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Shape characterizers are metrics that quantify aspects of the overall geometry of a three‐dimensional (3D) digital surface. When computed for biological objects, the values of a shape characterizer are largely independent of homology interpretations and often contain a strong ecological and functional signal. Thus, shape characterizers are useful for understanding evolutionary processes. Dirichlet normal energy (DNE) is a widely used shape characterizer in morphological studies. Recent studies found that DNE is sensitive to various procedures for preparing 3D mesh from raw scan data, raising concerns regarding comparability and objectivity when utilizing DNE in morphological research. We provide a robustly implemented algorithm for computing the Dirichlet energy of the normal (ariaDNE) on 3D meshes. We show through simulation that the effects of preparation‐related mesh surface attributes, such as triangle count, mesh representation, noise, smoothing and boundary triangles, are much more limited on ariaDNE than DNE. Furthermore, ariaDNE retains the potential of DNE for biological studies, illustrated by its effectiveness in differentiating species by dietary preferences. Use of ariaDNE can dramatically enhance the assessment of the ecological aspects of morphological variation by its stability under different 3D model acquisition methods and preparation procedure. Towards this goal, we provide scripts for computing ariaDNE and ariaDNE values for specimens used in previously published DNE analyses.

Large scale digitization projects such as #ScanAllFishes and oVert are generating high-resolution microCT scans of vertebrates by the thousands. Data from these projects are shared with the community using aggregate 3D specimen repositories like MorphoSource through various open licenses. MorphoSource currently hosts tens of thousands of 3D scans of eukaryotes. Along with the data from similarly scoped projects such as 10kPhenomes, DigiMorph and many others, soon hundreds of thousands of specimens that represent biodiversity of extinct and extant organisms will be conveniently available to researchers. We anticipate an explosion of quantitative research in organismal biology with the convergence of available data and the methodologies to analyze them.Though the data are available, the road from a series of images to analysis is fraught with challenges for most biologists. It involves tedious tasks of data format conversions, preserving spatial scale of the data accurately, 3D visualization and segmentations, acquiring measurements and annotations. When scientists use commercial software with proprietary formats, a roadblock for data exchange, collaboration, and reproducibility is erected that hurts the efforts of the scientific community to broaden participation in research. Another relevant concern is that ultimate derivative data from individual research projects (e.g., 3D models of segmentation) are shared in formats that do not preserve the correct spatial scale of the data.In this paper, we present our effort to tackle challenges biologists face when conducting 3D specimen-based research. We developed SlicerMorph as an extension of 3D Slicer, a biomedical visualization and analysis ecosystem with extensive visualization and segmentation capabilities built on proven python-scriptable open-source libraries such as Visualization Toolkit and Insight Toolkit. In addition to the core functionalities of Slicer, SlicerMorph provides users with modules to conveniently retrieve open-access 3D models or import users own 3D volumes, to annotate 3D curve and patch-based landmarks, generate canonical templates, conduct geometric morphometric analyses of 3D organismal form using both landmark-driven and landmark-free approaches, and create 3D animations from their results. We highlight how these individual modules can be tied together to establish complete workflow(s) from image sequence to morphospace. Our software development efforts were supplemented with short courses and workshops that cover the fundamentals of 3D imaging and morphometric analyses as it applies to study of organismal form and shape in evolutionary biology, and extensive links to the existing tutorials are provided as supplemental material.Our goal is to establish a community of organismal biologists centered around Slicer and SlicerMorph to facilitate easy exchange of data and results and collaborations using 3D specimens. Our proposition to our colleagues is that using a common open platform supported by a large user and developer community ensures the longevity and sustainability of the tools beyond the initial development effort.

In 2017 NSF funded "oVert (openVertebrate): Open Exploration of Vertebrate Diversity in 3D," which is the first Thematic Collections Network devoted entirely to vertebrate morphological specimens. The primary goal of oVert is to generate and serve highresolution digital three-dimensional data for internal anatomy across vertebrate diversity. oVert will CT-scan >20,000 fluid-preserved specimens representing >80% of the living genera of vertebrates, providing broad coverage for exploration and research on all major groups of vertebrates. Contrast-enhanced scans will be generated to reveal soft tissues and organs for a majority of the living vertebrate families. This collection of digital imagery and three-dimensional volumes will be open for exploration, download, and use. These new media will provide unprecedented global access to valuable morphological data of specimens in US collections.oVert is developing best practices and guidelines for highthroughput CT-scanning, including efficient workflows, preferred resolutions, and archival formats that optimize the variety of downstream applications.

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