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Abstract The presence of inclusions, twinning, and low-angle grain boundaries, demanded to exist by the third law of thermodynamics, drive the behavior of quantum materials. Identification and quantification of these structural complexities often requires destructive techniques. X-ray micro-computed tomography (µCT) uses high-energy X-rays to non-destructively generate 3D representations of a material with micron/nanometer precision, taking advantage of various contrast mechanisms to enable the quantification of the types and number of inhomogeneities. We present case studies of µCT informing materials design of electronic and quantum materials, and the benefits to characterizing inclusions, twinning, and low-angle grain boundaries as well as optimizing crystal growth processes. We discuss recent improvements in µCT instrumentation that enable elemental analysis and orientation to be obtained on crystalline samples. The benefits of µCT as a non-destructive tool to analyze bulk samples should encourage the community to adapt this technology into everyday use for quantum materials discovery. 

Our ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives.