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New classes of astronomical objects are often discovered serendipitously. Theenormous data volumes produced by recent high-time resolution, radio-telescopesurveys imply that efficient algorithms are required for a discovery. Suchalgorithms are usually tuned to detect specific, known sources. Existing datasets therefore likely contain unknown astronomical sources, which will remainundetected unless algorithms are developed that can detect a more diverse rangeof signals. We present the Single-dish PARKES data challenge for finding theuneXpected (SPARKESX), a compilation of real and simulated high-time resolutionobservations. 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 characterised signals (plausibleflare star signatures) and unknown unknowns are generated for each survey. Thegoal of this challenge is to aid in the development of new algorithms that candetect a wide-range of source types. We show how successful a typical pipelinebased on the standard pulsar search software, PRESTO, is at finding theinjected signals. The dataset is publicly available athttps://doi.org/10.25919/fd4f-0g20.

SPARKESX: Single-dish PARKES data sets for finding the uneXpected -- A data challenge