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LICENSE.md

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+# License Information
+
+## Public Domain (CC0 1.0)
+
+This dataset is released under **CC0 1.0 Universal (Public Domain Dedication)**.
+
+### Source Data License
+
+The underlying data comes from **NASA Meteoritical Bulletin**, which is a U.S. Government work and in the public domain under Title 17, Section 105 of the United States Code.
+
+### What This Means
+
+**You are free to:**
+- Share: Copy and redistribute the material in any medium or format
+- Adapt: Remix, transform, and build upon the material for any purpose, even commercially
+
+**No conditions:** You do not need to give attribution (though it is appreciated).
+
+### Recommended Citation
+
+```
+Steuber, L. (2026). Large Meteorites 1kg+ - NASA Catalog [Dataset].
+Kaggle. https://www.kaggle.com/datasets/lucassteuber/large-meteorites-1kg-nasa
+```
+
+Original data: NASA Open Data Portal
+https://data.nasa.gov/Space-Science/Meteorite-Landings/gh4g-9sfh
+
+### Full License Text
+
+https://creativecommons.org/publicdomain/zero/1.0/

README.md

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+---
+license: cc0-1.0
+task_categories:
+  - feature-extraction
+language:
+  - en
+tags:
+  - meteorites
+  - nasa
+  - astronomy
+  - space
+  - geospatial
+  - earth-science
+  - specimens
+  - planetary-science
+pretty_name: Large Meteorites (1kg+) - NASA Catalog
+size_categories:
+  - 1K<n<10K
+dataset_info:
+  features:
+    - name: latitude
+      dtype: float64
+    - name: longitude
+      dtype: float64
+    - name: name
+      dtype: string
+    - name: description
+      dtype: string
+    - name: category
+      dtype: string
+    - name: date
+      dtype: string
+    - name: mass_g
+      dtype: float64
+    - name: meteorite_class
+      dtype: string
+    - name: fall_type
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 4871
+---
+
+# Large Meteorites (1kg+) - NASA Catalog
+
+4,871 significant meteorite specimens weighing at least 1 kilogram.
+
+## What's Inside
+
+- 4,871 large meteorites (1kg minimum)
+- Complete coordinates for mapping
+- Mass data in grams
+- Petrologic classifications
+- Fall vs. found distinction
+- Date range: 1399-2013
+
+## Why Large Meteorites Matter
+
+Large meteorites are rare finds:
+- Enough material for multiple scientific analyses
+- Often displayed in museums worldwide
+- Can be sliced for study while preserving the specimen
+- More likely to have survived atmospheric entry intact
+
+## Filtering Criteria
+
+- Mass threshold: 1,000+ grams (1 kg)
+- Reduces NASA catalog by 89.3%
+- All records have valid mass data
+
+## Mass Statistics
+
+| Metric | Value |
+|--------|-------|
+| Minimum | 1.0 kg |
+| Median | 3.6 kg |
+| Maximum | 60,000 kg (60 tonnes) |
+
+## Record Structure
+
+```json
+{
+  "latitude": -27.5,
+  "longitude": 132.0,
+  "name": "Hoba",
+  "description": "Iron, IVB - 60000000g",
+  "category": "large_meteorites",
+  "date": "1920-01-01",
+  "mass_g": 60000000,
+  "meteorite_class": "Iron, IVB",
+  "fall_type": "Found"
+}
+```
+
+## Usage
+
+```python
+import json
+import pandas as pd
+
+with open('large_meteorites.json') as f:
+    meteorites = json.load(f)
+
+df = pd.DataFrame(meteorites)
+print(f"Total meteorites: {len(df):,}")
+
+# Find the heaviest
+heaviest = df.nlargest(10, 'mass_g')[['name', 'mass_g', 'meteorite_class']]
+
+# Filter by type
+irons = df[df['meteorite_class'].str.contains('Iron', na=False)]
+```
+
+## Source & License
+
+**Public Domain** - U.S. Government Work
+
+Data from NASA Meteoritical Bulletin via Open Data Portal.
+
+https://data.nasa.gov/Space-Science/Meteorite-Landings/gh4g-9sfh
+
+## Distribution
+
+- **Kaggle**: [lucassteuber/large-meteorites](https://www.kaggle.com/datasets/lucassteuber/large-meteorites)
+- **HuggingFace**: [lukeslp/large-meteorites](https://huggingface.co/datasets/lukeslp/large-meteorites)
+- **GitHub Gist**: [Demo Notebook](https://gist.github.com/lukeslp/1e62406b77598b0fc9fefbe3c3d3badb)
+
+## Author
+
+Luke Steuber | luke@lukesteuber.com | @lukesteuber.com (Bluesky)
+
+## Structured Data (JSON-LD)
+
+```json
+{
+  "@context": "https://schema.org",
+  "@type": "Dataset",
+  "name": "Large Meteorites (1kg+) - NASA Catalog",
+  "description": "4,871 significant meteorite specimens weighing at least 1 kilogram from NASA's Meteoritical Bulletin. Includes mass, classification, and fall type data.",
+  "url": "https://www.kaggle.com/datasets/lucassteuber/large-meteorites",
+  "sameAs": "https://huggingface.co/datasets/lukeslp/large-meteorites",
+  "license": "https://creativecommons.org/publicdomain/zero/1.0/",
+  "creator": {
+    "@type": "Person",
+    "name": "Luke Steuber",
+    "url": "https://lukesteuber.com"
+  },
+  "keywords": ["meteorites", "NASA", "astronomy", "space", "planetary science", "geospatial"],
+  "temporalCoverage": "1399/2013",
+  "spatialCoverage": {
+    "@type": "Place",
+    "name": "Global"
+  },
+  "distribution": [
+    {
+      "@type": "DataDownload",
+      "encodingFormat": "application/json",
+      "contentUrl": "https://www.kaggle.com/datasets/lucassteuber/large-meteorites"
+    }
+  ]
+}
+```

