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Synopsis Morphological features are the primary identifying properties of most animals and key to many comparative physiological studies, yet current techniques for preservation and documentation of soft-bodied marine animals are limited in terms of quality and accessibility. Digital records can complement physical specimens, with a wide array of applications ranging from species description to kinematics modeling, but options are lacking for creating models of soft-bodied semi-transparent underwater animals. We developed a lab-based technique that can live-scan semi-transparent, submerged animals, and objects within seconds. To demonstrate the method, we generated full three-dimensional reconstructions (3DRs) of an object of known dimensions for verification, as well as two live marine animals—a siphonophore and an amphipod—allowing detailed measurements on each. Techniques like these pave the way for faster data capture, integrative and comparative quantitative approaches, and more accessible collections of fragile and rare biological samples. 

Abstract The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of this required data collection rapidly outpaces our abilities to process and analyze them. Recent advances in machine learning enables fast, sophisticated analysis of visual data, but have had limited success in the ocean due to lack of data standardization, insufficient formatting, and demand for large, labeled datasets. To address this need, we built FathomNet, an open-source image database that standardizes and aggregates expertly curated labeled data. FathomNet has been seeded with existing iconic and non-iconic imagery of marine animals, underwater equipment, debris, and other concepts, and allows for future contributions from distributed data sources. We demonstrate how FathomNet data can be used to train and deploy models on other institutional video to reduce annotation effort, and enable automated tracking of underwater concepts when integrated with robotic vehicles. As FathomNet continues to grow and incorporate more labeled data from the community, we can accelerate the processing of visual data to achieve a healthy and sustainable global ocean.