Axions are a promising dark matter candidate, yet their feeble interactions with visible matter pose a considerable challenge in detecting them. The ABRACADABRA-10cm experiment was meticulously built to generate long time series data within which axion signals could hide. Currently, the axion analysis of this dataset is only conducted in the frequency domain, omitting the valuable phase information embedded in the raw time series. In this public data release, we present a labeled dataset comprised of time series data collected from the ABRACADABRA detector, complete with axion-mimicking hardware signal injections. This dataset paper sets the stage for critical challenges faced in ABRACADABRA data analysis, including peak finding, denoising, and time series reconstructions. The success of machine learning algorithms in tackling these challenges will boost the experimental sensitivity to the enigmatic axion.