The human factor: results of a small-angle scattering data analysis Round Robin

TL;DR

This study evaluates how human factors influence small-angle scattering data analysis through a Round Robin experiment, revealing variability in results and proposing solutions to improve consistency.

Contribution

It provides an empirical assessment of human impact on data analysis accuracy and discusses causes for discrepancies, offering solutions to enhance reliability.

Findings

Most participants correctly identified the number of populations in dilute samples.

Half of the population mean estimates were within 1.5%.

Results on powder datasets showed larger variability, with half within 44-86%.

Abstract

A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44% and 86% respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed.