Search Results

Search Constraints

Start Over
Filtering by: Keyword neural network Remove constraint Keyword: neural network

Search Results

  • Skelevision Dataset

    work
    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

  • Data for Improvement of Plasma Sheet Neural Network Accuracy with Inclusion of Physical Information

    work
    Creator:
    Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
    Description:
    We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
    Keyword:
    neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
    Citation to related publication:
    Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7.  https://doi.org/10.3389/fspas.2020.00042
    Discipline:
    Science and Engineering