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10.1371/journal.pone.0155718 http://scihub22266oqcxt.onion/10.1371/journal.pone.0155718 C4868339!4868339 !27182701     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27182701 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+One 2016 ; 11 (5 ): e0155718 Nephropedia Template TP {{tp|p=27182701 |t=2016. The Power of Heterogeneity: Parameter Relationships from Distributions |pdf=|usr=}}{{27182701 }} gab.com Text The Power of Heterogeneity: Parameter Relationships from Distributions JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27182701 #FOAvip 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. Twit Text FOAVip The Power of Heterogeneity: Parameter Relationships from Distributions ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27182701 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27182701 #FOAvip English Wikipedia <ref>{{Cite pmid|27182701 }}</ref> The Power of Heterogeneity: Parameter Relationships from Distributions #MMPMID27182701 Röding M ; Bradley SJ ; Williamson NH ; Dewi MR ; Nann T ; Nydén M PLoS One 2016[]; 11 (5 ): e0155718 PMID27182701 show ga 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. |*Models, Statistical [MESH] |Algorithms [MESH] |Computer Simulation [MESH]The Power of Heterogeneity: Parameter Relationships from Distributions(epmc).pdf DeepDyve Pubget Overpricing