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Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern
Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review
DOI
- https://doi.org/10.1107/S2053273322007483
Forlagets udgivne version
- Berrak Ozer, Columbia University ,
- Martin A. Karlsen, Syddansk Universitet ,
- Zachary Thatcher, Columbia University ,
- Ling Lan, Columbia University ,
- Brian McMahon, International Union of Crystallography ,
- Peter R. Strickland, International Union of Crystallography ,
- Simon P. Westrip, International Union of Crystallography ,
- Koh S. Sang, International Union of Crystallography ,
- David G. Billing, University of the Witwatersrand ,
- Dorthe B. Ravnsbaek
- Simon J. L. Billinge, Columbia University, Brookhaven National Laboratory
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.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Acta Crystallographica Section A: Foundations and Advances |
| Vol/bind | A78 |
| Nummer | Part 5 |
| Sider (fra-til) | 386-394 |
| Antal sider | 9 |
| ISSN | 2053-2733 |
| DOI | |
| Status | Udgivet - sep. 2022 |
Bibliografisk note
Publisher Copyright:
© 2022 International Union of Crystallography. All rights reserved.
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