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

De Leo, N, Michaud, M, Maiorano, L, Meloro, C ORCID: 0000-0003-0175-1706, Chatar, N and Tamagnini, D (2026) Open access and digital morphology data in evolutionary biology: expanding frontiers of knowledge. BMC Ecol Evol, 26 (1). ISSN 1471-2148

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	3D morphometrics; Digital morphology; Evolutionary biology; FAIR principles; Open access; Biological Evolution; Imaging, Three-Dimensional; Access to Information; Information Dissemination; Humans; Machine Learning; 31 Biological Sciences; 3103 Ecology; 3104 Evolutionary Biology; Bioengineering; Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); Data Science; Generic health relevance; 3 Good Health and Well Being
Subjects:	Q Science > QA Mathematics > QA76 Computer software Q Science > QH Natural history Q Science > QH Natural history > QH301 Biology
Divisions:	Biological and Environmental Sciences (from Sep 19)
Publisher:	Springer Science and Business Media LLC
Date of acceptance:	20 April 2026
Date of first compliant Open Access:	13 May 2026
Date Deposited:	13 May 2026 12:51
Last Modified:	13 May 2026 12:51
DOI or ID number:	10.1186/s12862-026-02522-y
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28565

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