Primate Phenotypes: A Multi-Institution Collection of 3D Morphological Data Housed in MorphoSource.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-18 DOI:10.1038/s41597-024-04261-5

Sergio Almécija, Kelsey D Pugh, Alisha Anaya, Christopher M Smith, Nancy B Simmons, Robert S Voss, Neil Duncan, Darrin P Lunde, Megan K Viera, Teresa Hsu, Emmanuel Gilissen, Stephanie A Maiolino, Julie M Winchester, Biren A Patel, Caley M Orr, Matthew W Tocheri, Eric Delson, Ashley S Hammond, Doug M Boyer, Santiago A Catalano

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引用次数: 0

Abstract

The field of phenomics is experiencing unprecedented advances thanks to the rapid growth of morphological quantification based on three-dimensional (3D) imaging, online data repositories, team-oriented collaborations, and open data-sharing policies. In line with these progressions, we present an extensive primate phenotypic dataset comprising >6,000 3D scans (media) representing skeletal morphologies of 386 individual specimens covering all hominoid genera (except humans) and other selected primates. The digitized specimens are housed in physical collections at the American Museum of Natural History, the National Museum of Natural History, the Royal Museum for Central Africa (Belgium), the Cleveland Museum of Natural History, and Stony Brook University. Our technical validation indicates that despite the diverse digitizing devices used to produce the scans, the final 3D models (meshes) can be safely combined to collect comparable morphometric data. The entire dataset (and detailed associated metadata) is freely available through MorphoSource. Hence, these data contribute to empowering the future of primate phenomics and providing a roadmap for future digitization and archiving of digital data from other collections.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.