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Software and Data

All coursework will be completed using jupyter notebooks running on an external HPC system.

Python Packages we will use in this course

  • geopandas for vector spatial data.
  • rasterio, rioxarray, and xarray for raster data.
  • pandas, numpy, and scikit-learn for data processing and modeling.
  • matplotlib, seaborn, or plotnine for figures.
  • conda, mamba, uv, or venv for environment management.

Spatial Biodiversity Data Sources

  • GBIF occurrence records.
  • iNaturalist research-grade observations.
  • VertNet or other taxon-specific occurrence repositories.
  • WorldClim, CHELSA, PRISM, or ERA5 climate data.
  • MODIS, Landsat, Sentinel, or derived remote sensing products.
  • Soil, terrain, hydrology, land cover, and protected area datasets.
  • OpenTree, VertLife, Fish Tree of Life, or clade-specific phylogenies.
  • TRY, BIEN, GIFT, or other trait and range databases.