This work reveals how naively using publicly available data for training and evaluating reconstruction algorithms may lead to artificially improved algorithm performance. We observed such practice in the “wild” and aim to bring this to the attention of the community. The underlying cause is common data preprocessing pipelines which are often ignored: k-space zero-padding in clinical scanners and JPEG compression in database storage. We show that retrospective subsampling of such preprocessed data leads to overly-optimistic reconstructions. We demonstrate this phenomenon for Compressed-Sensing, Dictionary-Learning and Deep Neural Networks. This work hence highlights the importance of careful task-adequate usage of public databases.