Automated Author ProfileTakahama, Shokichi
National Institute of Biomedical Innovation, Health and Nutrition, Japan0000-0002-5651-4217
Takahama, Shokichi
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: 0.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The file containing post QC, count matrix, containing 6 samples as following. (S01:CHBN001, S02:CHBN002, S03:CHBN003, S04:CHBN004, S12:CHBN005, S14:CHBN006.) scRNAseq Libraries generated by 10xGenomics 5'-kit were read by NovaSeq 6000 platform. After sequencing, raw reads were mapped to human generated by cellranger 6.1.2, then generated count matrix were subjected to QC according to Seurat manual (mitochondrial genes <10%, ribosomal genes > 0.05%), then SCT-transformed and integrated with 3000 features. Detail of QC/integration will be described in our manuscript.
Authors
- Takahama, Shokichi
The file containing post QC, count matrix, containing 6 samples as following. (S01:CHBN001, S02:CHBN002, S03:CHBN003, S04:CHBN004, S12:CHBN005, S14:CHBN006.) scRNAseq Libraries generated by 10xGenomics 5'-kit were read by NovaSeq 6000 platform. After sequencing, raw reads were mapped to human generated by cellranger 6.1.2, then generated count matrix were subjected to QC according to Seurat manual (mitochondrial genes <10%, ribosomal genes > 0.05%), then SCT-transformed and integrated with 3000 features. Detail of QC/integration will be described in our manuscript.
Authors
- Takahama, Shokichi