juliensimon/gcvs-variable-stars

General Catalogue of Variable Stars (GCVS)

The General Catalogue of Variable Stars (GCVS) is the canonical reference catalog of variable stars, maintained since 1948 by the Sternberg Astronomical Institute at Moscow State University. Currently 60,894 variable stars with 544 variability types.

Dataset description

Variable stars are stars whose brightness changes over time, either due to intrinsic physical processes (pulsation, eruption, rotation) or extrinsic geometry (eclipsing binaries). The GCVS is the internationally recognized authority for variable star designations and classifications. It has been compiled and updated for over 75 years, serving as the foundation for stellar variability research.

Each entry includes the GCVS designation, coordinates, variability type, magnitude range, period, epoch of maximum, and spectral type where known.

Schema

Column	Type	Description
gcvs_name	string	GCVS designation (e.g. "R And", "delta Cep")
ra_deg	float64	Right ascension J2000 (degrees)
dec_deg	float64	Declination J2000 (degrees)
variable_type	string	Variability type (e.g. M, SR, EA, DCEP, RR)
magnitude_max	float64	Maximum brightness (mag, lower = brighter)
magnitude_min_flag	string	Flag on minimum magnitude (e.g. "(" for amplitude)
magnitude_min	float64	Minimum brightness (mag)
period_days	float64	Period in days
epoch_jd	float64	Epoch of maximum/minimum (Julian Date)
spectral_type	string	Spectral type

Quick stats

• 60,894 variable stars
• 544 variability types
• 40,096 with known period
• 15,116 with spectral type
• Top types: M (7,920), RRAB (7,590), EA (4,113), EW (3,616), LB (2,800)

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/gcvs-variable-stars", split="train")
df = ds.to_pandas()

# Cepheid variables (standard candles for distance measurement)
cepheids = df[df["variable_type"].str.startswith("DCEP", na=False)]
print(f"{len(cepheids):,} classical Cepheids")

# Eclipsing binaries
eclipsing = df[df["variable_type"].str.startswith("E", na=False)]
print(f"{len(eclipsing):,} eclipsing binaries")

# Period-luminosity distribution
import matplotlib.pyplot as plt
valid = df.dropna(subset=["period_days", "magnitude_max"])
valid = valid[valid["period_days"] > 0]
plt.scatter(valid["period_days"], valid["magnitude_max"], s=0.5, alpha=0.3)
plt.xscale("log")
plt.gca().invert_yaxis()
plt.xlabel("Period (days)")
plt.ylabel("Magnitude (max brightness)")
plt.title("GCVS Period vs Magnitude")

Data source

General Catalogue of Variable Stars (Samus' N.N., Kazarovets E.V., Durlevich O.V., Kireeva N.N., Pastukhova E.N., 2017, Astronomy Reports, 61, 80), accessed via VizieR, CDS Strasbourg.

Update schedule

Quarterly (1st of the month at 08:00 UTC) via GitHub Actions.

Related datasets

• pulsar-catalog -- ATNF Pulsar Catalogue
• messier-catalog -- Messier deep-sky objects
• ngc-ic-catalog -- NGC/IC deep-sky catalog

Pipeline

Source code: juliensimon/space-datasets

Citation

@dataset{gcvs_variable_stars,
  author = {Simon, Julien},
  title = {General Catalogue of Variable Stars (GCVS)},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/gcvs-variable-stars},
  note = {Based on GCVS (Samus' et al. 2017) via VizieR CDS Strasbourg}
}

License

CC-BY-4.0