Hugging Face's logo

Datasets:
appleparan
/
telemanom

Tasks:
Other
Modalities:
Tabular
Time-series
Formats:
parquet
Size:
100K - 1M
ArXiv:
Tags:
timeseries
anomaly-detection
spacecraft
telemetry
NASA
SMAP
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
telemanom / README.md
Jongsu Liam Kim
feat: add per-channel Parquet files for Hugging Face dataset viewer
2d22e10
preview code
|
raw
history blame contribute delete
17.3 kB
metadata 
license: bsd-3-clause
task_categories:
  - other
tags:
  - timeseries
  - anomaly-detection
  - spacecraft
  - telemetry
  - NASA
  - SMAP
  - MSL
pretty_name: NASA SMAP and MSL Spacecraft Anomaly Detection Dataset
size_categories:
  - 100K<n<1M
configs:
  - config_name: A-1
    data_files:
      - split: train
        path: data/train/A-1.parquet
      - split: test
        path: data/test/A-1.parquet
  - config_name: A-2
    data_files:
      - split: train
        path: data/train/A-2.parquet
      - split: test
        path: data/test/A-2.parquet
  - config_name: A-3
    data_files:
      - split: train
        path: data/train/A-3.parquet
      - split: test
        path: data/test/A-3.parquet
  - config_name: A-4
    data_files:
      - split: train
        path: data/train/A-4.parquet
      - split: test
        path: data/test/A-4.parquet
  - config_name: A-5
    data_files:
      - split: train
        path: data/train/A-5.parquet
      - split: test
        path: data/test/A-5.parquet
  - config_name: A-6
    data_files:
      - split: train
        path: data/train/A-6.parquet
      - split: test
        path: data/test/A-6.parquet
  - config_name: A-7
    data_files:
      - split: train
        path: data/train/A-7.parquet
      - split: test
        path: data/test/A-7.parquet
  - config_name: A-8
    data_files:
      - split: train
        path: data/train/A-8.parquet
      - split: test
        path: data/test/A-8.parquet
  - config_name: A-9
    data_files:
      - split: train
        path: data/train/A-9.parquet
      - split: test
        path: data/test/A-9.parquet
  - config_name: B-1
    data_files:
      - split: train
        path: data/train/B-1.parquet
      - split: test
        path: data/test/B-1.parquet
  - config_name: C-1
    data_files:
      - split: train
        path: data/train/C-1.parquet
      - split: test
        path: data/test/C-1.parquet
  - config_name: C-2
    data_files:
      - split: train
        path: data/train/C-2.parquet
      - split: test
        path: data/test/C-2.parquet
  - config_name: D-1
    data_files:
      - split: train
        path: data/train/D-1.parquet
      - split: test
        path: data/test/D-1.parquet
  - config_name: D-11
    data_files:
      - split: train
        path: data/train/D-11.parquet
      - split: test
        path: data/test/D-11.parquet
  - config_name: D-12
    data_files:
      - split: train
        path: data/train/D-12.parquet
      - split: test
        path: data/test/D-12.parquet
  - config_name: D-13
    data_files:
      - split: train
        path: data/train/D-13.parquet
      - split: test
        path: data/test/D-13.parquet
  - config_name: D-14
    data_files:
      - split: train
        path: data/train/D-14.parquet
      - split: test
        path: data/test/D-14.parquet
  - config_name: D-15
    data_files:
      - split: train
        path: data/train/D-15.parquet
      - split: test
        path: data/test/D-15.parquet
  - config_name: D-16
    data_files:
      - split: train
        path: data/train/D-16.parquet
      - split: test
        path: data/test/D-16.parquet
  - config_name: D-2
    data_files:
      - split: train
        path: data/train/D-2.parquet
      - split: test
        path: data/test/D-2.parquet
  - config_name: D-3
    data_files:
      - split: train
        path: data/train/D-3.parquet
      - split: test
        path: data/test/D-3.parquet
  - config_name: D-4
    data_files:
      - split: train
        path: data/train/D-4.parquet
      - split: test
        path: data/test/D-4.parquet
  - config_name: D-5
    data_files:
      - split: train
        path: data/train/D-5.parquet
      - split: test
        path: data/test/D-5.parquet
  - config_name: D-6
    data_files:
      - split: train
        path: data/train/D-6.parquet
      - split: test
        path: data/test/D-6.parquet
  - config_name: D-7
    data_files:
