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This dataset contains the data for the paper 'Using Multiple Instance Learning for Explainable Solar Flare Prediction' (arxiv pre-print) . It comes as a compressed Python Numpy-File and contains the following variables: Name Shape Description data (10'000, 1100, 240) 10'000 Bags of zero-padded spectrograms data_scaled (10'000, 1100, 240) Like data, but standard-scaled masks (10'000, 1100) Masks that indicate where spectrograms have been zero-padded groups (10'000,) Observation group the bag is assigned to obs_ids (10'000,) Observation ID the bag is assigned to obs_classes (10'000,) Observation class (AR/PF) the bag is assigned to raster_pos (10'000,) Raster position number the bag was taken from (always 0 for sit-and-stare) folds (10'000,) Validation fold for the particular observation group To load the e.g. the variable 'data', use Python and Numpy: import numpy as np f = np.load("IRISMIL_dataset_10000_bags.npz", allow_pickle=True) f['data']
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 4 |

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