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Abstract Turtles are a charismatic reptile group with a peculiar body plan, which most notably includes the shell. Anatomists have often focused descriptive efforts on the shell and other strongly derived body parts, such as the akinetic skull, or the cervical vertebrae. Other parts of turtle osteology, like the girdles, limbs, and mandibles, are documented with less rigor and detail. The mandible is the primary skeletal element involved in food acquisition and initial food processing of turtles, and its features are thus likely linked to feeding ecology. In addition, the mandible of turtles is composed of up to seven bones (sometimes fused to as little as three) and has thus anatomical complexity that may be insightful for systematic purposes and phylogenetic research. Despite apparent complexity and diversity to the mandible of turtles, this anatomical system has not been systematically studied, not even in search of characters that might improve phylogenetic resolution. Here, we describe the mandibular osteology for all major subclades of extant turtles with the help of digitally dissected 3D models derived from high‐resolution computed tomography (μCT) scans of 70 extant species. We provide 31 fully segmented mandibles, as well as 3D models of the mandibular musculature, innervation, and arterial circulation of the cryptodireDermatemys mawii. We synthesize observed variation into 51 morphological characters, which we optimize onto a molecular phylogeny. This analysis shows some mandibular characters to have high systematic value, whereas others are highly homoplastic and may underlie ecological influences or other factors invoking variation. 

Abstract The impact of preserved museum specimens is transforming and increasing by three-dimensional (3D) imaging that creates high-fidelity online digital specimens. Through examples from the openVertebrate (oVert) Thematic Collections Network, we describe how we created a digitization community dedicated to the shared vision of making 3D data of specimens available and the impact of these data on a broad audience of scientists, students, teachers, artists, and more. High-fidelity digital 3D models allow people from multiple communities to simultaneously access and use scientific specimens. Based on our multiyear, multi-institution project, we identify significant technological and social hurdles that remain for fully realizing the potential impact of digital 3D specimens. 

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.