Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern
Abstract
We investigate a prototype application for machine-readable literature. 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 (doi) 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.
Cite
@article{arxiv.2204.00434,
title = {Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern},
author = {Berrak Özer and Martin A. Karlsen and Zachary Thatcher and Ling Lan and Brian McMahon and Peter R. Strickland and Simon P. Westrip and Koh S. Sang and David G. Billing and Dorthe B. Ravnsbæk and Simon J. L. Billinge},
journal= {arXiv preprint arXiv:2204.00434},
year = {2022}
}
Comments
27 pages, 4 figures