arXiv Open Access 2022

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

Berrak Özer Martin A. Karlsen Zachary Thatcher Ling Lan Brian McMahon +6 lainnya

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Abstrak

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.

Topik & Kata Kunci

cond-mat.mtrl-sci cs.DL

Penulis (11)

Berrak Özer

Martin A. Karlsen

Zachary Thatcher

Ling Lan

Brian McMahon

Peter R. Strickland

Simon P. Westrip

Koh S. Sang

David G. Billing

Dorthe B. Ravnsbæk

Simon J. L. Billinge

Format Sitasi

APA MLA BibTeX

Özer, B., Karlsen, M.A., Thatcher, Z., Lan, L., McMahon, B., Strickland, P.R. et al. (2022). Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern. https://arxiv.org/abs/2204.00434

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