Automated Author ProfileAguilar-Saavedra, J.A.
Aguilar-Saavedra, J.A.
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: 2.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Abstract We present an algorithm which allows a fast numerical computation of Feldman–Cousins confidence intervals for Poisson processes, even when the number of background events is relatively large. This algorithm incorporates an appropriate treatment of the singularities that arise as a consequence of the discreteness of the variable. Title of program: PCI Catalogue Id: ADMA_v1_0 Nature of problem Determination of confidence intervals for Poisson processes with background. Versions of this program held in the CPC repository in Mendeley Data ADMA_v1_0; PCI; 10.1016/S0010-4655(00)00035-7 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
Authors
- Aguilar-Saavedra, J.A.
Abstract We present an algorithm which allows a fast numerical computation of Feldman–Cousins confidence intervals for Poisson processes, even when the number of background events is relatively large. This algorithm incorporates an appropriate treatment of the singularities that arise as a consequence of the discreteness of the variable. Title of program: PCI Catalogue Id: ADMA_v1_0 Nature of problem Determination of confidence intervals for Poisson processes with background. Versions of this program held in the CPC repository in Mendeley Data ADMA_v1_0; PCI; 10.1016/S0010-4655(00)00035-7 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
Authors
- Aguilar-Saavedra, J.A.