The scale of the COVID-19 pandemic has resulted in the acquisition of huge volumes of imaging data.
\nTraditionally, research using imaging data constituted collation of data within single hospitals or groups
\nof hospitals at most. Endeavours on a local scale have the constraint that not all patient subgroups or
\ndisease manifestations might be captured in the collected data. It has long been recognised that there
\nis an acute need to curate larger, more comprehensive datasets to better understand a disease.
\nCOVID-19 has arrived in an era where advances in computational power, aligned with an increased
\navailability of big data and the development of self-learning neural networks, have begun to redefine
\nresearch in medicine. In recent years, computer algorithms trained on imaging data, widely available
\non the internet, have been adapted to the task of medical image analysis [5, 6]. For computer
\nalgorithms to be successfully applied to medical image analysis, it is imperative that they train on large
\nvolumes and representative examples of imaging data. These are typically orders of magnitude larger
\nthan traditional imaging research datasets, and beyond the capacity of traditional research
\ne-infrastructure<\/p>\n