juliensimon/kepler-observations
Kepler Observation Catalog Credit: NASA/Ames/JPL-Caltech Part of a dataset collection on Hugging Face. Dataset description The Kepler Observation Catalog indexes every target observed by NASA's Kepler space telescope during its prime mission (2009–2013), drawn from the Mikulski Archive for Space Telescopes (MAST). Kepler is the most successful exoplanet-hunting mission in history: by continuously monitoring ~200,000 stars in a single 100-square-degree… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/kepler-observations.
Kepler Observation Catalog
<div align="center"> <img src="banner.jpg" alt="Artist concept of NASA's Kepler space telescope in orbit, surrounded by a starfield" width="400"> <p><em>Credit: NASA/Ames/JPL-Caltech</em></p> </div>
Part of a [dataset collection](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743) on Hugging Face.
Dataset description
The Kepler Observation Catalog indexes every target observed by NASA's Kepler space telescope during its prime mission (2009–2013), drawn from the Mikulski Archive for Space Telescopes (MAST). Kepler is the most successful exoplanet-hunting mission in history: by continuously monitoring ~200,000 stars in a single 100-square-degree field of view in Cygnus–Lyra, it discovered the majority of confirmed exoplanets through high-precision photometric transit detection, including the first Earth-sized planets in habitable zones.
Each row in this catalog is one Kepler target — identified by its 9-digit Kepler Input Catalog (KIC) ID — with the cadence at which it was observed (long cadence = 29.4-minute integration, capable of catching transits on weeks-to-months orbital periods; short cadence = 58.9-second integration, used for asteroseismology and short-period transits), the pointing (RA/Dec), and a 17-character bitmask indicating which of Kepler's 17 quarterly observing periods contain data for that target. The quarters_observed column summarises the mask as an integer count — a target observed in all 17 quarters has the longest, most exoplanet-favourable light curve in the archive.
This dataset is designed for cross-matching with other exoplanet catalogs (Kepler confirmed planets, TESS TOI, Gaia DR3), for selecting targets with long baselines for long-period planet searches, and for understanding the Kepler field's completeness. It complements the Kepler eclipsing binary and transit timing variation catalogs already in this collection by providing the full target list. Each target's raw and de-trended light curves can be retrieved from MAST using the obs_id.
The catalog is derived from MAST's CAOM table dbo.caomobservation (collection = 'KEPLER'). The K2 extended mission (2014–2018) uses a different observation-id schema and is published as a separate dataset (planned). The Kepler prime-mission archive is static, so this dataset is refreshed quarterly to pick up any late-stage reprocessing.
This dataset is suitable for tabular classification tasks.
Schema
Quick stats
- 212,993 Kepler prime-mission observations (2009–2013)
- 207,656 long cadence (29.4 min), 3,724 short cadence (58.9 s)
- 82,915 targets observed in all 17 Kepler quarters (maximum baseline)
- 207,656 distinct Kepler Input Catalog (KIC) targets
Usage
from datasets import load_dataset
ds = load_dataset("juliensimon/kepler-observations", split="train")
df = ds.to_pandas()from datasets import load_dataset
ds = load_dataset("juliensimon/kepler-observations", split="train")
df = ds.to_pandas()
# Targets with longest baseline (all 17 quarters)
full_baseline = df[(df["quarters_observed"] == 17) & (df["cadence"] == "lc")]
print(f"Targets observed across the full Kepler prime mission: {len(full_baseline):,}")
# Map of Kepler field
import matplotlib.pyplot as plt
sample = df.sample(min(50000, len(df)))
plt.figure(figsize=(10, 8))
plt.scatter(sample["target_ra"], sample["target_dec"], s=0.2, alpha=0.3)
plt.xlabel("RA (deg)"); plt.ylabel("Dec (deg)")
plt.title("Kepler prime-mission field of view (50K sample)")
plt.gca().invert_xaxis()
plt.show()
# Cadence distribution per quarter
import numpy as np
mask_chars = np.array([list(m) for m in df["quarters_mask"].fillna("0" * 17)])
per_quarter = (mask_chars == "1").sum(axis=0)
plt.bar(range(1, 18), per_quarter)
plt.xlabel("Kepler quarter"); plt.ylabel("Targets observed")
plt.title("Kepler target count per quarter")
plt.show()Data source
https://archive.stsci.edu/missions-and-data/kepler
Update schedule
Quarterly (1st of Jan/Apr/Jul/Oct at 14:00 UTC) via GitHub Actions.
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{kepler_observations,
title = {Kepler Observation Catalog},
author = {Simon, Julien},
year = {2026},
url = {https://huggingface.co/datasets/juliensimon/kepler-observations},
publisher = {Hugging Face},
note = {Derived from MAST, Space Telescope Science Institute, https://archive.stsci.edu/missions-and-data/kepler}
}