Training and test data for: Not getting in too deep: A practical deep learning approach to routine crystallisation image classification

These data were used to classify crystallisation experiments in Milne et al., (https://doi.org/10.1371/journal.pone.0282562). Here, four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources were compared. It was shown that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative (Bruno et al. PLOS one, 13(6), 2018). Eight classes were used to rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions.

External deposit with Dryad