Beginning with such famous cases as the Harrisburg conspiracy trial, the use of "scientific" jury selection has gained wide publicity and numerous advocates. Both profit and nonprofit organizations are increasingly offering such services for "good" causes and/or hard cash. Yet no rigorous evaluation of scientific jury selection has ever been undertaken, and impressionistic data on its effectiveness are at best equivocal. In an effort to present a more balanced assessment, this paper undertakes a consciously skeptical examination of the kinds of survey data routinely used to inform the juror- selection process.
The exotic internal structure of polar topologies in multiferroic materials offers a rich landscape for materials science research. As the spatial scale of these entities is often subatomic in nature, aberration-corrected transmission electron microscopy (TEM) is the ideal characterization technique. Software to quantify and visualize the slight shifts in atomic placement within unit cells is of paramount importance due to the now routine acquisition of images at such resolution. In the previous ~decade since the commercialization of aberration-corrected TEM, many research groups have written their own code to visualize these polar entities. More recently, open-access Python packages have been developed for the purpose of TEM atomic position quantification. Building on these packages, we introduce the TEMUL Toolkit: a Python package for analysis and visualization of atomic resolution images. Here, we focus specifically on the TopoTEM module of the toolkit where we show an easy to follow, streamlined version of calculating the atomic displacements relative to the surrounding lattice and thus plotting polarization. We hope this toolkit will benefit the rapidly expanding field of topology-based nano-electronic and quantum materials research, and we invite the electron microscopy community to contribute to this open-access project.
In the previous decade, open-course python packages, such as Hyperspy [1], have gifted researchers in the field of electron microscopy (EM) with easy to use, well-documented, and most-importantly flexible analysis tools. Although there is a small initial barrier for those not versed in programming and scripting, the simplicity and depth of documentation of these python packages allow many to contribute to their development. The power of these programs is the ability to adapt and create code for many areas of data and image analysis. Combining python-based packages developed for EM and material science such as Hyperspy [2], RigidRegistration [3], Atomap [4], PyPrismatic [5,6] and Atomic Simulation Environment (ASE) [7] allows for automated and reproducible image analysis.136
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