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- Creator:
- Brian C. Weeks
- Description:
- Description: Each folder contains all of the data for a specific specimen; the folder names correspond to the University of Michigan Museum of Zoology catalog number for the specimen. Folders with a “-“ in the name are individual specimens that were photographed multiple independent times; the number following the “-“ indicates the repetition number (i.e. the folder named “UMMZ_242382-10” contains the tenth set of photographs for specimen UMMZ 242382). The photographs are necessary to train and test the Skelevision model, which is a computer vision approach to identifying and measuring elements of the skeleton (length of the tibiotarsus, tarsometatarsus, femur, humerus, ulna, radius, carpometacarpus, 2nd digit 1st phalanx, skull, and keel; the outer diameter of the sclerotic ring at its widest point; and the distance from the back of the skull to the tip of the bill). The data span 115 species of passerines across 79 genera from 59 families.
- Keyword:
- Bird skeleton, neural network, and functional traits
- Citation to related publication:
- Weeks, B.C., Zhou, Z., O’Brien, B., Darling, R., Dean, M., Dias, T., Hassena, G., Zhang, M., and Fouhey, D.F. 2022. A deep neural network for high throughput measurement of functional traits on museum skeletal specimens. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13864
- Discipline:
- Science