Experimental Research Data Quality In Materials Science

TL;DR

This paper discusses the challenges and proposes a systematic methodology for measuring and assuring the quality of experimental research data in materials science to enhance data accessibility and dissemination.

Contribution

It introduces a combined approach of methods for systematically assessing and ensuring the quality of materials science experimental data before publication.

Findings

Identified key requirements and challenges for data quality measurement.

Critically assessed various methods for data quality evaluation.

Presented a practical, combined approach for data quality assurance.

Abstract

In materials sciences, a large amount of research data is generated through a broad spectrum of different experiments. As of today, experimental research data including meta-data in materials science is often stored decentralized by the researcher(s) conducting the experiments without generally accepted standards on what and how to store data. The conducted research and experiments often involve a considerable investment from public funding agencies that desire the results to be made available in order to increase their impact. In order to achieve the goal of citable and (openly) accessible materials science experimental research data in the future, not only an adequate infrastructure needs to be established but the question of how to measure the quality of the experimental research data also to be addressed. In this publication, the authors identify requirements and challenges towards a systematic methodology to measure experimental research data quality prior to publication and derive different approaches on that basis. These methods are critically discussed and assessed by their contribution and limitations towards the set goals. Concluding, a combination of selected methods is presented as a systematic, functional and practical quality measurement and assurance approach for experimental research data in materials science with the goal of supporting the accessibility and dissemination of existing data sets.