Dartmouth DIFUSE's repositories
geography-extreme-climate-events
Students examine drought, famine, floods, landslides and other extreme weather events looking through the lens of climate change, while developing skills in Python’s Numpy and pandas.
anthropology-behavior-sampling
Students use two standard data collection methods in anthropology, focal bout sampling and instantaneous scan sampling, on video footage of a basketball game to create Excel data sets on shot taking.
anthropology-footprint-collect-analyze
Students use anthropological methods to make inferences about societies with fossil records. Students collect data on their own footprints, analyze aggregated class data, and use their insights to infer behaviors of historical populations.
astronomy-celestial-bodies
Students learn the way astrophysicists manipulate observatory data and perform analyses in Google Colab Python notebooks with an emphasis on data visualization and plot interpretation.
astronomy-imaging
Students use astrometric analysis to estimate orbits, mass, and statistical error with regards to Uranus and its moons.
biology-remote-sensing
Students learn data analysis and visualization in Google Colab while investigating a dataset at the intersection of remote sensing, biology, and ecology. Students work with data in table format, map format, and PCA and k-means plots in the main lab assignment.
covid-wave-environmental-map-regression
This repo contains data and code for a web app designed for the visualization and analysis of geospatial data for the ENVS3 course, developed by the DIFUSE project (NSF IUSE-1917002).
difuse-research
This repository contains data from different research projects associated with the DIFUSE project (NSF IUSE 1917002).
ecology-eddy-covariance
Students explore and observe patterns from raw eddy covariance data and implications towards net ecosystem exchange. Students discover important meteorological and phenological properties that contribute towards the overall ecosystem.
engineering-airline-analysis
Students reinforce the introductory statistical concepts through the process of building a data analysis pipeline. Statistical concepts are explored to gain an understanding of the data, then used to implement three supervised machine learning models in MATLAB.
engineering-analyze-first-order-systems
Students examine the open loop response of a small motorized cart with a voltage applied to the motor in MATLAB.
engineering-statistics-in-R
Students learn basic functional R commands/procedures whilst tying in key statistical content. Repository for ENGS93 text files
Survey-of-Attitudes-towards-Data-Science
This repository contains all of the materials for the Survey of Attitudes towards Data Science, developed by the DIFUSE project (NSF IUSE-1917002).
wind-speed-power-analysis
Students engage with the wind energy power equations and explore other considerations in the siting of a wind farm in a 3-part Google Colab Python notebook.
earth-science-environmental-change
Students collect and analyze solar incidence angles over time to evaluate their own hypotheses, coupling this with the additional data analysis in Excel to draw conclusions about environmental change.
engineering-glucose-model-ode
Students find numerical solutions to a first order ordinary differential equation (ODE) model of glucose-insulin system using Euler’s method and least squares in MATLAB.
engineering-visualize-air-quality
Students model air quality dispersion using the “openair” package in R, analyze air quality datasets in Germany, and make recommendations based on their findings.
sociology-health-outcomes
Students examine the effect of different factors on self-rated health in Texas counties using interactive maps and regression analyses in Google Colab Python notebooks.