Open access and digital morphology data in evolutionary biology: expanding frontiers of knowledge.

The recent integration of 3D imaging and digital methodologies has revolutionized evolutionary biology, offering unprecedented opportunities for analysing and sharing morphological data. However, the transition toward open access remains incomplete due to persistent technical, legal, and institutional barriers. Issues such as lack of standardization, massive file sizes, and unclear intellectual property rights continue to hinder data verification and reproducibility. These challenges have acquired new urgency with the rapid rise of machine learning and AI-based tools for automated segmentation, landmarking, and shape analysis, which require large, standardized, and openly accessible training datasets - making inaccessible 3D data not merely an inconvenience, but a source of systematic bias in the algorithms shaping the field's future. This review synthesizes technical, legal, and behavioural perspectives on open data in digital morphology, building on prior work to address the specific challenges of the current AI era. By advocating for the adoption of FAIR principles, the use of persistent digital identifiers, and the implementation of digital watermarking, we offer recommendations for establishing minimum standards in data publication. Ultimately, a shift toward responsible data stewardship is essential to ensuring that digital morphological resources remain accessible, reproducible, and scientifically valuable for both human and computational users.