Automated Author Profile
Liu, Yang
Centre for Plant Biotechnology and GenomicsSouth China Agricultural University

Current S-Index

3.1

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.5

Average Dataset Index per dataset

Total Datasets

6

Total datasets for this author

Average FAIR Score

84.6%

Average FAIR Score per dataset

Total Citations

0

Total citations to the author's datasets

Total Mentions

0

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: 3.1 (sum of 6 datasets Dataset Index scores)

More information here.

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Additional file 2 of Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses

Additional file 2: Table S2. Parameters and options used in this study.

Authors

Manzano-Morales, Saioa ;
Liu, Yang ;
González-Bodí, Sara ;
Huerta-Cepas, Jaime ;
Iranzo, Jaime

0 Citations0 Mentions85% FAIR0.5 Dataset Index

10.6084/m9.figshare.244653182024

Additional file 2 of Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses

Additional file 2: Table S2. Parameters and options used in this study.

Authors

Manzano-Morales, Saioa ;
Liu, Yang ;
González-Bodí, Sara ;
Huerta-Cepas, Jaime ;
Iranzo, Jaime

0 Citations0 Mentions85% FAIR0.5 Dataset Index

10.6084/m9.figshare.24465318.v12024

Additional file 4 of Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses

Additional file 4. Ready-to-use code to calculate the predicted percentage of accessory genes per genome from the output of mOTUpan.

Authors

Manzano-Morales, Saioa ;
Liu, Yang ;
González-Bodí, Sara ;
Huerta-Cepas, Jaime ;
Iranzo, Jaime

0 Citations0 Mentions85% FAIR0.5 Dataset Index

10.6084/m9.figshare.266340182024

Additional file 4 of Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses

Additional file 4. Ready-to-use code to calculate the predicted percentage of accessory genes per genome from the output of mOTUpan.

Authors

Manzano-Morales, Saioa ;
Liu, Yang ;
González-Bodí, Sara ;
Huerta-Cepas, Jaime ;
Iranzo, Jaime

0 Citations0 Mentions85% FAIR0.5 Dataset Index

10.6084/m9.figshare.26634018.v12024

Additional file 1 of Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses

Additional file 1: Table S1. Popular tools for gene clustering and their application to pangenome analysis.

Authors

Manzano-Morales, Saioa ;
Liu, Yang ;
González-Bodí, Sara ;
Huerta-Cepas, Jaime ;
Iranzo, Jaime

0 Citations0 Mentions85% FAIR0.5 Dataset Index

10.6084/m9.figshare.244651422023

Additional file 1 of Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses

Additional file 1: Table S1. Popular tools for gene clustering and their application to pangenome analysis.

Authors

Manzano-Morales, Saioa ;
Liu, Yang ;
González-Bodí, Sara ;
Huerta-Cepas, Jaime ;
Iranzo, Jaime

0 Citations0 Mentions85% FAIR0.5 Dataset Index

10.6084/m9.figshare.24465142.v12023