Automated Author ProfileTUN, Hein
Chinese University of Hong Kong
TUN, Hein
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: 1.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Data SourcesThe uploded data consists of processed/formatted publicly available summary statistics, with the original data sourced from Public GWAS Summary Statistics Sources.xlsx. The ethics approval procedures are detailed in the original GWAS studies.MethodsGenome-wide significant SNPs (P < 5 × 10−8) were clumped to ensure genetic independence using the TwoSampleMR package. A strict cut-off of R2 < 0.001 within 10,000 kb windows were used for clumping with the 1000 Genomes European data as the reference linkage disequilibrium (LD) panel using PLINK's clumping algorithm.AcknowledgementsWe would like to thank UK Biobank (Health research data for the world - UK Biobank), GSCAN (https://genome.psych.umn.edu/index.php/GSCAN), GIANT (GIANT consortium - Giant Consortium), GERA (GERA summary statistics), GoDMC (GoDMC Database), DMP consortium (Dutch Microbiome Project), IEU GWAS Database (IEU OpenGWAS project) and GWAS catalog (GWAS Catalog) for providing the publicly available genetic and phenotypic data critical to our research. We acknowledge the efforts of the research teams, participants, and consortia behind these datasets for their commitment to open science.
Authors
- WU, Xueqi ;
- PENG, Ye ;
- TUN, Hein
Data SourcesThe uploded data consists of processed/formatted publicly available summary statistics, with the original data sourced from Public GWAS Summary Statistics Sources.xlsx. The ethics approval procedures are detailed in the original GWAS studies.MethodsGenome-wide significant SNPs (P < 5 × 10−8) were clumped to ensure genetic independence using the TwoSampleMR package. A strict cut-off of R2 < 0.001 within 10,000 kb windows were used for clumping with the 1000 Genomes European data as the reference linkage disequilibrium (LD) panel using PLINK's clumping algorithm.AcknowledgementsWe would like to thank UK Biobank (Health research data for the world - UK Biobank), GSCAN (https://genome.psych.umn.edu/index.php/GSCAN), GIANT (GIANT consortium - Giant Consortium), GERA (GERA summary statistics), GoDMC (GoDMC Database), DMP consortium (Dutch Microbiome Project), IEU GWAS Database (IEU OpenGWAS project) and GWAS catalog (GWAS Catalog) for providing the publicly available genetic and phenotypic data critical to our research. We acknowledge the efforts of the research teams, participants, and consortia behind these datasets for their commitment to open science.
Authors
- WU, Xueqi ;
- PENG, Ye ;
- TUN, Hein