Forking anatomy: How MorphoDepot applies the open‐source development model to 3D digital morphology

The increasing use of 3D imaging technologies in biological sciences is generating vast repositories of anatomical data, yet significant barriers prevent these data from reaching its full potential in educational and collaborative contexts. While sharing raw computed tomography and magnetic resonance imaging scans has become routine, distributing value‐added segmented datasets—where anatomical structures are precisely labelled and delineated—remains difficult and rare. Current repositories function primarily as static archives, lacking mechanisms for iterative refinement, community‐driven curation and the controlled terminology essential for downstream computational applications, including artificial intelligence (AI). We propose that segmented 3D morphological datasets should be reconceptualized as collaborative, version‐controlled projects rather than static archival products. We introduce MorphoDepot, a framework that adapts the ‘fork‐and‐contribute’ model—a cornerstone of modern open‐source software development—for collaborative management of 3D morphological data. By integrating git version control and GitHub's collaborative infrastructure with 3D Slicer and its SlicerMorph extension, MorphoDepot transforms segmented anatomical datasets into dynamic, community‐curated projects. By treating each anatomical dataset as an individual repository that can be forked, modified, reviewed and merged, MorphoDepot enables communities of researchers, educators and students to co‐create high‐quality, standardized anatomical atlases through a transparent, auditable process. This approach directly addresses the challenges of distributed collaboration, enforces transparent provenance tracking and creates high‐quality, standardized training data for AI model development. The result is a system that embodies FAIR (Findable, Accessible, Interoperable and Reusable) data principles while creating powerful new opportunities for remote learning and collaborative science. We argue that format accessibility and collaborative curation are inseparable, genuine community‐driven data refinement requires open formats and open tools as prerequisites for participation.