A package of two computation methods ‘Correlation’ and ‘Scedasticity’ extracts critical information on sample response from series of large ranked data sets. The methods used are based on correlation functions and statistical scedasticity formalism, and thus they are generally applicable to all types of spectra and diffractograms that evolve as a function of any arbitrary parameter. This allows large datasets that are accumulated via high throughput experiments to be analyzed straightforwardly, addressing a major challenge in parametric studies. The methods can be readily applied for on-the-fly data analysis during various in situ or operando experiments, providing immediate feedback on regions of interest for greater experimental consideration. Likewise, they may be used for mining significance during post-experiment data analysis, where considerable volumes of data prevents point-by-point inspection.

Furthermore, a two-dimensional (2D) reactivity pattern calculated as the scedasticity along all measured reciprocal space of all successive diffraction pattern pairs unveils clearly the structural evolution path and the active areas of interest during the synthesis (see figures below).