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The Power of Heterogeneity: Parameter Relationships from Distributions
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868339/ https://www.ncbi.nlm.nih.gov/pubmed/27182701 http://dx.doi.org/10.1371/journal.pone.0155718 |
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author | Röding, Magnus Bradley, Siobhan J. Williamson, Nathan H. Dewi, Melissa R. Nann, Thomas Nydén, Magnus |
author_facet | Röding, Magnus Bradley, Siobhan J. Williamson, Nathan H. Dewi, Melissa R. Nann, Thomas Nydén, Magnus |
author_sort | Röding, Magnus |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4868339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-48683392016-05-26 The Power of Heterogeneity: Parameter Relationships from Distributions Röding, Magnus Bradley, Siobhan J. Williamson, Nathan H. Dewi, Melissa R. Nann, Thomas Nydén, Magnus PLoS One Research Article 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. Public Library of Science 2016-05-16 /pmc/articles/PMC4868339/ /pubmed/27182701 http://dx.doi.org/10.1371/journal.pone.0155718 Text en © 2016 Röding et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Röding, Magnus Bradley, Siobhan J. Williamson, Nathan H. Dewi, Melissa R. Nann, Thomas Nydén, Magnus The Power of Heterogeneity: Parameter Relationships from Distributions |
title | The Power of Heterogeneity: Parameter Relationships from Distributions |
title_full | The Power of Heterogeneity: Parameter Relationships from Distributions |
title_fullStr | The Power of Heterogeneity: Parameter Relationships from Distributions |
title_full_unstemmed | The Power of Heterogeneity: Parameter Relationships from Distributions |
title_short | The Power of Heterogeneity: Parameter Relationships from Distributions |
title_sort | power of heterogeneity: parameter relationships from distributions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868339/ https://www.ncbi.nlm.nih.gov/pubmed/27182701 http://dx.doi.org/10.1371/journal.pone.0155718 |
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