Automated Author ProfileCsabai, István
Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
Csabai, István
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: 0.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Generated whistlers with pixel-segmented separate masks for each trace. File naming:
- train_images.tar.gz contains all the train images (>10 GB)
- example_images.zip contains 5 spectrograms and the corresponding segmentation masks. (~5 MB))
- {id}rothera{n}_y.png, {id}_rothera_x.png, where {id} is a unique ID to match the masks and the spectrogram image, {n} enumerates the whistler traces within a spectrogram. Files ending _y.png are the bitmask images, while the _x.png files are the spectrograms. Opening the bitmasks in a regular image viewer might show only a black image. In this case, opening as a matrix (eg via matplotlib or opencv in python), converting to binary values, and visualizing them can help. To be updated upon acceptance of our research paper.
Authors
- Pataki, Bálint Ármin ;
- Lichtenberger, János ;
- Clilverd, Mark ;
- Máthé, Gergely ;
- Csabai, István
Generated whistlers with pixel-segmented separate masks for each trace. File naming:
- train_images.tar.gz contains all the train images (>10 GB)
- example_images.zip contains 5 spectrograms and the corresponding segmentation masks. (~5 MB))
- {id}rothera{n}_y.png, {id}_rothera_x.png, where {id} is a unique ID to match the masks and the spectrogram image, {n} enumerates the whistler traces within a spectrogram. Files ending _y.png are the bitmask images, while the _x.png files are the spectrograms. Opening the bitmasks in a regular image viewer might show only a black image. In this case, opening as a matrix (eg via matplotlib or opencv in python), converting to binary values, and visualizing them can help. To be updated upon acceptance of our research paper.
Authors
- Pataki, Bálint Ármin ;
- Lichtenberger, János ;
- Clilverd, Mark ;
- Máthé, Gergely ;
- Csabai, István