Datasets:

juliensimon
/

kepler-transit-timing

koi float64	transit_number int32	t_obs_bjd float64	o_c float64	o_c_err float64	tdv float64	tdv_err float64	tpv float64	tpv_err float64	outlier int64
1.01	0	55.763337	-0.05	0.085	0.001	0.0029	-0.0057	0.0028	0
1.01	1	58.23395	0.077	0.086	-0.002	0.0027	-0.0077	0.0027	0
1.01	2	60.704563	-0.037	0.086	0.0012	0.0024	-0.0109	0.0026	0
1.01	4	65.64579	-0.271	0.086	0.0014	0.0023	-0.0028	0.0025	4
1.01	5	68.116404	-0.003	0.085	-0.0014	0.0024	-0.0016	0.0025	0
1.01	6	70.587017	0.061	0.085	-0.0027	0.0026	-0.0017	0.0026	0
1.01	7	73.05763	0.175	0.087	0.019	0.0028	-0.0305	0.0027	0
1.01	8	75.528244	0.186	0.085	0.004	0.0031	-0.0031	0.0028	0
1.01	9	77.998857	0.047	0.082	-0.0066	0.0032	0.0052	0.0029	0
1.01	10	80.46947	-0.082	0.085	0.005	0.0031	-0.0124	0.0028	0
1.01	11	82.940084	-0.196	0.086	0.0095	0.0028	-0.0071	0.0027	0
1.01	12	85.410697	0.075	0.085	-0.0062	0.0026	-0.0007	0.0026	0
1.01	13	87.881311	-0.024	0.085	-0.0007	0.0024	-0.002	0.0025	0
1.01	14	90.351924	0.112	0.086	-0.0001	0.0023	-0.0039	0.0025	0
1.01	15	92.822537	0.051	0.086	0.0003	0.0023	-0.0013	0.0025	0
1.01	16	95.293151	-0.011	0.086	0.0042	0.0024	-0.0121	0.0026	0
1.01	17	97.763764	-0.224	0.087	0.0041	0.0027	-0.0113	0.0027	0
1.01	20	105.175604	0.107	0.083	-0.0026	0.0032	0.0015	0.0029	0
1.01	21	107.646218	0.059	0.084	0.0013	0.003	0.0001	0.0028	0
1.01	22	110.116831	0.001	0.085	-0.0025	0.0027	0.0007	0.0027	0
1.01	23	112.587444	-0.046	0.085	0.0063	0.0025	-0.0084	0.0026	0
1.01	25	117.528671	-0.031	0.085	-0.0045	0.0023	0.0017	0.0025	0
1.01	26	119.999284	0.108	0.086	0.0012	0.0023	-0.0033	0.0025	0
1.01	27	122.469898	-0.016	0.085	-0.0071	0.0025	0.0012	0.0026	0
1.01	28	124.940511	-0.003	0.085	0.006	0.0027	-0.0038	0.0027	0
1.01	29	127.411125	0.073	0.085	0.0024	0.003	-0.0004	0.0028	0
1.01	30	129.881738	0.026	0.084	0.0028	0.0032	-0.0042	0.0029	0
1.01	31	132.352351	-0.079	0.084	0.0021	0.0032	-0.0055	0.0029	0
1.01	32	134.822965	-0.197	0.085	0.0067	0.003	-0.0074	0.0028	0
1.01	33	137.293578	0.013	0.085	-0.0037	0.0027	-0.0062	0.0027	0
1.01	34	139.764191	-0.008	0.086	0.0049	0.0025	-0.0101	0.0026	0
1.01	35	142.234805	0.098	0.085	-0.007	0.0023	0.0059	0.0025	0
1.01	36	144.705418	0.174	0.086	-0.0011	0.0023	-0.0022	0.0025	0
1.01	37	147.176032	0.084	0.086	0.0003	0.0023	-0.0005	0.0025	0
1.01	38	149.646645	0	0.085	-0.0005	0.0025	0.0022	0.0026	0
1.01	39	152.117258	0.016	0.085	-0.0012	0.0027	0.0006	0.0027	0
1.01	40	154.587872	-0.126	0.085	-0.002	0.003	-0.0042	0.0028	0
1.01	41	157.058485	-0.002	0.084	-0.001	0.0032	-0.0065	0.0029	0
1.01	42	159.529098	0.309	0.084	0.0058	0.0032	-0.0019	0.0029	4
1.01	43	161.999712	0.077	0.084	0.0009	0.003	-0.0024	0.0028	0
1.01	45	166.940939	0.115	0.085	-0.001	0.0025	-0.0021	0.0026	0
1.01	46	169.411552	-0.01	0.086	-0.0036	0.0024	-0.0024	0.0025	0
1.01	47	171.882165	-0.028	0.085	-0.0014	0.0023	0.0025	0.0025	0
1.01	48	174.352779	0.154	0.086	0.0013	0.0023	-0.0037	0.0025	0
1.01	49	176.823392	0.066	0.085	-0.0003	0.0024	-0.0024	0.0026	0
1.01	50	179.294005	0.153	0.086	-0.0081	0.0027	-0.0151	0.0027	0
1.01	51	181.764619	-0.121	0.085	-0.0014	0.003	-0.0027	0.0028	0
1.01	52	184.235232	0.028	0.084	0.0032	0.0031	0.0016	0.0028	0
1.01	53	186.705846	-0.035	0.087	0.0045	0.0037	-0.006	0.003	0
