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Cinder : keeping crystallographers app‐y
Author(s) -
Rosa Nicholas,
Ristic Marko,
Marshall Bevan,
Newman Janet
Publication year - 2018
Publication title -
acta crystallographica section f
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 37
ISSN - 2053-230X
DOI - 10.1107/s2053230x18008038
Subject(s) - cinder , computer science , bottleneck , software , process (computing) , pleiades , artificial intelligence , machine learning , computer vision , operating system , embedded system , engineering , coal , stars , waste management
The process of producing suitable crystals for X‐ray diffraction analysis most often involves the setting up of hundreds (or thousands) of individual crystallization trials, each of which must be repeatedly examined for crystals or hints of crystallinity. Currently, the only real way to address this bottleneck is to use an automated imager to capture images of the trials. However, the images still need to be assessed for crystals or other outcomes. Ideally, there would exist some rapid and reliable machine‐analysis tool to translate the images into a quantitative result. However, as yet no such tool exists in wide usage, despite this being a well recognized problem. One of the issues in creating robust automatic image‐analysis software is the lack of reliable data for training machine‐learning algorithms. Here, a mobile application, Cinder , has been developed which allows crystallization images to be scored quickly on a smartphone or tablet. The Cinder scores are inserted into the appropriate table in a crystallization database and are immediately available to the user through a more sophisticated web interface, allowing more detailed analyses. A sharp increase in the number of scored images was observed after Cinder was released, which in turn provides more data for training machine‐learning tools.