All notebooks described in this section are available in R and Python, you will find them in the R_based_scripts and the Python_based_scripts folders, respectively.
- AIMS temperature loggers: Available as a Quarto notebook for
Rusers, and as a Jupyter notebook forPythonusers. In this example, we will connect to the RIMReP geoparquet collection in our AWS S3 bucket to access the AIMS temperature logger dataset, which contains over 150 million records! We will calculate the mean latitude, longitude, and temperature, and total number of temperature records for all deployment sites around the Great Barrier Reef Marine Protected Area using familiar dplyr verbs. Finally, we aggregated all data at a site level to create a map where the site marker change in size in proportion to the number of records at each site. - Extracting water temperature at site: Available as a Quarto notebook for
Rusers, and as a Jupyter notebook forPythonusers. This notebook calculates monthly temperature means for any sites of interest included in the AIMS Sea Surface Temperature Monitoring Program. Data summaries and plots saved in local machine. - Extracting spatial data GBR: Available as a Quarto notebook for
Rusers, and as a Jupyter notebook forPythonusers. This notebook extracts spatial data for all above water features within the Great Barrier Reef Marine Protected Area. - Extracting water temperature within GBR feature boundaries: Available as a Quarto notebook and as a Jupyter notebook: This notebook will identify AIMS water temperature monitoring sites within a GBR feature and calculate monthly means.
- Plotting ABS census data: Available as a Quarto notebook and as a Jupyter notebook: This notebooks uses Australian Bureau of Statistics (ABS) census data to create summary tables, bar plots and maps.
- Plotting SELTMP data: Available as a Quarto notebook and as a Jupyter notebook: This notebooks uses the the Social and Economic Long-Term Monitoring Program (SELTMP) dataset from CSIRO to create a variety of plots available in their dashboard.
Additionally, there are two more notebooks available exclusively for Python users:
- Geoparquet example (using AIMS Temperature Loggers data)
- Zarr example (using NOAA Coral Reef Watch degree heating weeks data)
And one notebook available exclusively for
Rusers: - NOAA Degree Heating Weeks (DHW) data. This notebooks gets the DHW gridded data from NOAA Coral Reef Watch via the RIMReP API and plots it.
Note: You will notice that there are two files with the same name, but two different extensions: .md and .Rmd. They contain the same information, but in different formats. The .Rmd file is the source code of the notebook, which you can open in RStudio and run. While the .md file is the output of the .Rmd file and they include the results of running the code. If you click on the notebook links above, it will take you to the .md files, which are nicely formatted for GitHub.
All scripts described in this section are available in R and Python, you will find them in the R_based_scripts and the Python_based_scripts folders, respectively.