  The volume of data from current and future observatories has motivated the
increased development and application of automated machine learning
methodologies for astronomy. However, less attention has been given to the
production of standardised datasets for assessing the performance of different
machine learning algorithms within astronomy and astrophysics. Here we describe
in detail the MiraBest dataset, a publicly available batched dataset of 1256
radio-loud AGN from NVSS and FIRST, filtered to $0.03 < z < 0.1$, manually
labelled by Miraghaei and Best (2017) according to the Fanaroff-Riley
morphological classification, created for machine learning applications and
compatible for use with standard deep learning libraries. We outline the
principles underlying the construction of the dataset, the sample selection and
pre-processing methodology, dataset structure and composition, as well as a
comparison of MiraBest to other datasets used in the literature. Existing
applications that utilise the MiraBest dataset are reviewed, and an extended
dataset of 2100 sources is created by cross-matching MiraBest with other
catalogues of radio-loud AGN that have been used more widely in the literature
for machine learning applications.
