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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: int64 |
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- name: antenna |
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dtype: string |
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- name: datetime |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1833634770.762 |
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num_examples: 113409 |
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- name: validation |
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num_bytes: 228715568.866 |
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num_examples: 14253 |
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- name: test |
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num_bytes: 230174837.398 |
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num_examples: 14271 |
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download_size: 2352964096 |
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dataset_size: 2292525177.026 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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# e-Callisto Solar Flare Detection Dataset |
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[Institute of Data Science i4Ds, FHNW](https://i4ds.ch) |
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Compiled by [Vincenzo Timmel | Kenfus](https://github.com/kenfus) |
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## Overview |
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This dataset comprises radio spectra from the [e-Callisto solar spectrometer network](https://www.e-callisto.org/index.html), annotated according to the [e-Callisto Label List by Christian Monstein](http://soleil.i4ds.ch/solarradio/data/BurstLists/2010-yyyy_Monstein/). It is designed for training machine learning models to automatically detect and classify solar flares, using data collected via the [ecallisto_ng Package](https://github.com/i4Ds/ecallisto_ng). |
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## Non Radio-Sunburst Images |
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For every Radio-Sunburst image, five Non-Sunburst images are included (Label 0). |
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## Data Collection |
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The dataset encompasses observations from several antennas, each documenting specific periods of data collection. Below is the updated list of stations with their respective data collection ranges: |
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| Antenna | Min Date | Max Date | |
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|-----------------------|------------|------------| |
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| ALASKA-COHOE_63 | 2022-04-09 | 2024-02-22 | |
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| ALASKA-HAARP_62 | 2021-11-18 | 2024-02-23 | |
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| ALGERIA-CRAAG_59 | 2021-04-23 | 2024-02-22 | |
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| ALMATY_58 | 2021-02-28 | 2024-02-23 | |
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| AUSTRIA-UNIGRAZ_01 | 2021-01-20 | 2024-02-23 | |
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| Australia-ASSA_02 | 2021-02-13 | 2021-12-09 | |
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| Australia-ASSA_62 | 2021-12-10 | 2024-02-22 | |
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| BIR_01 | 2021-04-17 | 2024-02-14 | |
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| EGYPT-Alexandria_02 | 2021-08-20 | 2024-02-21 | |
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| GERMANY-DLR_63 | 2022-11-11 | 2024-02-22 | |
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| GLASGOW_01 | 2022-01-07 | 2024-02-22 | |
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| HUMAIN_59 | 2021-01-20 | 2024-02-23 | |
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| INDIA-GAURI_01 | 2022-04-20 | 2024-02-21 | |
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| INDIA-OOTY_02 | 2021-12-09 | 2024-02-23 | |
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| KASI_59 | 2021-04-22 | 2024-02-23 | |
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| MEXART_59 | 2021-02-18 | 2024-02-21 | |
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| MEXICO-FCFM-UANL_01 | 2023-09-02 | 2024-02-21 | |
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| MEXICO-LANCE-B_62 | 2022-03-30 | 2022-08-02 | |
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| MONGOLIA-UB_01 | 2021-03-01 | 2024-02-16 | |
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| MRO_59 | 2021-03-01 | 2024-02-16 | |
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| MRO_61 | 2021-02-28 | 2024-02-16 | |
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| NORWAY-EGERSUND_01 | 2022-10-16 | 2024-02-23 | |
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| SSRT_59 | 2022-10-30 | 2024-02-23 | |
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| SWISS-Landschlacht_62 | 2021-10-05 | 2024-02-23 | |
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| TRIEST_57 | 2021-01-20 | 2024-02-23 | |
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| USA-ARIZONA-ERAU_01 | 2022-05-15 | 2024-02-22 | |
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## Data Augmentation |
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Data augmentation is applied by subtracting random minutes before the start of a detected radio sunburst, thereby generating 15-minute images that include the onset of a radio-sunburst. |
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## Caution |
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Preprocessing, including label cleanup based on specific assumptions, has been applied to the dataset. Users should note that the labels might not be entirely accurate, reflecting potential inaccuracies |
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## Distribution |
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**Train** |
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**Validation** |
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**Test** |
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