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ABSTRACT Jumping is a crucial behavior in fitness-critical activities including locomotion, resource acquisition, courtship displays and predator avoidance. In primates, paleontological evidence suggests selection for enhanced jumping ability during their early evolution. However, our interpretation of the fossil record remains limited, as no studies have explicitly linked levels of jumping performance with interspecific skeletal variation. We used force platform analyses to generate biomechanical data on maximal jumping performance in three genera of callitrichine monkeys falling along a continuum of jumping propensity: Callimico (relatively high propensity jumper), Saguinus (intermediate jumping propensity) and Callithrix (relatively low propensity jumper). Individuals performed vertical jumps to perches of increasing height within a custom-built tower. We coupled performance data with high-resolution micro-CT data quantifying bony features thought to reflect jumping ability. Levels of maximal performance between species – e.g. maximal take-off velocity of the center of mass (CoM) – parallel established gradients of jumping propensity. Both biomechanical analysis of jumping performance determinants (e.g. CoM displacement, maximal force production and peak mechanical power during push-off) and multivariate analyses of bony hindlimb morphology highlight different mechanical strategies among taxa. For instance, Callimico, which has relatively long hindlimbs, followed a strategy of fully extending of the limbs to maximize CoM displacement – rather than force production – during push-off. In contrast, relatively shorter-limbed Callithrix depended mostly on relatively high push-off forces. Overall, these results suggest that leaping performance is at least partially associated with correlated anatomical and behavioral adaptations, suggesting the possibility of improving inferences about performance in the fossil record. 

Abstract As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly analyze these new large, complex datasets. Here we describe novel computational methods to capture and quantify volumetric information, and to efficiently characterize and compare shape volumes. It is based on innovative theoretical and computational reformulation of volumetric computing. It consists of two theoretical constructs and their numerical implementation: the spherical wave decomposition ( SWD ), that provides fast, accurate automated characterization of shapes embedded within complex 3D datasets; and symplectomorphic registration with phase space regularization by entropy spectrum pathways ( SYMREG ), that is a non-linear volumetric registration method that allows homologous structures to be correctly warped to each other or a common template for comparison. Together, these constitute the Shape Analysis for Phenomics from Imaging Data ( SAPID ) method. We demonstrate its ability to automatically provide rapid quantitative segmentation and characterization of single unique datasets, and both inter-and intra-specific comparative analyses. We go beyond pairwise comparisons and analyze collections of samples from 3D data repositories, highlighting the magnified potential our method has when applied to data collections. We discuss the potential of SAPID in the broader context of generating normative morphologies required for meaningfully quantifying and comparing variations in complex 3D anatomical structures and systems. 

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.