Christopher L. Farrow

Addresses a need to gain scientific insights from one-dimensional (1D) scientific spectra in model-independent ways. A powerful approach for this is to take differences between pairs of spectra and look for meaningful changes that might indicate underlying chemical, structural, or other modifications. The challenge is that the difference curve may contain uninteresting differences such as experimental inconsistencies and benign physical changes such as the effects of thermal expansion. allows researchers to apply simple transformations, or “morphs,” to one of the datasets to remove the unwanted differences, revealing, when they are present, nontrivial differences. is an open-source Python package available on the Python Package Index and conda-forge. Here, we describe its functionality and apply it to solve a range of experimental challenges on diffraction and pair distribution function data from x-rays and neutrons, though we note that it may be applied to any 1D function in principle.

This chapter takes a step back from the technical details of data-driven product development methods to discuss the broader industrial R&D digital transformation context within which these projects most often reside. It discusses the business factors that answer why these methods are so important to industrial competitive advantage, what other digital technologies are involved in full solutions, and how they all work together to form new ways of working in the lab to create innovative product offerings. Importantly, it also overviews several critical non-technical challenges that must be considered and overcome to be successful. Finally, based on Enthought’s R&D digital transformation experience over two decades, the final section provides essential advice on how full solutions can be implemented and continuously improved in a way that maximizes value and mitigates risk.