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

Berrak Ozer*, Martin A. Karlsen, Zachary Thatcher, Ling Lan, Brian McMahon, Peter R. Strickland, Simon P. Westrip, Koh S. Sang, David G. Billing, Dorthe B. Ravnsbaek, Simon J. L. Billinge*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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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.

Original languageEnglish
JournalActa Crystallographica Section A: Foundations and Advances
VolumeA78
IssuePart 5
Pages (from-to)386-394
Number of pages9
ISSN2053-2733
DOIs
Publication statusPublished - Sept 2022

Keywords

  • machine-readable scientific literature
  • data-driven literature search
  • powder diffraction
  • data similarity
  • CIF
  • CRYSTAL-STRUCTURE
  • DATABASE
  • PHASES
  • FILE

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