The Australian Community Reference Climate Data Collection @ NCI shapefile collection contains the following:
/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/aus_local_gov/
aus_local_gov.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/aus_states_territories/
aus_states_territories.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/australia/
australia.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/broadacre_regions/
broadacre_regions.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/NCRA_Marine_region/
ncra_marine_region.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/ncra_regions/
ncra_regions.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/NCRA_regions_coastal_waters_GDA94/
ncra_coastal_waters.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/nrm_regions/
nrm_regions.ipynb
for details/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/river_regions/
river_regions.ipynb
for detailsMost programming languages have libraries for reading in shapefiles and selecting geographic data points that fall within them.
If your workflow is based around Python and xarray, you can typically read a shapefile using geopandas. The resulting GeoDataFrame can then be passed to a function from the regionmask or clisops library to select grid points from an xarray data set that fall within the shape/s.
See python_tutorial.ipynb
for a worked example using regionmask and the shapefiles in this collection.
TODO.