Towards a machine‐readable literature: finding relevant papers based on an uploaded powder diffraction pattern

A prototype application for machine‐readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data‐driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top‐ranked papers together with a stack plot of the user data alongside the top‐five database entries. The paper describes the approach and explores successes and challenges. A prototype application, pyDataRecognition, is described and tested. It has the goal that, given a measured powder diffraction pattern, it will return a list of publications from the IUCr Journals database that might be related based on the similarity to powder diffraction data deposited for those publications. This explores the possibility of a machine‐readable literature where, for example, relevant studies may be found automatically through data similarity matches of online databases.