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Amanda Ramcharan, graduate student, Agricultural and Biological Engineering
My research focuses on applying the latest computational methods to problems in agriculture. For my dissertation, I applied machine learning methods to 3 US soil databases and over 200 environmental datasets to generate complete coverage, gridded soil maps of the conterminous US at 100 m resolution. This machine learning approach makes soil maps easy to update and more accessible to diverse groups of scientists who rely on soil data. This project also has a GitHub page available at https://github.com/aramcharan/US_SoilGrids100m
Why does Amanda use ScholarSphere?
I am passionate about sharing knowledge through open access data with scientists around the world therefore Scholarsphere is a great website to host, share, and archive open access data with other researchers. As science research becomes more data intensive and publishers request reproducible research, Scholarsphere provides an important service to Penn State scientists.
See the other research Amanda has deposited into ScholarSphere