Data
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Dataset Overview
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Time Span
2010 – 2025
Samples
2.1M
Features
35 / 145
Cycles
24 + 25
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Specifications
| Source | NASA SDO / HMI · SHARP CEA data product |
|---|---|
| Time span | 2010-05 — 2025 |
| Cadence | 12 minutes |
| Base parameters | 25 raw SHARP parameters (see BASE_FEATS_25.txt) |
| Engineered features | … |
| Label | … |
| Split protocol | Stratified Cycle Splitting (chronological, AR-disjoint) |
| File format | .parquet (Apache Parquet, snappy compression) |
| Version | v1.0-preview |
Split Protocol Schematic
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Files
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| File | Size | |
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df_train.parquet
Training set — used for model fitting (Cycle 24/25 chronological, AR-disjoint)
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~ 1.1 GB | Request |
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df_val.parquet
Validation set — used for early stopping and threshold tuning
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~ 160 MB | Request |
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df_test.parquet
Test set — held out, used only for final reporting (no leakage)
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~ 180 MB | Request |
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BASE_FEATS_25.txt
25 raw SHARP parameter names (before feature engineering)
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< 1 KB | Request |
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selected_features_35.json
35 final features used by the model (Random-Forest selected)
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< 5 KB | Request |
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split_info.json
AR-level split metadata (which AR went to train / val / test)
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< 50 KB | Request |
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README.md
Documentation, usage instructions, and citation guidelines
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< 10 KB | Request |
📦 Get the entire dataset in one click
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Quick Start
Once you have the files, load and explore the dataset in a few lines:
import pandas as pd import json # 1. Load splits df_train = pd.read_parquet('df_train.parquet') df_val = pd.read_parquet('df_val.parquet') df_test = pd.read_parquet('df_test.parquet') # 2. Load feature list with open('selected_features_35.json') as f: feats = json.load(f) X_train, y_train = df_train[feats], df_train['label'] print(f'Train: {len(X_train):,} samples, positive rate: {y_train.mean():.3f}')
Reproducing the published benchmark
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License & Citation
License
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Cite this dataset
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Contact & Support
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