The Power of Heterogeneity: Parameter Relationships from Distributions

Magnus Röding; Siobhan J Bradley; Nathan H Williamson; Melissa R Dewi; Thomas Nann; Magnus Nydén

doi:10.1371/journal.pone.0155718

The Power of Heterogeneity: Parameter Relationships from Distributions

PLoS One. 2016 May 16;11(5):e0155718. doi: 10.1371/journal.pone.0155718. eCollection 2016.

Authors

Magnus Röding^{1

2

3}, Siobhan J Bradley^{2

4}, Nathan H Williamson², Melissa R Dewi^{2

5}, Thomas Nann^{2

5

4}, Magnus Nydén³

Affiliations

¹ SP Food and Bioscience, Soft Materials Science, Göteborg, Sweden.
² Future Industries Institute, University of South Australia, Adelaide, Australia.
³ School of Energy and Resources, UCL Australia, University College London, Adelaide, Australia.
⁴ MacDiarmid Institute, Victoria University of Wellington, Wellington, New Zealand.
⁵ ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Ian Wark Research Institute, University of South Australia, Adelaide, Australia.

Abstract

Complex scientific data is becoming the norm, many disciplines are growing immensely data-rich, and higher-dimensional measurements are performed to resolve complex relationships between parameters. Inherently multi-dimensional measurements can directly provide information on both the distributions of individual parameters and the relationships between them, such as in nuclear magnetic resonance and optical spectroscopy. However, when data originates from different measurements and comes in different forms, resolving parameter relationships is a matter of data analysis rather than experiment. We present a method for resolving relationships between parameters that are distributed individually and also correlated. In two case studies, we model the relationships between diameter and luminescence properties of quantum dots and the relationship between molecular weight and diffusion coefficient for polymers. Although it is expected that resolving complicated correlated relationships require inherently multi-dimensional measurements, our method constitutes a useful contribution to the modelling of quantitative relationships between correlated parameters and measurements. We emphasise the general applicability of the method in fields where heterogeneity and complex distributions of parameters are obstacles to scientific insight.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computer Simulation
Data Mining / methods*
Models, Statistical*

Grants and funding

This research was funded by the South Australian Government Premier’s Research and Industry Grant project “A Systems Approach to Surface Science” and partly by the Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology (project number CE140100036). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.