  New classes of astronomical objects are often discovered serendipitously. The
enormous data volumes produced by recent high-time resolution, radio-telescope
surveys imply that efficient algorithms are required for a discovery. Such
algorithms are usually tuned to detect specific, known sources. Existing data
sets therefore likely contain unknown astronomical sources, which will remain
undetected unless algorithms are developed that can detect a more diverse range
of signals. We present the Single-dish PARKES data challenge 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 characterised signals (plausible
flare star signatures) and unknown unknowns are generated for each survey. The
goal of this challenge is to aid in the development of new algorithms that can
detect a wide-range of source types. We show how successful a typical pipeline
based on the standard pulsar search software, PRESTO, is at finding the
injected signals. The dataset is publicly available at
https://doi.org/10.25919/fd4f-0g20.
