juliensimon/global-meteor-network
Global Meteor Network Trajectory Data Credit: NASA/ESA Part of a dataset collection on Hugging Face. Dataset description Individual meteor trajectory solutions from the Global Meteor Network (GMN), a worldwide network of 500+ all-sky cameras operated by volunteer astronomers. Each row is one detected meteor with orbital elements derived from multi-station triangulation. The GMN was founded in 2018 and has grown to cover all longitudes from Europe, the… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/global-meteor-network.
Global Meteor Network Trajectory Data
<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
Individual meteor trajectory solutions from the Global Meteor Network (GMN), a worldwide network of 500+ all-sky cameras operated by volunteer astronomers. Each row is one detected meteor with orbital elements derived from multi-station triangulation.
The GMN was founded in 2018 and has grown to cover all longitudes from Europe, the Americas, Australia, and beyond. When at least two cameras simultaneously detect a meteor, the geometry of their positions allows triangulation of the atmospheric trajectory. Combined with timing, this yields the meteoroid's velocity at the top of the atmosphere and — after correction for Earth's gravitational attraction — the heliocentric orbit before encounter. The result is a complete set of Keplerian elements (a, e, i, ω, Ω, q) that places each detected meteoroid in the Solar System context.
Unlike the IAU Meteor Shower Database which catalogs mean radiant/orbit solutions per shower, this dataset contains individual meteor detections with full orbital parameters. The majority of detections are sporadic meteors (showercode = '...') with no known parent body; shower members are identified by matching with the IAU shower list. The nstations column provides a quality indicator: two-station solutions are the minimum for a valid trajectory, while higher counts improve the accuracy of both the radiant and the orbital elements.
This dataset is valuable for: identifying new meteor streams, studying the dynamical evolution of meteoroid trails, searching for meteoroids of potential interstellar origin (high eccentricity or retrograde orbits), correlating meteor detections with asteroid/comet close approaches, and building ML models for meteor source classification.
This dataset is suitable for tabular classification, tabular regression tasks.
Schema
Quick stats
- 3,498,969 meteor trajectories (2018-12-10 to 2026-09-21)
- 953,612 shower meteors (27%) and 2,545,357 sporadics (73%)
- Top 5 showers by count: PER (174,287), GEM (104,735), ORI (52,467), SDA (44,684), ETA (33,443)
- Median geocentric velocity: 41.4 km/s; fastest detected: 84.4 km/s
Usage
from datasets import load_dataset
ds = load_dataset("juliensimon/global-meteor-network", split="train")
df = ds.to_pandas()from datasets import load_dataset
import pandas as pd
ds = load_dataset("juliensimon/global-meteor-network", split="train")
df = ds.to_pandas()
# Shower vs sporadic breakdown
print(df["shower_code"].value_counts().head(10))
# Velocity distribution by shower
import matplotlib.pyplot as plt
showers = df[df["shower_code"] != "..."]
top = showers["shower_code"].value_counts().head(6).index
showers[showers["shower_code"].isin(top)].boxplot(
column="v_g_kms", by="shower_code", figsize=(10, 5)
)
plt.suptitle("")
plt.title("Geocentric Velocity Distribution by Meteor Shower")
plt.ylabel("v_g (km/s)")
plt.show()
# Radiant sky map
fig, ax = plt.subplots(figsize=(12, 6))
scatter = ax.scatter(
df["radiant_ra_deg"], df["radiant_dec_deg"],
c=df["v_g_kms"], s=0.5, cmap="plasma", alpha=0.3
)
plt.colorbar(scatter, label="v_g (km/s)")
ax.set_xlabel("RA (degrees)")
ax.set_ylabel("Dec (degrees)")
ax.set_title("GMN Meteor Radiants on the Sky")
plt.show()Data source
https://globalmeteornetwork.org/data/
Update schedule
Daily at 10:00 UTC
Related datasets
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About the author
Created by Julien Simon — AI Operating Partner at Fortino Capital. Part of the Space Datasets collection.
Citation
@dataset{global_meteor_network,
title = {Global Meteor Network Trajectory Data},
author = {Simon, Julien},
year = {2026},
url = {https://huggingface.co/datasets/juliensimon/global-meteor-network},
note = {Derived from Global Meteor Network, https://globalmeteornetwork.org/data/},
publisher = {Hugging Face}
}