      - split: train
        path: data/train/D-7.parquet
      - split: test
        path: data/test/D-7.parquet
  - config_name: D-8
    data_files:
      - split: train
        path: data/train/D-8.parquet
      - split: test
        path: data/test/D-8.parquet
  - config_name: D-9
    data_files:
      - split: train
        path: data/train/D-9.parquet
      - split: test
        path: data/test/D-9.parquet
  - config_name: E-1
    data_files:
      - split: train
        path: data/train/E-1.parquet
      - split: test
        path: data/test/E-1.parquet
  - config_name: E-10
    data_files:
      - split: train
        path: data/train/E-10.parquet
      - split: test
        path: data/test/E-10.parquet
  - config_name: E-11
    data_files:
      - split: train
        path: data/train/E-11.parquet
      - split: test
        path: data/test/E-11.parquet
  - config_name: E-12
    data_files:
      - split: train
        path: data/train/E-12.parquet
      - split: test
        path: data/test/E-12.parquet
  - config_name: E-13
    data_files:
      - split: train
        path: data/train/E-13.parquet
      - split: test
        path: data/test/E-13.parquet
  - config_name: E-2
    data_files:
      - split: train
        path: data/train/E-2.parquet
      - split: test
        path: data/test/E-2.parquet
  - config_name: E-3
    data_files:
      - split: train
        path: data/train/E-3.parquet
      - split: test
        path: data/test/E-3.parquet
  - config_name: E-4
    data_files:
      - split: train
        path: data/train/E-4.parquet
      - split: test
        path: data/test/E-4.parquet
  - config_name: E-5
    data_files:
      - split: train
        path: data/train/E-5.parquet
      - split: test
        path: data/test/E-5.parquet
  - config_name: E-6
    data_files:
      - split: train
        path: data/train/E-6.parquet
      - split: test
        path: data/test/E-6.parquet
  - config_name: E-7
    data_files:
      - split: train
        path: data/train/E-7.parquet
      - split: test
        path: data/test/E-7.parquet
  - config_name: E-8
    data_files:
      - split: train
        path: data/train/E-8.parquet
      - split: test
        path: data/test/E-8.parquet
  - config_name: E-9
    data_files:
      - split: train
        path: data/train/E-9.parquet
      - split: test
        path: data/test/E-9.parquet
  - config_name: F-1
    data_files:
      - split: train
        path: data/train/F-1.parquet
      - split: test
        path: data/test/F-1.parquet
  - config_name: F-2
    data_files:
      - split: train
        path: data/train/F-2.parquet
      - split: test
        path: data/test/F-2.parquet
  - config_name: F-3
    data_files:
      - split: train
        path: data/train/F-3.parquet
      - split: test
        path: data/test/F-3.parquet
  - config_name: F-4
    data_files:
      - split: train
        path: data/train/F-4.parquet
      - split: test
        path: data/test/F-4.parquet
  - config_name: F-5
    data_files:
      - split: train
        path: data/train/F-5.parquet
      - split: test
        path: data/test/F-5.parquet
  - config_name: F-7
    data_files:
      - split: train
        path: data/train/F-7.parquet
      - split: test
        path: data/test/F-7.parquet
  - config_name: F-8
    data_files:
      - split: train
        path: data/train/F-8.parquet
      - split: test
        path: data/test/F-8.parquet
  - config_name: G-1
    data_files:
      - split: train
        path: data/train/G-1.parquet
      - split: test
        path: data/test/G-1.parquet
  - config_name: G-2
    data_files:
      - split: train
        path: data/train/G-2.parquet
      - split: test
        path: data/test/G-2.parquet
  - config_name: G-3
    data_files:
      - split: train
        path: data/train/G-3.parquet
      - split: test
        path: data/test/G-3.parquet
  - config_name: G-4
    data_files:
      - split: train
        path: data/train/G-4.parquet
      - split: test
        path: data/test/G-4.parquet
  - config_name: G-6
    data_files:
      - split: train
        path: data/train/G-6.parquet
      - split: test
        path: data/test/G-6.parquet
  - config_name: G-7
    data_files:
      - split: train
        path: data/train/G-7.parquet
      - split: test
        path: data/test/G-7.parquet
  - config_name: M-1
    data_files:
