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LICENSE

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+CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
+
+The person who associated a work with this deed has dedicated the work to the
+public domain by waiving all of his or her rights to the work worldwide under
+copyright law, including all related and neighboring rights, to the extent
+allowed by law.
+
+You can copy, modify, distribute and perform the work, even for commercial
+purposes, all without asking permission.
+
+For more information: https://creativecommons.org/publicdomain/zero/1.0/

README.md

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----
-license: cc0-1.0
----
+---
+license: cc0-1.0
+task_categories:
+  - tabular-classification
+size_categories:
+  - 1K<n<10K
+---
+
+# Outbreak Containment Data
+
+Simulated outbreak data for epidemiological modeling.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("epidata-research/outbreak-containment-data")
+df = dataset["train"].to_pandas()
+```
+
+Or use the provided loader:
+
+```python
+from loader import load_data
+
+df = load_data()
+```
+
+## Schema
+
+### Metrics
+
+| Column | Type | Description |
+|--------|------|-------------|
+| immune_diversity | float | Normalized metric |
+| transmission_coherence | float | Normalized metric |
+| healthcare_capacity | float | Normalized metric |
+| containment_index | float | Normalized metric |
+
+### Categorical Variables
+
+| Column | Type | Description |
+|--------|------|-------------|
+| pathogen_type | string | Categorical variable |
+| region_density | string | Categorical variable |
+
+## Statistics
+
+- **Rows**: 1000
+- **Columns**: 6
+
+## License
+
+CC0 1.0 Universal (Public Domain)
+
+---
+
+*Note: Homogeneity is the breeding ground of plague.*

data/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2e138ff3c04ab0dd2b06d0156ad497c69411c1a06e0dd84c1963d9bb93962de0
+size 41663

loader.py

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+"""
+Data loader for outbreak-containment-data
+"""
+
+import pandas as pd
+from pathlib import Path
+
+
+def load_data(split: str = "train") -> pd.DataFrame:
+    """
+    Load the dataset.
+    
+    Args:
+        split: Data split ("train" by default)
+    
+    Returns:
+        DataFrame with the loaded data
+    """
+    data_dir = Path(__file__).parent / "data"
+    parquet_path = data_dir / f"{split}.parquet"
+    
+    if not parquet_path.exists():
+        raise FileNotFoundError(f"Data file not found: {parquet_path}")
+    
+    return pd.read_parquet(parquet_path)
+
+
+def get_columns() -> list:
+    """Return list of column names."""
+    return ['immune_diversity', 'transmission_coherence', 'healthcare_capacity', 'containment_index']
+
+
+if __name__ == "__main__":
+    df = load_data()
+    print(f"Loaded {len(df)} rows")
+    print(df.head())

schema.json

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+{
+  "schema_version": "1.0",
+  "columns": {
+    "immune_diversity": {
+      "type": "float",
+      "range": [
+        0.02153563774324717,
+        0.9925414602206185
+      ]
+    },
+    "transmission_coherence": {
+      "type": "float",
+      "range": [
+        0.009676416116400879,
+        0.9691074498232646
+      ]
+    },
+    "healthcare_capacity": {
+      "type": "float",
+      "range": [
+        0.05250272826469958,
+        0.9969587313251344
+      ]
+    },
+    "containment_index": {
+      "type": "float",
+      "range": [
+        0.0,
+        0.577262341412194
+      ]
+    },
+    "pathogen_type": {
+      "type": "string",
+      "values": [
+        "fungal",
+        "bacterial",
+        "parasitic",
+        "viral"
+      ]
+    },
+    "region_density": {
+      "type": "string",
+      "values": [
+        "suburban",
+        "urban",
+        "rural",
+        "megacity"
+      ]
+    }
+  },
+  "note": "Normalized metrics for cross-domain analysis"
+}