Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern.
Acta Crystallogr A Found Adv 2022;
78:386-394. [PMID:
36047395 PMCID:
PMC9434602 DOI:
10.1107/s2053273322007483]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/21/2022] [Indexed: 02/05/2023] Open
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.
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