizing genetic diversity improves prediction accuracy compared to a random baseline in simple GBLUP models. However, this advantage disappears when explicit family pedigree information is available. We also found that maximizing genetic diversity preserves at least one individual from as many different families as possible, while minimizing diversity eliminates or overrepresentes them.
Authors
- Jarquin, Diego ;
- Garcia-Abadillo, Julian ;
- Zhou, Xianming