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Towards a machine‐readable literature: finding relevant papers based on an uploaded powder diffraction pattern.
- Source :
-
Acta Crystallographica. Section A, Foundations & Advances . Sep2022, Vol. 78 Issue 5, p386-394. 9p. - Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DIFFRACTION patterns
*POWDERS
*DIGITAL Object Identifiers
*SCIENTIFIC literature
Subjects
Details
- Language :
- English
- ISSN :
- 20532733
- Volume :
- 78
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Acta Crystallographica. Section A, Foundations & Advances
- Publication Type :
- Academic Journal
- Accession number :
- 158868354
- Full Text :
- https://doi.org/10.1107/S2053273322007483