{"url":"/dataset/sparkesx","name":"SPARKESX","full_name":"Single-dish PARKES for finding the uneXpected","description_markdown":"We present the Single-dish PARKES data sets for finding the uneXpected (SPARKESX), a compilation of real and simulated high-time resolution observations. SPARKESX comprises three mock surveys from the Parkes ''Murriyang'' radio telescope. A broad selection of simulated and injected expected signals (such as pulsars, fast radio bursts), poorly known signals (such as the features expected from flare stars) and unknown unknowns are generated for each survey. We provide a baseline by presenting how successful a typical pipeline based on the standard pulsar search software, PRESTO, is at finding the injected signals.\r\n\r\nThe dataset is designed to aid in the development of new search algorithms, including image processing, machine learning, and deep learning. The raw data, ground truth labels, and baseline are provided.","description_withheld":null,"homepage":"https://doi.org/10.25919/fd4f-0g20","introduced_date":"2022-09-07","introduced_date_note":null,"introduced_by":{"paper":null,"title":"SPARKESX: Single-dish PARKES data sets for finding the uneXpected -- A data challenge","first_author":null,"url":null},"license":{"name":"Creative Commons Attribution 4.0 International License","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[],"tasks":[],"languages":[],"variants":["SPARKESX"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}