Datasets:
license: cc-by-4.0
pretty_name: Kepler Transit Timing Catalog
language:
- en
description: >-
Holczer et al. (2016) Kepler transit timing catalog — 295,187 individual
transit times for 2,599 Kepler Objects of Interest (KOIs), with O-C residuals,
durations, and depths.
task_categories:
- tabular-regression
tags:
- space
- exoplanets
- kepler
- transit-timing
- ttv
- astronomy
- open-data
- tabular-data
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: data/kepler_transit_timing.parquet
default: true
Kepler Transit Timing Catalog
Part of the Astronomy Datasets collection on Hugging Face.
Transit timing catalog from Holczer et al. (2016), containing 295,187 individual transit mid-times for 2,599 Kepler Objects of Interest (KOIs). Each record includes the observed mid-transit time, observed-minus-computed (O-C) residual, transit duration, and transit depth with uncertainties.
Dataset description
Transit timing variations (TTVs) occur when gravitational interactions between planets in a multi-planet system cause measurable deviations from a strictly periodic transit schedule. Holczer et al. (2016) performed a uniform analysis of all Kepler long-cadence light curves to extract individual transit times, producing the most comprehensive Kepler TTV catalog. The O-C (observed minus computed) residuals reveal planetary interactions, orbital eccentricities, and the presence of additional non-transiting planets.
Key columns
| Column | Type | Description |
|---|---|---|
koi |
float64 | Kepler Object of Interest number |
transit_number |
Int32 | Sequential transit number for this KOI |
t_obs_bjd |
float64 | Observed mid-transit time (BJD - 2454833) |
t_obs_err |
float64 | Uncertainty on mid-transit time (days) |
o_c |
float64 | Observed minus computed residual (days) |
o_c_err |
float64 | Uncertainty on O-C residual (days) |
duration_hr |
float64 | Transit duration (hours) |
duration_err |
float64 | Uncertainty on transit duration (hours) |
depth_ppm |
float64 | Transit depth (ppm) |
depth_err |
float64 | Uncertainty on transit depth (ppm) |
Quick stats
- 295,187 individual transit times
- 2,599 unique KOIs
- Median O-C residual: 0.0000 days
- Median transit depth: nan ppm
- Median transit duration: nan hours
Usage
from datasets import load_dataset
ds = load_dataset("juliensimon/kepler-transit-timing", split="train")
df = ds.to_pandas()
# TTVs for a specific KOI
koi_137 = df[df["koi"] == 137.01].sort_values("transit_number")
print(f"KOI 137.01: {len(koi_137)} transits")
# Plot O-C diagram
import matplotlib.pyplot as plt
plt.errorbar(koi_137["transit_number"], koi_137["o_c"],
yerr=koi_137["o_c_err"], fmt=".", ms=3)
plt.xlabel("Transit number")
plt.ylabel("O-C (days)")
plt.title("KOI 137.01 Transit Timing Variations")
plt.show()
# KOIs with the strongest TTVs (largest O-C scatter)
ttv_rms = df.groupby("koi")["o_c"].std().sort_values(ascending=False)
print("Top 10 TTV candidates:")
print(ttv_rms.head(10))
Data source
Holczer, T. et al. (2016), "Transit Timing Observations from Kepler. IX. Catalog of Transit Timing Measurements of the Long-Cadence Data", ApJS, 225, 9. Accessed via VizieR, CDS Strasbourg (J/ApJS/225/9).
Pipeline
Source code: juliensimon/space-datasets
Citation
@dataset{kepler_transit_timing,
author = {Simon, Julien},
title = {Kepler Transit Timing Catalog},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/kepler-transit-timing},
note = {Based on Holczer et al. (2016) ApJS 225, 9, via VizieR CDS}
}