Automated Author ProfileGleeson, Josie
University of Melbourne
Gleeson, Josie
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: 2.0 (sum of 5 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Contents:
nanopolish: direct RNA datasets analysed by nanopolish - raw_data: merged data between nanopolish and npTranscript results - analysis: testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks with log-transformed data of Calu-3 48hpi direct RNA dataset
- scripts: example scripts for testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks, and also with log-transformed data
tailfindr: direct cDNA datasets analysed by tailfindr - scripts: example scripts for poly(A) vs poly(T) correlation, mixed_model and generating raincloud plots for poly(A) length
- raw_data: merged data between tailfindr and npTranscript results
- analysis: tailfindr outputs and mixed model analysis outputs where files with names ending with 'int' shows the coefficient (changes in log tail length relative to control (intercept), and files with names ending with 'log_nofilter' shows the padj values for changes between control and infected poly(A) lengths.
nanopolish_vs_tailfindr:
- raw_data: median poly(A/T) lengths from nanopolish and tailfindr (before log transformation) - analysis: Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
- scripts: example scripts for generating Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
tailfindr_scripts: -tailfindr-related scripts only
tailfindr_calu_48hpi_analysis: - analysis results files from calu 48 hpi dataset including 'meancalc' - working sheet for backtransforming and calculating difference in log-tranformed poly(A) lengths between control and infected - control: intercepts for control - infected: coefficients for infected - mt: mitochondrial genes - nonmt: non-mitochondrial genes
Authors
- Chang, Jessie Jie-Youen ;
- Gleeson, Josie ;
- Rawlinson, Daniel ;
- Pitt, Miranda ;
- De Paoli-Iseppi, Ricardo ;
- Zhou, Chenxi ;
- LONDRIGAN, SARAH ;
- Clark, Michael ;
- Mordant, Francesca ;
- SUBBARAO, KANTA ;
- STINEAR, TIMOTHY ;
- Coin, Lachlan
Contents:
nanopolish: direct RNA datasets analysed by nanopolish - raw_data: merged data between nanopolish and npTranscript results - analysis: testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks with log-transformed data of Calu-3 48hpi direct RNA dataset
- scripts: example scripts for testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks, and also with log-transformed data
tailfindr: direct cDNA datasets analysed by tailfindr - scripts: example scripts for poly(A) vs poly(T) correlation, mixed_model and generating raincloud plots for poly(A) length
- raw_data: merged data between tailfindr and npTranscript results
- analysis: tailfindr outputs and mixed model analysis outputs where files with names ending with 'int' shows the coefficient (changes in log tail length relative to control (intercept), and files with names ending with 'log_nofilter' shows the padj values for changes between control and infected poly(A) lengths.
nanopolish_vs_tailfindr:
- raw_data: median poly(A/T) lengths from nanopolish and tailfindr (before log transformation) - analysis: Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
- scripts: example scripts for generating Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
Authors
- Chang, Jessie Jie-Youen ;
- Gleeson, Josie ;
- Rawlinson, Daniel ;
- Pitt, Miranda ;
- De Paoli-Iseppi, Ricardo ;
- Zhou, Chenxi ;
- LONDRIGAN, SARAH ;
- Clark, Michael ;
- Mordant, Francesca ;
- SUBBARAO, KANTA ;
- STINEAR, TIMOTHY ;
- Coin, Lachlan
Contents:
nanopolish: direct RNA datasets analysed by nanopolish - raw_data: merged data between nanopolish and npTranscript results - analysis: testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks with log-transformed data of Calu-3 48hpi direct RNA dataset
- scripts: example scripts for testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks, and also with log-transformed data
tailfindr: direct cDNA datasets analysed by tailfindr - scripts: example scripts for poly(A) vs poly(T) correlation, mixed_model and generating raincloud plots for poly(A) length
- raw_data: merged data between tailfindr and npTranscript results
- analysis: tailfindr outputs and mixed model analysis outputs where files with names ending with 'int' shows the coefficient (changes in log tail length relative to control (intercept), and files with names ending with 'log_nofilter' shows the padj values for changes between control and infected poly(A) lengths.