1.01	56	194.117686	-0.071	0.085	-0.0072	0.0025	0.0047	0.0026	0
1.01	57	196.588299	-0.078	0.086	0.0002	0.0024	-0.0126	0.0026	0
1.01	58	199.058912	-0.193	0.085	0.0003	0.0023	0.0049	0.0025	0
1.01	59	201.529526	-0.146	0.085	0.001	0.0023	0.0012	0.0025	0
1.01	60	204.000139	0.035	0.086	0.004	0.0024	-0.0098	0.0026	0
1.01	61	206.470753	0.132	0.085	0.0046	0.0025	-0.0065	0.0026	0
1.01	62	208.941366	-0.009	0.085	0.0024	0.0028	0.0006	0.0027	0
1.01	63	211.411979	-0.037	0.084	-0.0003	0.0031	0.0009	0.0028	0
1.01	64	213.882593	-60.1	1.7	0	0.026	0	0.00029	39
1.01	65	216.353206	-0.018	0.083	0.0001	0.0031	-0.0018	0.0029	0
1.01	66	218.823819	0.038	0.084	0.0031	0.0029	0.0032	0.0028	0
1.01	67	221.294433	-0.211	0.09	0.0081	0.0033	-0.0096	0.0048	32
1.01	69	226.23566	0.031	0.085	0.0029	0.0023	0.003	0.0025	0
1.01	70	228.706273	-0.05	0.085	0.0018	0.0023	0.0014	0.0025	0
1.01	71	231.176886	-0.019	0.085	-0.0019	0.0023	0.0021	0.0025	0
1.01	72	233.6475	0.024	0.085	0.004	0.0024	-0.0025	0.0026	0
1.01	73	236.118113	0.077	0.085	-0.0031	0.0026	0.002	0.0027	0
1.01	74	238.588726	-0.021	0.084	-0.0069	0.0029	0.0043	0.0028	0
1.01	75	241.05934	-0.008	0.083	-0.0007	0.0031	0.0012	0.0029	0
1.01	76	243.529953	-0.114	0.083	0.0003	0.0032	-0.0022	0.0029	0
1.01	77	246.000567	0.212	0.084	-0.0027	0.0031	-0.0053	0.0029	0
1.01	78	248.47118	0.086	0.085	0.0029	0.0028	0.0004	0.0027	0
1.01	79	250.941793	-0.018	0.086	-0.0018	0.0026	-0.003	0.0026	0
1.01	80	253.412407	-0.062	0.085	-0.0036	0.0024	-0.0007	0.0026	0
1.01	82	258.353633	0.061	0.085	0.0023	0.0023	-0.0008	0.0025	0
1.01	83	260.824247	-0.164	0.085	-0.0039	0.0024	-0.0011	0.0026	0
1.01	84	263.29486	-0.078	0.085	-0.0023	0.0025	-0.0049	0.0026	0
1.01	85	265.765474	0.095	0.085	0.0024	0.0028	0.0032	0.0027	0
1.01	86	268.236087	0.022	0.084	0.0008	0.0031	-0.0009	0.0028	0
1.01	87	270.7067	0.005	0.082	-0.0045	0.0032	0.0016	0.0029	0
1.01	88	273.177314	-0.078	0.083	-0.0015	0.0032	0.0037	0.0029	0
1.01	89	275.647927	-0.1	0.085	-0.0004	0.003	-0.0079	0.0028	0
1.01	90	278.118541	-0.052	0.085	-0.0063	0.0027	-0.0019	0.0027	0
1.01	91	280.589154	0.164	0.085	-0.0018	0.0025	-0.0052	0.0026	0
1.01	93	285.530381	-0.035	0.086	0.0017	0.0023	-0.0076	0.0025	0
1.01	94	288.000994	1.81	0.1	-0.0905	0.0028	-0.1194	0.0029	62
1.01	95	290.471607	-0.143	0.085	-0.0042	0.0025	-0.0038	0.0026	0
1.01	96	292.942221	0.181	0.085	-0.001	0.0027	-0.0076	0.0027	0
1.01	97	295.412834	0.03	0.085	0.0025	0.003	-0.0086	0.0028	0
1.01	98	297.883448	0.033	0.084	0.0008	0.0031	-0.0069	0.0029	0
1.01	99	300.354061	-0.03	0.16	-0.0043	0.0045	0.0008	0.0031	32
1.01	100	302.824674	-0.04	0.084	-0.0057	0.0031	0.0008	0.0028	0
1.01	101	305.295288	0.098	0.086	0.0042	0.0028	-0.0085	0.0027	0
1.01	130	376.943076	0.128	0.085	0.0051	0.003	-0.0092	0.0028	0
1.01	131	379.413689	-0.047	0.084	0.0003	0.0032	-0.0048	0.0029	0
1.01	132	381.884302	0.06	0.083	-0.0086	0.0032	-0.001	0.0029	0
1.01	133	384.354916	0.052	0.084	-0.0016	0.003	-0.0007	0.0028	0
1.01	134	386.825529	0.015	0.085	-0.0002	0.0027	0.0012	0.0027	0
1.01	135	389.296142	0.062	0.085	-0.0002	0.0024	-0.0018	0.0026	0
1.01	136	391.766756	-0.037	0.086	0.003	0.0023	-0.0049	0.0025	0
1.01	137	394.237369	-0.085	0.085	0.0003	0.0023	0.0007	0.0025	0

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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}
}