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    The Power of Heterogeneity: Parameter Relationships from Distributions.

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    Author
    Röding, M; Bradley, SJ; Williamson, NH; Dewi, MR; Nann, T; Nydén, M
    Date
    2016
    Source Title
    PLoS One
    Publisher
    Public Library of Science (PLoS)
    University of Melbourne Author/s
    Bradley, Siobhan
    Affiliation
    School of Chemistry
    Metadata
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    Document Type
    Journal Article
    Citations
    Röding, M., Bradley, S. J., Williamson, N. H., Dewi, M. R., Nann, T. & Nydén, M. (2016). The Power of Heterogeneity: Parameter Relationships from Distributions.. PLoS One, 11 (5), pp.e0155718-. https://doi.org/10.1371/journal.pone.0155718.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253710
    DOI
    10.1371/journal.pone.0155718
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868339
    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.

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