nanopolish_vs_tailfindr:
- raw_data: median poly(A/T) lengths from nanopolish and tailfindr (before log transformation) - analysis: Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
- scripts: example scripts for generating Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
Authors
- Chang, Jessie Jie-Youen ;
- Gleeson, Josie ;
- Rawlinson, Daniel ;
- Pitt, Miranda ;
- De Paoli-Iseppi, Ricardo ;
- Zhou, Chenxi ;
- LONDRIGAN, SARAH ;
- Clark, Michael ;
- Mordant, Francesca ;
- SUBBARAO, KANTA ;
- STINEAR, TIMOTHY ;
- Coin, Lachlan
Contents:
nanopolish: direct RNA datasets analysed by nanopolish - raw_data: merged data between nanopolish and npTranscript results - analysis: testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks with log-transformed data of Calu-3 48hpi direct RNA dataset
- scripts: example scripts for testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks, and also with log-transformed data
tailfindr: direct cDNA datasets analysed by tailfindr - scripts: example scripts for poly(A) vs poly(T) correlation, mixed_model and generating raincloud plots for poly(A) length
- raw_data: merged data between tailfindr and npTranscript results
- analysis: tailfindr outputs and mixed model analysis outputs where files with names ending with 'int' shows the coefficient (changes in log tail length relative to control (intercept), and files with names ending with 'log_nofilter' shows the padj values for changes between control and infected poly(A) lengths.
nanopolish_vs_tailfindr:
- raw_data: median poly(A/T) lengths from nanopolish and tailfindr (before log transformation) - analysis: Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
- scripts: example scripts for generating Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
tailfindr_scripts: -tailfindr-related scripts only
tailfindr_calu_48hpi_analysis: - analysis results files from calu 48 hpi dataset including 'meancalc' - working sheet for backtransforming and calculating difference in log-tranformed poly(A) lengths between control and infected - control: intercepts for control - infected: coefficients for infected - mt: mitochondrial genes - nonmt: non-mitochondrial genes
Authors
- Zhou, Chenxi ;
- LONDRIGAN, SARAH ;
- Chang, Jessie Jie-Youen ;
- Gleeson, Josie ;
- Rawlinson, Daniel ;
- Pitt, Miranda ;
- De Paoli-Iseppi, Ricardo ;
- Clark, Michael ;
- Mordant, Francesca ;
- SUBBARAO, KANTA ;
- STINEAR, TIMOTHY ;
- Coin, Lachlan
Contents:
nanopolish: direct RNA datasets analysed by nanopolish- raw_data: merged data between nanopolish and npTranscript results- analysis: testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks with log-transformed data of Calu-3 48hpi direct RNA dataset
- scripts: example scripts for testing changes in poly(A) length between control and infected using Wilcoxon's test of ranks, and also with log-transformed data
tailfindr: direct cDNA datasets analysed by tailfindr- raw_data: merged data between tailfindr and npTranscript results
- analysis: tailfindr outputs and mixed model analysis outputs where files with names ending with 'int' shows the intercept (changes in log tail length relative to control), and files with names ending with 'log_nofilter' shows the padj values for changes between control and infected poly(A) lengths.
- scripts: example scripts for poly(A) vs poly(T) correlation, mixed_model and generating raincloud plots for poly(A) length
nanopolish_vs_tailfindr:
- raw_data: median poly(A/T) lengths from nanopolish and tailfindr (before log transformation)- analysis: Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
- scripts: example scripts for generating Spearman's correlation tests between nanopolish poly(A) and tailfindr poly(A/T) median lengths
Authors
- Chang, Jessie Jie-Youen ;
- Gleeson, Josie ;
- Rawlinson, Daniel ;
- Pitt, Miranda ;
- De Paoli-Iseppi, Ricardo ;
- Zhou, Chenxi ;
- LONDRIGAN, SARAH ;
- Clark, Michael ;
- Mordant, Francesca ;
- SUBBARAO, KANTA ;
- STINEAR, TIMOTHY ;
- Coin, Lachlan