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

TL;DR

This paper presents 'pyDataRecognition', a prototype tool that enables machine-readable literature searches by matching user-uploaded powder diffraction patterns with a database of published data, facilitating more direct data-driven literature exploration.

Contribution

The paper introduces a novel data-driven approach for literature search using experimental powder diffraction data, demonstrating a prototype application that ranks relevant papers based on pattern similarity.

Findings

Effective matching of diffraction patterns to literature references

Identification of challenges in pattern comparison and database coverage

Potential for streamlining literature discovery in materials science

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.