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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
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                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
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                      leeway=leeway,
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                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
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                      jwt,
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                      detached_payload=detached_payload,
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                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
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                      signing_input,
                      ^^^^^^^^^^^^^^
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                      options=merged_options,
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                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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General Catalogue of Variable Stars (GCVS)

The Crab Nebula, a supernova remnant

Credit: NASA/ESA/Hubble

Part of a dataset collection on Hugging Face.

Dataset description

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.

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.

The catalog spans an extraordinary range of stellar physics. Mira variables (type M) are asymptotic giant branch stars with periods of hundreds of days and visual amplitudes exceeding 2.5 magnitudes, driven by radial pulsations in their extended hydrogen envelopes. Semi-regular variables (SR) occupy a similar evolutionary stage but pulsate with smaller amplitudes and less predictable cycles. Eclipsing binaries (EA, EB, EW) are not intrinsically variable at all -- their brightness changes arise purely from orbital geometry as one star transits the disk of its companion. At the other extreme, eruptive variables like UV Ceti flare stars and FU Orionis objects undergo sudden, dramatic outbursts linked to magnetic reconnection events or disk accretion instabilities.

Among the most scientifically important classes are the pulsating variables used as standard candles: classical Cepheids (DCEP), whose period-luminosity relation underpins the extragalactic distance ladder, and RR Lyrae stars (RR), horizontal-branch pulsators that trace the old stellar populations of the Galactic halo and globular clusters.

Because the GCVS draws on over a century of photometric monitoring, it captures variability on timescales inaccessible to modern surveys that have operated for only a few years. Many entries include epochs of maximum light stretching back to the early twentieth century, enabling studies of period changes, evolutionary effects, and long-term amplitude modulation that would be impossible from any single contemporary survey alone.

This dataset is suitable for tabular classification tasks.

Schema

Column Type Description Sample Null %
gcvs_name str GCVS official designation β€” Greek letter + constellation for bright variables (e.g. 'R And', 'delta Cep'), or V+number for fainter ones (e.g. 'V1500 Cyg'); IAU-recognized identifier AA And 0.0%
ra_deg float64 Right ascension, ICRS J2000.0, in decimal degrees (0-360) 346.3451666666666 0.3%
dec_deg float64 Declination, ICRS J2000.0, in decimal degrees (-90 to +90) 47.67630555555555 0.3%
variable_type str GCVS variability type code; common values: DCEP (classical Cepheid, period-luminosity standard candle), RR (RR Lyrae, old metal-poor horizontal-branch pulsator), M (Mira, AGB long-period pulsator), SR (semi-regular), EA/EB/EW (eclipsing binaries), UV (UV Ceti/flare star), N (nova), SN (supernova); ~100 distinct types EB 1.4%
magnitude_max float64 Brightness at maximum light in V-band (mag); lower value = brighter star; null for a small number of entries 10.3 0.4%
magnitude_min_flag str Qualifier on magnitude_min: '(' means value is amplitude (mag range) rather than absolute minimum magnitude; blank otherwise < 83.4%
magnitude_min float64 Brightness at minimum light in V-band (mag); for eclipsing binaries this is the primary minimum; amplitude = magnitude_min - magnitude_max; null for irregular variables with poorly defined minima 11.2 0.8%
period_days float64 Pulsation or orbital period in days; null for irregular variables (type L, I), single-event novae, and eruptive stars with no recurring period 0.9350969 34.2%
epoch_jd float64 Julian Date of the light curve reference point β€” epoch of maximum light for pulsators, epoch of primary minimum for eclipsing binaries; null when no reliable epoch has been established 52500.73 46.1%
spectral_type str MK spectral type at the phase of maximum light; null for ~40% of entries where spectral classification is unavailable B8V 75.2%

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()
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")
plt.show()

Data source

https://www.sai.msu.su/gcvs/gcvs/

Related datasets

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About the author

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

Citation

@dataset{gcvs_variable_stars,
  title = {General Catalogue of Variable Stars (GCVS)},
  author = {juliensimon},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/gcvs-variable-stars},
  publisher = {Hugging Face}
}

License

CC-BY-4.0

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