Röding Magnus, Bradley Siobhan J, Williamson Nathan H, Dewi Melissa R, Nann Thomas, Nydén Magnus
SP Food and Bioscience, Soft Materials Science, Göteborg, Sweden.
Future Industries Institute, University of South Australia, Adelaide, Australia.
PLoS One. 2016 May 16;11(5):e0155718. doi: 10.1371/journal.pone.0155718. eCollection 2016.
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.
复杂的科学数据正变得越来越常见，许多学科的数据量极大且不断丰富，人们通过进行高维测量来解析参数之间的复杂关系。诸如核磁共振和光谱学等固有的多维测量能够直接提供有关各个参数的分布及其相互关系的信息。然而，当数据源自不同测量且形式各异时，解析参数关系就成了数据分析而非实验的问题。我们提出了一种解析单独分布且相互关联的参数之间关系的方法。在两个案例研究中，我们对量子点的直径与发光特性之间的关系以及聚合物的分子量与扩散系数之间的关系进行了建模。尽管预计解析复杂的相关关系需要固有的多维测量，但我们的方法对相关参数与测量之间定量关系的建模做出了有益贡献。我们强调该方法在参数的异质性和复杂分布阻碍科学洞察的领域具有普遍适用性。
