Automated Author ProfileFederica Esposito
Federica Esposito
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: 2.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Data refer to the results obtained within the GR-2016-02363997 project funded by the Italian Ministry of Health. Main aim was to combine clinical data with genetic variants, transcriptomic and T lymphocyte repertoires signatures to dissect the biological basis of multiple sclerosis (MS) inflammatory activity.Two sets of patients were analysed: i) Extended cohort, for the identification of genetic biomarkers, ii) Core Cohort, for the identification of genetic, transcriptomic and immune repertoires biomarkers. Each patient was classified according to the NEDA (No Evidence of Disease Activity) criterion at 4- year follow-up, while time to first relapse (TTFR) was considered as secondary outcome.Data includes:-Results: GR-2016-02363997_results.pdf-uploaded documents.pdf-Genetic data: GEN_top_SNPs_geno.ped; GEN_top_SNPs_geno.map; GEN_var.txt; GEN_gene-wise.txt -Transcriptomic data: EXPR_topRNA_CPM.txt; EXPR_var.txt -Immunosequencing data: IMMSEQ_diversity.txt; IMMSEQ_trbd.txt; IMMSEQ_trbj.txt; IMMSEQ_trbv.txt; IMMSEQ_var.txt
Authors
- Federica Esposito
Data refer to the results obtained within the GR-2016-02363997 project funded by the Italian Ministry of Health. Main aim was to combine clinical data with genetic variants, transcriptomic and T lymphocyte repertoires signatures to dissect the biological basis of multiple sclerosis (MS) inflammatory activity.Two sets of patients were analysed: i) Extended cohort, for the identification of genetic biomarkers, ii) Core Cohort, for the identification of genetic, transcriptomic and immune repertoires biomarkers. Each patient was classified according to the NEDA (No Evidence of Disease Activity) criterion at 4- year follow-up, while time to first relapse (TTFR) was considered as secondary outcome.Data includes:-Results: GR-2016-02363997_results.pdf-uploaded documents.pdf-Genetic data: GEN_top_SNPs_geno.ped; GEN_top_SNPs_geno.map; GEN_var.txt; GEN_gene-wise.txt -Transcriptomic data: EXPR_topRNA_CPM.txt; EXPR_var.txt -Immunosequencing data: IMMSEQ_diversity.txt; IMMSEQ_trbd.txt; IMMSEQ_trbj.txt; IMMSEQ_trbv.txt; IMMSEQ_var.txt
Authors
- Federica Esposito