Automated Organization ProfileDiRAC Institute and the Department of Astronomy, University of Washington
DiRAC Institute and the Department of Astronomy, University of Washington
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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: 4.0 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plus -- This version corresponds to commit: 956b5d555f58800c01a74b3977e0a3b5476ea9cd and tag v0.0.8.dataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.model_0.yaml: Configuration file associated with model_0.pt.model_z_0.pt: Same as model_0.pt, but trained using redshift information.model_z_0.yaml: Configuration file associated with model_z_0.pt.early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.probs_concat.csv: Spectroscopic set's classification results without using redshift information.probs_z_concat.csv: Spectroscopic set's classification results using redshift information.probs_photometric_v2.mrt: Superphot+'s probabilities for the photometric set without using redshift information. Updated to correct for missing IAU names.
Authors
- de Soto, Kaylee ;
- Villar, Ashley ;
- Berger, Edo ;
- Gomez, Sebastian ;
- Hosseinzadeh, Griffin ;
- Branton, Doug ;
- Campos, Sandro ;
- DeLucchi, Melissa ;
- Kubica, Jeremy ;
- Lynn, Olivia ;
- Malanchev, Konstantin ;
- Malz, Alex I.
This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plus -- This version corresponds to commit: 956b5d555f58800c01a74b3977e0a3b5476ea9cd and tag v0.0.8.dataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.model_0.yaml: Configuration file associated with model_0.pt.model_z_0.pt: Same as model_0.pt, but trained using redshift information.model_z_0.yaml: Configuration file associated with model_z_0.pt.early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.probs_concat.csv: Spectroscopic set's classification results without using redshift information.probs_z_concat.csv: Spectroscopic set's classification results using redshift information.probs_photometric_v2.mrt: Superphot+'s probabilities for the photometric set without using redshift information. Updated to correct for missing IAU names.
Authors
- de Soto, Kaylee ;
- Villar, Ashley ;
- Berger, Edo ;
- Gomez, Sebastian ;
- Hosseinzadeh, Griffin ;
- Branton, Doug ;
- Campos, Sandro ;
- DeLucchi, Melissa ;
- Kubica, Jeremy ;
- Lynn, Olivia ;
- Malanchev, Konstantin ;
- Malz, Alex I.
This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plusdataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.model_0.yaml: Configuration file associated with model_0.pt.model_z_0.pt: Same as model_0.pt, but trained using redshift information.model_z_0.yaml: Configuration file associated with model_z_0.pt.early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.probs_concat.csv: Spectroscopic set's classification results without using redshift information.probs_z_concat.csv: Spectroscopic set's classification results using redshift information.probs_photometric.mrt: Superphot+'s probabilities for the photometric set without using redshift information.
Authors
- de Soto, Kaylee ;
- Villar, Ashley ;
- Berger, Edo ;
- Gomez, Sebastian ;
- Hosseinzadeh, Griffin ;
- Branton, Doug ;
- Campos, Sandro ;
- DeLucchi, Melissa ;
- Kubica, Jeremy ;
- Lynn, Olivia ;
- Malanchev, Konstantin ;
- Malz, Alex I.