{"url":"/dataset/smap","name":"SMAP","full_name":"Soil Moisture Active Passive","description_markdown":"Soil Moisture Active Passive (SMAP) dataset is a dataset of soil samples and telemetry information using the Mars rover by NASA. Originally published in https://arxiv.org/abs/1802.04431 and used for the unsupervised anomaly detection task in time series data. Later it was used in many popular anomaly detection methods and benchmarks that distribute it in their repositories e.g., https://github.com/OpsPAI/MTAD","description_withheld":null,"homepage":"https://github.com/OpsPAI/MTAD","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":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"},{"name":"Time Series Anomaly Detection","url":"/task/time-series-anomaly-detection","datasets_with_task":"/datasets/task/time-series-anomaly-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SMAP"],"data_loaders":[],"num_papers_in_archive":127,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-anomaly-detection-on-smap","task":"Unsupervised Anomaly Detection","dataset_variant":"SMAP","rows":9,"metrics":["F1","Precision","Recall","AUC"],"first_row_in_archive_order":{"model":"DFM (flow matching)","paper":"/paper/dfm-interpolant-free-dual-flow-matching","metrics":{"AUC":"98.6","F1":"94.1","Precision":"89.7","Recall":"98.9"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-anomaly-detection-on-smap","task":"Time Series Anomaly Detection","dataset_variant":"SMAP","rows":1,"metrics":["AUPR","F1 Score","Recall","precision"],"first_row_in_archive_order":{"model":"CARLA","paper":"/paper/carla-a-self-supervised-contrastive","metrics":{"AUPR":"0.448","F1 Score":"0.5292","Recall":"0.804","precision":"0.3944"},"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/dfm-interpolant-free-dual-flow-matching","title":"DFM: Interpolant-free Dual Flow Matching","date":"2024-10-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/contextflow-generalist-specialist-flow-based","title":"ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding","date":"2024-06-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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},{"paper":"/paper/tranad-deep-transformer-networks-for-anomaly","title":"TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data","date":"2022-01-18","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-deep-anomaly-detection-for-multi","title":"Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals","date":"2021-07-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/graph-neural-network-based-anomaly-detection","title":"Graph Neural Network-Based Anomaly Detection in Multivariate Time Series","date":"2021-06-13","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":3,"samples_unverified":3,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multivariate-time-series-anomaly-detection","title":"Multivariate Time-series Anomaly Detection via Graph Attention Network","date":"2020-09-04","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/usad-unsupervised-anomaly-detection-on","title":"USAD: UnSupervised Anomaly Detection on Multivariate Time Series","date":"2020-08-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/robust-anomaly-detection-for-multivariate","title":"Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network","date":"2019-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/glow-generative-flow-with-invertible-1x1","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","date":"2018-07-09","rows_on_this_dataset":1,"code_links":27,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":129,"samples_ran":75,"samples_unverified":54,"pointer_only_for_licence":44,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":150,"samples_ran":85,"samples_unverified":65,"pointer_only_for_licence":50,"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."}