Source code for reproducing "Predicting Dreissenid Mussel Abundance using Deep Learning" by Galloway et al..
Machine learning projects done as part of an online PGPML course offered by Great Learning, an ed-tech company.
Sussman A.L., Gardner B., Adams E.M., Salas L., Kenow K.P., Luukkonen D.R., Monfils M.J., Mueller W.P., Williams K.A., Leduc-Lapierre M., & Zipkin E.F. 2019. A comparative analysis of common methods to identify waterbird hotspots. MEE 10(9): 1454-1468.
A sensors.AFRICA project to send SMS alerts of storms around the great lakes region. Accessible at https://stormwatch.sensors.africa/
This repository contains the entire methodological workflow described in Martin et al. (2025) – "Spatial-Temporal Patterns of Per- and Polyfluoroalkyl Substances (PFAS) in the Biota of the Laurentian Great Lakes: A Meta-Analysis"
Exploring and modeling the relationships between lake characteristics, surface temperature, and ice concentration in the Great Lakes.
Bayesian phylogeographic reconstruction and interactive visual analytics of Zaire, Sudan and Bundibugyo ebolavirus spread in the Great-Lakes basin
For accessing Great Lakes buoy API data. Created for GLOS Data Challenge Hackathon 2016
Contains R codes for processing and analyzing data for Great Lakes Cladophora work
CHR-2022 data was used to analyze factors impacting mental health in five Great Lakes states.
Empirical project for Dr. Chris Stoddard's ECNS 562
Great Lakes temperature analysis application developed for CPS125.
data and scripts from the St. Louis River Estuary (SLRE)
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