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.

翻译：当前及未来天文台产生的海量数据推动了自动化机器学习方法在天文学中的开发与应用。然而，在天文学与天体物理学领域，用于评估不同机器学习算法性能的标准化数据集构建工作尚未得到足够重视。本文详细介绍了MiraBest数据集——一个从NVSS和FIRST巡天中筛选出的1256个射电噪活动星系核（AGN）的公开批次数据集，其红移范围限定为$0.03 < z < 0.1$，并由Miraghaei和Best（2017）依据Fanaroff-Riley形态分类进行人工标注。该数据集专为机器学习应用构建，可与标准深度学习库兼容使用。我们阐述了数据集构建的基本原则、样本选取与预处理方法、数据结构与组成，并比较了MiraBest与文献中其他数据集的差异。本文还回顾了现有利用MiraBest数据集的应用研究，并通过将MiraBest与文献中更广泛用于机器学习应用的射电噪AGN其他星表进行交叉匹配，生成了一个包含2100个源的扩展数据集。