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juliensimon/meteorite-database

Meteorite Database Credit: NASA/ESA Part of a dataset collection on Hugging Face. Dataset description Catalogue of known meteorites sourced from Wikidata, covering mass, classification, fall date, country of recovery, and geographic coordinates. Meteorites are extraterrestrial rocks that survive passage through Earth's atmosphere and reach the surface. They are classified by mineralogy and petrology (e.g., chondrites, achondrites, iron meteorites) and… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/meteorite-database.

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Dataset Card

Meteorite Database

<div align="center"> <img src="banner.jpg" alt="Rosetta spacecraft approaching Comet 67P/Churyumov-Gerasimenko" width="400"> <p><em>Credit: NASA/ESA</em></p> </div>

Part of a [dataset collection](https://huggingface.co/collections/juliensimon/orbital-mechanics-datasets-69c24caca4ab3934c9856994) on Hugging Face.

Dataset description

Catalogue of known meteorites sourced from Wikidata, covering mass, classification, fall date, country of recovery, and geographic coordinates.

Meteorites are extraterrestrial rocks that survive passage through Earth's atmosphere and reach the surface. They are classified by mineralogy and petrology (e.g., chondrites, achondrites, iron meteorites) and recorded either as falls (witnessed descent) or finds (recovered without observation).

This dataset aggregates Wikidata entries for all entities of type Q60186 (meteorite), pulling structured properties including mass (P2067), fall/discovery date (P585/P575), country (P17), coordinates (P625), and mineralogical class (via P31 subclass hierarchy). It complements NASA and Meteoritical Society databases with Wikidata's multilingual, cross-linked knowledge graph.

This dataset is suitable for tabular classification tasks.

Schema

Column	Type	Description	Sample	Null %
`wikidata_id`	str	Wikidata entity ID (e.g. Q1029); stable cross-reference key for linking to other Wikidata properties	Q138638076	0.0%
`name`	string	Official meteorite name assigned by the Meteoritical Society (e.g., 'Allende', 'NWA 7034', 'Chelyabinsk'); typically location of find plus sequence number	2026 Koblenz meteor	0.0%
`fall_date`	str	Date of observed fall or discovery/recovery in ISO format (YYYY-MM-DD); null for historical finds without a recorded date; precision often year-only (day defaults to 01)	1880-01-01	53.9%
`mass_g`	float64	Total known mass in grams; null if unknown; range from <1 g (tiny fragments) to ~60,000,000 g (Hoba, the largest known meteorite)	21.0	68.8%
`classification`	string	Meteoritical Society mineralogical/petrological class (e.g., 'L5', 'CM2', 'Iron IIIAB'); letters = chemical group, numbers = petrologic grade; null if not recorded in Wikidata	chondrite	89.4%
`country`	string	Country of recovery (English label from Wikidata); null for finds without a recorded country or in international territory (e.g., Antarctica)	Germany	81.8%
`latitude`	float64	Recovery location latitude in decimal degrees (positive = N, negative = S); null for historical or poorly documented finds	50.775	64.7%
`longitude`	float64	Recovery location longitude in decimal degrees (positive = E, negative = W); null for historical or poorly documented finds	6.08333	64.7%

Quick stats

—1,184 meteorites total
—370 with recorded mass
—418 with geographic coordinates
—126 with classification
—55 countries of recovery
—Heaviest: Allende meteorite (2,000,000 g)
—Top countries: United States (49), Australia (13), Germany (11), Argentina (10), France (9)
—Top classifications: iron meteorite (20), H chondrite (17), ordinary chondrite (15), chondrite (13), L chondrite (11)

Usage

python

from datasets import load_dataset

ds = load_dataset("juliensimon/meteorite-database", split="train")
df = ds.to_pandas()

python

from datasets import load_dataset

ds = load_dataset("juliensimon/meteorite-database", split="train")
df = ds.to_pandas()

# Heaviest meteorites
print(df.nlargest(10, "mass_g")[["name", "mass_g", "country", "classification"]])

# Meteorites by country
import matplotlib.pyplot as plt
df["country"].value_counts().head(15).plot.barh()
plt.xlabel("Count")
plt.ylabel("Country")
plt.title("Meteorites by Country of Recovery")
plt.tight_layout()
plt.show()

# Mass distribution (log scale)
import numpy as np
masses = df["mass_g"].dropna()
plt.hist(np.log10(masses[masses > 0]), bins=50)
plt.xlabel("log10(mass in grams)")
plt.ylabel("Count")
plt.title("Meteorite Mass Distribution")
plt.show()

Data source

https://www.wikidata.org/

Related datasets

—juliensimon/impact-craters

—juliensimon/fireball-bolide-events

If you find this dataset useful, please consider giving it a like on Hugging Face. It helps others discover it.

About the author

Created by Julien Simon — AI Operating Partner at Fortino Capital. Part of the Space Datasets collection.

Citation

bibtex

@dataset{meteorite_database,
  title = {Meteorite Database},
  author = {Simon, Julien},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/meteorite-database},
  note = {Derived from Wikidata contributors, https://www.wikidata.org/},
  publisher = {Hugging Face}
}

License

CC0-1.0