      - split: train
        path: data/train/M-1.parquet
      - split: test
        path: data/test/M-1.parquet
  - config_name: M-2
    data_files:
      - split: train
        path: data/train/M-2.parquet
      - split: test
        path: data/test/M-2.parquet
  - config_name: M-3
    data_files:
      - split: train
        path: data/train/M-3.parquet
      - split: test
        path: data/test/M-3.parquet
  - config_name: M-4
    data_files:
      - split: train
        path: data/train/M-4.parquet
      - split: test
        path: data/test/M-4.parquet
  - config_name: M-5
    data_files:
      - split: train
        path: data/train/M-5.parquet
      - split: test
        path: data/test/M-5.parquet
  - config_name: M-6
    data_files:
      - split: train
        path: data/train/M-6.parquet
      - split: test
        path: data/test/M-6.parquet
  - config_name: M-7
    data_files:
      - split: train
        path: data/train/M-7.parquet
      - split: test
        path: data/test/M-7.parquet
  - config_name: P-1
    data_files:
      - split: train
        path: data/train/P-1.parquet
      - split: test
        path: data/test/P-1.parquet
  - config_name: P-10
    data_files:
      - split: train
        path: data/train/P-10.parquet
      - split: test
        path: data/test/P-10.parquet
  - config_name: P-11
    data_files:
      - split: train
        path: data/train/P-11.parquet
      - split: test
        path: data/test/P-11.parquet
  - config_name: P-14
    data_files:
      - split: train
        path: data/train/P-14.parquet
      - split: test
        path: data/test/P-14.parquet
  - config_name: P-15
    data_files:
      - split: train
        path: data/train/P-15.parquet
      - split: test
        path: data/test/P-15.parquet
  - config_name: P-2
    data_files:
      - split: train
        path: data/train/P-2.parquet
      - split: test
        path: data/test/P-2.parquet
  - config_name: P-3
    data_files:
      - split: train
        path: data/train/P-3.parquet
      - split: test
        path: data/test/P-3.parquet
  - config_name: P-4
    data_files:
      - split: train
        path: data/train/P-4.parquet
      - split: test
        path: data/test/P-4.parquet
  - config_name: P-7
    data_files:
      - split: train
        path: data/train/P-7.parquet
      - split: test
        path: data/test/P-7.parquet
  - config_name: R-1
    data_files:
      - split: train
        path: data/train/R-1.parquet
      - split: test
        path: data/test/R-1.parquet
  - config_name: S-1
    data_files:
      - split: train
        path: data/train/S-1.parquet
      - split: test
        path: data/test/S-1.parquet
  - config_name: S-2
    data_files:
      - split: train
        path: data/train/S-2.parquet
      - split: test
        path: data/test/S-2.parquet
  - config_name: T-1
    data_files:
      - split: train
        path: data/train/T-1.parquet
      - split: test
        path: data/test/T-1.parquet
  - config_name: T-10
    data_files:
      - split: train
        path: data/train/T-10.parquet
      - split: test
        path: data/test/T-10.parquet
  - config_name: T-12
    data_files:
      - split: train
        path: data/train/T-12.parquet
      - split: test
        path: data/test/T-12.parquet
  - config_name: T-13
    data_files:
      - split: train
        path: data/train/T-13.parquet
      - split: test
        path: data/test/T-13.parquet
  - config_name: T-2
    data_files:
      - split: train
        path: data/train/T-2.parquet
      - split: test
        path: data/test/T-2.parquet
  - config_name: T-3
    data_files:
      - split: train
        path: data/train/T-3.parquet
      - split: test
        path: data/test/T-3.parquet
  - config_name: T-4
    data_files:
      - split: train
        path: data/train/T-4.parquet
      - split: test
        path: data/test/T-4.parquet
  - config_name: T-5
    data_files:
      - split: train
        path: data/train/T-5.parquet
      - split: test
        path: data/test/T-5.parquet
  - config_name: T-8
    data_files:
      - split: train
        path: data/train/T-8.parquet
      - split: test
        path: data/test/T-8.parquet
  - config_name: T-9
    data_files:
      - split: train
        path: data/train/T-9.parquet
      - split: test
        path: data/test/T-9.parquet
default_config_name: A-1

