1. Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern.
- Author
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Özer B, Karlsen MA, Thatcher Z, Lan L, McMahon B, Strickland PR, Westrip SP, Sang KS, Billing DG, Ravnsbæk DB, and Billinge SJL
- Subjects
- Databases, Factual, Powder Diffraction, Powders, Publications
- Abstract
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., (open access.)
- Published
- 2022
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