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

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.

Automated summary

The research investigates a prototype application for machine-readable literature, pyDataRecognition, which allows users to search literature based on experimental data sets. The program compares user data with existing database entries and ranks them according to similarity scores. Users can upload data and receive top-ranked papers with digital object identifiers and full references.