Automated Author ProfileZARB ADAMI, KRISTIAN
ZARB ADAMI, KRISTIAN
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: 1.4 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
These files are the weights from the models described in our paper, "Convolutional Neural Networks for the Automated Detection of Strong Gravitational Lensing" (https://academic.oup.com/mnras/article/505/4/6155/6295319), trained for a varying number of epochs.The dataset on which these models have been trained is available on the Gravitational Lens Finding Challenge 1.0 web page:
http://metcalf1.difa.unibo.it/blf-portal/gg_challenge.html
The code written to train and load these models is available on the GitHub repository:
https://github.com/DanielMagro97/LEXACTUM
These models were also previously uploaded to, and are accessible on, Zenodo:
https://zenodo.org/records/4299924
Authors
- MAGRO, DANIEL ;
- ZARB ADAMI, KRISTIAN ;
- De Marco, Andrea ;
- Riggi, Simone ;
- Sciacca, Eva
These files are the weights from the models described in our paper, "Convolutional Neural Networks for the Automated Detection of Strong Gravitational Lensing" (https://academic.oup.com/mnras/article/505/4/6155/6295319), trained for a varying number of epochs.The dataset on which these models have been trained is available on the Gravitational Lens Finding Challenge 1.0 web page:
http://metcalf1.difa.unibo.it/blf-portal/gg_challenge.html
The code written to train and load these models is available on the GitHub repository:
https://github.com/DanielMagro97/LEXACTUM
These models were also previously uploaded to, and are accessible on, Zenodo:
https://zenodo.org/records/4299924
Authors
- MAGRO, DANIEL ;
- ZARB ADAMI, KRISTIAN ;
- De Marco, Andrea ;
- Riggi, Simone ;
- Sciacca, Eva