juliensimon/lunar-sample-geochemistry

Lunar Sample Geochemistry (Astromat Synthesis)

Credit: NASA/Apollo 8

Part of the Planetary Science Datasets collection on Hugging Face.

A comprehensive compilation of 58,289 geochemical analyses from 14,379 unique lunar samples collected by the Apollo, Luna, and Chang'e 5 missions. Covers major oxide compositions, trace element abundances, and rare earth elements from 980 published studies, compiled by the Astromat/EarthChem project.

Dataset description

Between 1969 and 2020, six Apollo missions (382 kg), three Soviet Luna missions (326 g), and China's Chang'e 5 mission (1.73 kg) returned samples from the Moon's surface. These samples — basalts, breccias, soils, and crustal rocks — have been analyzed in laboratories worldwide for over 50 years, producing a rich geochemical database that underpins our understanding of the Moon's origin, differentiation, and volcanic history.

This dataset compiles the results of those analyses into a single tabular format. Major oxides (SiO2, TiO2, Al2O3, FeO, MgO, CaO, etc.) in weight percent define the bulk composition and are used to classify lunar rock types (high-Ti basalt, low-Ti basalt, KREEP-rich, ferroan anorthosite). Trace elements and rare earth elements in ppm provide diagnostic signatures for petrogenetic processes: partial melting, fractional crystallization, and impact mixing.

The data was compiled by the Astromat Synthesis project (EarthChem Library) from peer-reviewed publications spanning 1970 to 2025, making it the most comprehensive single source for lunar sample chemistry.

Sample types

Type	Analyses
ROCK>>BRECCIA	17,455
ROCK>>BASALT	15,949
SOIL	12,703
ROCK>>CRUSTAL	1,713
ROCK>>CORE	1,179
ROCK>>UNCLASSIFIED	132

Missions

Mission	Analyses
Other	58,289

Quick stats

• 58,289 total analyses from 14,379 unique samples
• 980 published references
• 55 columns (metadata + major oxides + trace elements + REEs)

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/lunar-sample-geochemistry", split="train")
df = ds.to_pandas()

# TAS-like plot for lunar basalts
import matplotlib.pyplot as plt
basalts = df[df["sample_type"].str.contains("BASALT", na=False)]
valid = basalts.dropna(subset=["sio2_wt_pct", "tio2_wt_pct"])
plt.scatter(valid["sio2_wt_pct"], valid["tio2_wt_pct"], alpha=0.3, s=10)
plt.xlabel("SiO2 (wt%)")
plt.ylabel("TiO2 (wt%)")
plt.title("Lunar Basalt Compositions")
plt.show()

# Compare missions
print(df.groupby("mission")[["sio2_wt_pct", "feo_wt_pct", "mgo_wt_pct"]].mean())

# Filter Apollo 15 samples
a15 = df[df["mission"] == "Apollo 15"]
print(f"Apollo 15: {len(a15)} analyses")

Data source

Astromat Synthesis Compilation: Lunar samples v.1 (February 2025). EarthChem Library, doi:10.60520/IEDA/113696. CC-BY-4.0.

Related datasets

• Lunar Craters (Robbins 2019) — 2M+ Moon craters
• Meteorite Database — Named meteorites with classification
• Meteorite Landings — 45K+ meteorite fall records
• IAU Planetary Nomenclature — Named lunar features

Pipeline

Source code: juliensimon/space-datasets

Support

If you find this dataset useful, please give it a ❤️ on the dataset page and share feedback in the Community tab! Also consider giving a ⭐ to the space-datasets repo.

Citation

@dataset{lunar_geochemistry,
  author = {Simon, Julien},
  title = {Lunar Sample Geochemistry (Astromat Synthesis)},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/lunar-sample-geochemistry},
  note = {Based on Astromat Synthesis Compilation via EarthChem Library}
}

License

CC-BY-4.0