{"url":"/dataset/pronto","name":"PRONTO","full_name":"PRONTO heterogeneous benchmark dataset","description_markdown":"The PRONTO heterogeneous benchmark dataset is based on an industrial-scale multiphase flow facility. It includes data from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements, an operation log and video recordings. The study collected data from various operational conditions with and without induced faults to generate a multi-rate, multi-modal dataset. The dataset is suitable for developing and validating algorithms for fault detection and diagnosis (FDD) and data fusion.","description_withheld":null,"homepage":"https://zenodo.org/records/1341583","introduced_date":"2019-05-16","introduced_date_note":null,"introduced_by":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":"Fault Detection","url":"/task/fault-detection","datasets_with_task":"/datasets/task/fault-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["PRONTO"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-anomaly-detection-on-pronto","task":"Unsupervised Anomaly Detection","dataset_variant":"PRONTO","rows":1,"metrics":["AUC","Best Delay","Best F1","F1"],"first_row_in_archive_order":{"model":"DyEdgeGAT","paper":"/paper/dynamic-graph-attention-for-anomaly-detection","metrics":{"AUC":"0.8","Best Delay":"61","Best F1":"0.86","F1":"0.83"},"code_links":[{"title":"mengjiezhao/dyedgegat","url":"https://github.com/mengjiezhao/dyedgegat"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dynamic-graph-attention-for-anomaly-detection","title":"DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems","date":"2023-07-07","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."}