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X-ray radiography and computed tomography (CT) reveal hidden subsurface features within fossil specimens embedded in matrix. With X-rays, distinguishing features from the background (i.e., contrast) results from sample density and atomic X-ray attenuation—fundamental properties of the sample. However, even high energy X-rays may poorly resolve hard and soft tissue structures when the matrix has similar density or X-ray attenuation to the fossil. Here, neutron radiography and neutron tomography complement X-ray imaging, as the source of contrast comes instead from how a neutron beam interacts with the sample's atomic nuclei. The contrast is highly nonlinear across the periodic table, and so researchers can see enhanced contrast between adjacent features when X-ray imaging could not. As the signal source is completely different than X-ray imaging, some intuition from X-rays must be discarded. For instance, neutrons quite easily pass through lead, but are blocked by hydrogen. Since neutron imaging is uncommon within paleontology, we introduce this exciting technology at a high level with an emphasis on applications to paleontology. We cover some basic physics underlying neutron imaging, where one can perform such experiments, and sample considerations. The neutron source, concepts of beam flux, and image resolution will also be covered. As neutron imaging typically complements X-ray imaging, we discuss how to digitally combine modalities for segmentation and inference. We present examples of how neutron imaging informed fossil descriptions. This includes the skull of a Paleocene mammal Tetraclaenodon from New Mexico and a variety of Permian vertebrate specimens from Richards Spur, Oklahoma and imaged at the DINGO nuclear imaging facility in Australia. Though neutron sources will always be difficult to access, we aim to assist interested researchers considering this exciting imaging technology for their paleontology research. 

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.