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Incorporating Drones In My Classroom: Survey Data From High School Teachers 2018, Tyson Sorensen, Kelsey Hall, Olivia Horning, Joshua Dallin, David Francis
Incorporating Drones In My Classroom: Survey Data From High School Teachers 2018, Tyson Sorensen, Kelsey Hall, Olivia Horning, Joshua Dallin, David Francis
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Survey data collected from 69 high school and middle school teachers. Survey gauges intentions of integrating drone curriculum concepts into the classroom.
Dataset includes summary data, raw data and survey instrument.
Genetic Engineering In Agriculture Curricula For Grades 9-12, Tyson Sorensen, Olivia Horning, Kelsey Hall, David Francis, Joshua Dallin
Genetic Engineering In Agriculture Curricula For Grades 9-12, Tyson Sorensen, Olivia Horning, Kelsey Hall, David Francis, Joshua Dallin
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This is a curriculum package that was developed for teachers to be able to integrate genetic engineering and biotechnology concepts, skills, and career applications into their classrooms. This package was developed as part of the LEARN workshop entitled "Genetic Engineering: Workshop for Teachers." This curriculum package includes five full units of instructions with lesson plans, presentation resources, and other resources for teachers. This package is intended for students in grades 9-12.
Each unit is complete with the corresponding slides found in the main Genetic Engineering PowerPoint. Teachers have the liberty to cover the material on an as-needed bases based on …
Sdnet2018: A Concrete Crack Image Dataset For Machine Learning Applications, Marc Maguire, Sattar Dorafshan, Robert J. Thomas
Sdnet2018: A Concrete Crack Image Dataset For Machine Learning Applications, Marc Maguire, Sattar Dorafshan, Robert J. Thomas
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SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep learning convolutional neural networks, which are a subject of continued research in the field …