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.
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
This data was produced as part of field experiment investigating the extent, drivers, and consequences of functional trait variation in cover crops. Specifically, we studied the role of intraspecific trait variation in explaining interactions between species in cover crop mixtures, and whether and how intraspecific trait variation improves understanding of relationships between functional traits and ecosystem services from cover crops.