NASA SMAP and MSL Spacecraft Anomaly Detection Dataset

Dataset Description

This dataset contains real spacecraft telemetry data and labeled anomalies from two NASA missions:

  • SMAP (Soil Moisture Active Passive satellite)
  • MSL (Mars Science Laboratory / Curiosity Rover)

The data was originally used in the 2018 KDD paper Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding and released via the telemanom repository.

Dataset Structure 

data/
  train/       # 82 Parquet files (one per channel)
  test/        # 82 Parquet files (one per channel)
  data/
    train/     # 82 .npy files (original format)
    test/      # 82 .npy files (original format)
labeled_anomalies.csv

Each channel is available as a separate config (e.g., A-1, P-1, M-1) that can be selected in the dataset viewer or loaded programmatically.

Data Files

Each Parquet file contains the following columns:

Column Description
timestep Integer index of the time step
value Target telemetry value being monitored
cmd_0 ... cmd_N One-hot encoded command information (24 or 54 columns depending on channel)
  • All telemetry values are anonymized and normalized to [-1, 1] based on min/max in the test set.
  • Channel IDs are anonymized; the first letter indicates channel type (P = power, R = radiation, etc.).
  • SMAP channels have 24 command columns; MSL channels have 54 command columns.

Anomaly Labels (labeled_anomalies.csv)

Column Description
chan_id Anonymized channel ID
spacecraft SMAP or MSL
anomaly_sequences Start and end indices of anomalies in the test set
class Anomaly type: point or contextual
num_values Number of telemetry values in the test stream

Dataset Statistics

SMAP MSL Total
Total anomaly sequences 69 36 105
Point anomalies (%) 43 (62%) 19 (53%) 62 (59%)
Contextual anomalies (%) 26 (38%) 17 (47%) 43 (41%)
Unique telemetry channels 55 27 82
Telemetry values evaluated 429,735 66,709 496,444

Usage

Using datasets library (recommended) 

from datasets import load_dataset

# Load a specific channel (e.g., P-1)
ds = load_dataset("appleparan/telemanom", name="P-1")
print(ds)
# DatasetDict({
#     train: Dataset({features: ['timestep', 'value', 'cmd_0', ...], num_rows: ...})
#     test:  Dataset({features: ['timestep', 'value', 'cmd_0', ...], num_rows: ...})
# })

# Access train/test splits
train_df = ds["train"].to_pandas()
test_df = ds["test"].to_pandas()

Using numpy (original .npy format) 

import numpy as np
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="appleparan/telemanom",
    filename="data/data/test/P-1.npy",
    repo_type="dataset",
)
data = np.load(path)
print(data.shape)  # (n_timesteps, n_inputs)

Source

  • Original repository: khundman/telemanom
  • Paper: arXiv:1802.04431
  • Contributors: Kyle Hundman, Valentinos Constantinou, Christopher Laporte, Ian Colwell, Tom Soderstrom (NASA JPL)

Citation 

@inproceedings{hundman2018detecting,
  title     = {Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding},
  author    = {Hundman, Kyle and Constantinou, Valentino and Laporte, Christopher and Colwell, Ian and Soderstrom, Tom},
  booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year      = {2018}
}

License

This dataset is distributed under the BSD 3-Clause License (Copyright (c) 2018, California Institute of Technology).

Note: The original repository's README states Apache 2.0, but the actual LICENSE.txt file contains a BSD 3-Clause license from Caltech/JPL. This dataset card follows the license file.