Polar projections for big data analysis in applied superconductivity

There is a growing problem to represent and analyse large experimental datasets in many emerging fields of science aside of traditional big data-based disciplines, i.e., elementary particles, genetics/genomics and geoscience. One of these emerging fields is applied superconductivity where recently a large, regularly up-dated, public database of critical currents of commercial superconductors was established. The size, dimensionality and resolution of this data makes current methods of display and analysis inadequate. As is often the case in physics and materials science, when dealing with any anisotropic properties, one measures the effects of rotations around a low symmetry axis, this is also the case in critical current measurements as found in applied superconductivity. In this paper we propose the use of polar projected images to map these much larger data sets into useful visualizations for analysis. Where we suggest the radial coordinate and the colour represent amplitudes of two measured parameters, and sample rotation angle is naturally mapped to the polar coordinate. We demonstrate the advantage of this projection for analysing, otherwise unwieldy large, critical current datasets, and naturally recover previously used empirical relations.