Automated Author ProfileCalogero, Lorenzo
Calogero, Lorenzo
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
A dataset made of 500 cell for each of the 7 cell lines described in GSE243665 was generate using the Shiny App available at http://130.192.212.153:3838/.The dataset was annotated using Homo_sapiens.GRCh38.110.gtf (annotated_BE1500.csv) and clustered using the rCASC function scannoByGtf#clustering (BE1-500/Results/annotated_BE1500/6 folder) is performed using seurat Lovain modularity method, embedded in the rCASC package (Alessandri et al. Gigascience. 2019, PMID: 31494672)command.R describes all the analysis steps.BE1_log2TPM.txt.zip was generated using the data available at https://doi.org/https://doi.org/10.6084/m9.figshare.23284748.v1The comparison with Tian 2019 dataset requires the data available at https://doi.org/10.6084/m9.figshare.23274413.v1The comparison with PC9 scRNA-seq data requires the data available at https://doi.org/10.6084/m9.figshare.23626407.v1
Authors
- Calogero, Raffaele ;
- Calogero, Lorenzo
A dataset made of 500 cell for each of the 7 cell lines described in GSE243665 was generate using the Shiny App available at http://130.192.212.153:3838/.The dataset was annotated using Homo_sapiens.GRCh38.110.gtf (annotated_BE1500.csv) and clustered using the rCASC function scannoByGtf#clustering (BE1-500/Results/annotated_BE1500/6 folder) is performed using seurat Lovain modularity method, embedded in the rCASC package (Alessandri et al. Gigascience. 2019, PMID: 31494672)command.R describes all the analysis steps.BE1_log2TPM.txt.zip was generated using the data available at https://doi.org/https://doi.org/10.6084/m9.figshare.23284748.v1The comparison with Tian 2019 dataset requires the data available at https://doi.org/10.6084/m9.figshare.23274413.v1The comparison with PC9 scRNA-seq data requires the data available at https://doi.org/10.6084/m9.figshare.23626407.v1
Authors
- Calogero, Raffaele ;
- Calogero, Lorenzo