dataset-metadata.json

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+{
+  "title": "Large Meteorites (1kg+) - NASA Catalog",
+  "id": "lucassteuber/large-meteorites-1kg-nasa",
+  "licenses": [{"name": "CC0-1.0"}],
+  "subtitle": "4,871 significant meteorite specimens weighing at least 1 kilogram",
+  "description": "NASA meteorite catalog filtered to substantial specimens (1kg+). These larger meteorites are rare, scientifically valuable, and often found in museum collections worldwide.",
+  "isPrivate": false,
+  "keywords": [
+    "meteorites",
+    "nasa",
+    "astronomy",
+    "space",
+    "geospatial",
+    "earth-science",
+    "specimens",
+    "planetary-science"
+  ]
+}

demo_notebook.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["# Large Meteorites (1kg+) - NASA Catalog\n\nExplore **4,871 significant meteorite specimens** weighing at least 1 kilogram.\n\n**Dataset Highlights:**\n- Minimum mass: 1 kg\n- Maximum mass: 60 tonnes\n- Dates from 1399 to 2013\n- Includes petrologic classifications"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["import json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport warnings\nwarnings.filterwarnings('ignore')\n\nplt.style.use('seaborn-v0_8-whitegrid')\nprint('Libraries loaded')"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## 1. Load Dataset"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["with open('large_meteorites.json') as f:\n    data = json.load(f)\n\ndf = pd.DataFrame(data)\nprint(f'Total meteorites: {len(df):,}')\ndf.head()"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## 2. Mass Distribution"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["df['mass_kg'] = df['mass_g'] / 1000\n\nprint('Mass Statistics (kg):')\nprint('=' * 40)\nprint(f'Minimum: {df[\"mass_kg\"].min():.1f} kg')\nprint(f'Median: {df[\"mass_kg\"].median():.1f} kg')\nprint(f'Mean: {df[\"mass_kg\"].mean():.1f} kg')\nprint(f'Maximum: {df[\"mass_kg\"].max():,.0f} kg')"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["fig, ax = plt.subplots(figsize=(10, 5))\nax.hist(np.log10(df['mass_kg']), bins=50, color='sienna', edgecolor='black')\nax.set_xlabel('Log10(Mass in kg)')\nax.set_ylabel('Frequency')\nax.set_title('Meteorite Mass Distribution (Log Scale)', fontweight='bold')\nplt.tight_layout()\nplt.show()"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## 3. Heaviest Meteorites"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["heaviest = df.nlargest(15, 'mass_g')[['name', 'mass_kg', 'meteorite_class', 'fall_type']]\nprint('Top 15 Heaviest Meteorites:')\nprint('=' * 70)\nfor i, row in heaviest.iterrows():\n    print(f\"{row['name']:30s} {row['mass_kg']:12,.0f} kg  {row['meteorite_class']}\")"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## 4. Classification Distribution"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["# Extract main class (first part before comma or space)\ndf['main_class'] = df['meteorite_class'].str.split('[,\\s]').str[0]\nclass_counts = df['main_class'].value_counts().head(15)\n\nprint('Top 15 Meteorite Classes:')\nprint('=' * 40)\nfor cls, count in class_counts.items():\n    print(f'{cls:20s} {count:6,}')"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["fig, ax = plt.subplots(figsize=(10, 6))\nclass_counts.plot(kind='barh', ax=ax, color='brown')\nax.set_xlabel('Number of Meteorites')\nax.set_title('Top Meteorite Classifications', fontweight='bold')\nplt.tight_layout()\nplt.show()"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## 5. Falls vs Finds"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["fall_counts = df['fall_type'].value_counts()\nprint('Fall Type Distribution:')\nprint('=' * 40)\nfor fall_type, count in fall_counts.items():\n    pct = count / len(df) * 100\n    print(f'{fall_type:10s} {count:6,} ({pct:.1f}%)')"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## 6. Geographic Distribution"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["valid_coords = df[(df['latitude'].notna()) & (df['longitude'].notna())]\nprint(f'Meteorites with coordinates: {len(valid_coords):,}')\n\nfig, ax = plt.subplots(figsize=(14, 7))\nscatter = ax.scatter(valid_coords['longitude'], valid_coords['latitude'], \n                     c=np.log10(valid_coords['mass_kg']), cmap='YlOrRd',\n                     alpha=0.5, s=10)\nax.set_xlabel('Longitude')\nax.set_ylabel('Latitude')\nax.set_title('Large Meteorite Locations (color = log mass)', fontweight='bold')\nplt.colorbar(scatter, label='Log10(Mass kg)')\nplt.tight_layout()\nplt.show()"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## 7. Temporal Distribution"]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ["df['year'] = pd.to_datetime(df['date'], errors='coerce').dt.year\nyearly = df[df['year'] > 1800].groupby('year').size()\n\nfig, ax = plt.subplots(figsize=(12, 5))\nyearly.plot(ax=ax, color='darkred')\nax.set_xlabel('Year')\nax.set_ylabel('Number of Meteorites')\nax.set_title('Large Meteorite Discoveries by Year (1800+)', fontweight='bold')\nplt.tight_layout()\nplt.show()"]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": ["## Conclusion\n\nThis notebook demonstrated:\n- Loading 4,871 large meteorites (1kg+)\n- Mass distribution analysis\n- Classification breakdown (L chondrites dominate)\n- Falls vs Finds (97% are finds)\n- Geographic and temporal patterns\n\n**Source**: NASA Meteoritical Bulletin\n\n**Author**: Luke Steuber | @lukesteuber.com (Bluesky)"]
+    }
+  ]
+}