  Machine learning based methods for diagnosis and progression prediction of
COVID-19 from imaging data have gained significant attention in the last
months, in particular by the use of deep learning models. In this context
hundreds of models where proposed with the majority of them trained on public
datasets. Data scarcity, mismatch between training and target population, group
imbalance, and lack of documentation are important sources of bias, hindering
the applicability of these models to real-world clinical practice. Considering
that datasets are an essential part of model building and evaluation, a deeper
understanding of the current landscape is needed. This paper presents an
overview of the currently public available COVID-19 chest X-ray datasets. Each
dataset is briefly described and potential strength, limitations and
interactions between datasets are identified. In particular, some key
properties of current datasets that could be potential sources of bias,
impairing models trained on them are pointed out. These descriptions are useful
for model building on those datasets, to choose the best dataset according the
model goal, to take into account the specific limitations to avoid reporting
overconfident benchmark results, and to discuss their impact on the
generalisation capabilities in a specific clinical setting
