{"url":"/dataset/msl","name":"MSL","full_name":"Mars Science Laboratory","description_markdown":"This dataset contains expert-labeled telemetry anomaly data from the Mars Science Laboratory (MSL) rover, Curiosity.\r\n\r\nReal spacecraft and curiosity rover anomalies for anomaly detection\r\nIndications of telemetry anomalies can be found within previously mentioned ISA reports. All telemetry channels discussed in an individual ISA were reviewed to ensure that the anomaly was evident in the associated telemetry data, and specific anomalous time ranges were manually labeled for each channel. If multiple anomalous sequences and channels closely resembled each other, only one was kept for the experiment in order to create a diverse and balanced set. Anomalies were classified into two categories, point and contextual, to distinguish between anomalies that would likely be identified by properly set alarms or distance-based methods that ignore temporal information (point anomalies) and those that require more complex methodologies such as LSTMs or Hierarchical Temporal Memory (HTM) approaches to detect (contextual anomalies)\r\n\r\nMSL:\r\nTM Channels (27)\r\nTotal TM values (66,709)\r\nTotal anomalies (36)","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/patrickfleith/nasa-anomaly-detection-dataset-smap-msl","introduced_date":"2018-02-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/detecting-spacecraft-anomalies-using-lstms","title":"Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding","first_author":"Kyle Hundman","url":null},"license":null,"modalities":[],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Time Series","url":"/task/time-series-1","datasets_with_task":"/datasets/task/time-series-1"},{"name":"Time Series Anomaly Detection","url":"/task/time-series-anomaly-detection","datasets_with_task":"/datasets/task/time-series-anomaly-detection"}],"languages":[],"variants":["MSL"],"data_loaders":[{"repo":"https://github.com/zamanzadeh/CARLA","url":"https://github.com/zamanzadeh/CARLA","frameworks":["pytorch"]}],"num_papers_in_archive":130,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-anomaly-detection-on-msl","task":"Time Series Anomaly Detection","dataset_variant":"MSL","rows":1,"metrics":["AUPR","F1 Score","Recall","precision"],"first_row_in_archive_order":{"model":"CARLA","paper":"/paper/carla-a-self-supervised-contrastive","metrics":{"AUPR":"0.501","F1 Score":"52.27","Recall":"0.7959","precision":"0.3891"},"code_links":[{"title":"zamanzadeh/CARLA","url":"https://github.com/zamanzadeh/CARLA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/carla-a-self-supervised-contrastive","title":"CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection","date":"2023-08-18","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}