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Updated: Aug 30, 2025

$X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects$

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Published on: June 8, 2016

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

Berrak Özer¹, Martin A Karlsen², Zachary Thatcher¹

¹Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, 10027, USA.

Acta Crystallographica. Section A, Foundations and Advances

|September 1, 2022

Summary

This summary is machine-generated.

pyDataRecognition enables data-driven literature searches by comparing user powder patterns to a database, identifying relevant scientific papers. This machine-readable literature approach aids researchers in discovering and accessing key publications efficiently.

Keywords:

CIF data similarity data-driven literature search machine-readable scientific literature powder diffraction

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Area of Science:

Materials Science
Crystallography
Data Science

Background:

Scientific literature is a rich source of experimental data, but accessing specific information can be challenging.
Traditional literature searches rely on keyword matching, which may not capture nuanced data relationships.
Developing machine-readable formats for scientific data is crucial for advanced data mining and analysis.

Purpose of the Study:

To investigate a prototype application, pyDataRecognition, for machine-readable literature searches.
To demonstrate a data-driven approach where experimental data serves as the search query.
To facilitate the retrieval of relevant scientific papers based on experimental data similarity.

Main Methods:

The pyDataRecognition application was developed as a prototype.
Users upload experimental powder diffraction patterns and radiation wavelength data.
The program compares user data against a database of known powder patterns using similarity scoring.

Main Results:

The application successfully compares user-provided data to a database of powder patterns.
It generates a rank-ordered list of publications based on similarity scores.
Results include digital object identifiers, full references, and comparative plots of diffraction patterns.

Conclusions:

pyDataRecognition offers a novel, data-driven method for literature discovery.
The application demonstrates the potential of machine-readable literature for scientific research.
Further development is needed to address challenges and enhance functionality.