Assignment 2: Explore a climate dataset

At-home assignment — worth 10 points. When you’re done, push your work to your week folder and post a link to that folder on the matching Courseworks assignment.

Pick any climate-related dataset that interests you — could be something you’re considering for your final project, or just something you’re curious about. The point here is practice with the FAIR + access workflow, not a commitment to a specific project.

Learning goals

This assignment exercises the data-discovery and access skills from this section:

• Locate a dataset in a public scientific repository (Zenodo, NOAA, NASA, IRI, etc.)

• Document a dataset’s format, license, provenance, and FAIR-ness

• Load data into Python using pandas.read_csv, xarray.open_dataset, or pooch.retrieve

• Submit work via the week-folder pattern in your clmt5405-assignments repo

Prerequisites

• You’ve worked through Formats and metadata and Loading data.

• You have your clmt5405-assignments repo set up from Assignment 1, and can push to it.

What to do

Throughout the steps below, replace <weekN> with the current week’s folder name (e.g., week1, week2, etc).

1. Find a dataset. Look on Zenodo, a NOAA or NASA portal, the IRI Data Library, or anywhere else scientific data lives. Pick something with a clear source page that you can document.

2. Create a <weekN> folder inside your clmt5405-assignments repo. (On Colab, first git clone your repo down if you don’t already have it locally.)

   mkdir <weekN>
   cd <weekN>
   

3. Write dataset.md inside <weekN>/ describing:

   • What it is — one sentence about the dataset (variable, region, time period).

   • Where it lives — the URL or DOI.

   • Format — CSV? NetCDF? Zarr? GeoTIFF?

   • License — what does the source page say? (CC-BY, public domain, “no restrictions,” etc.)

   • Provenance — who created it?

   • FAIR check — does it have a persistent identifier? Is the metadata clear? Is the format machine-readable? Is the license open?

4. Create dataset_load.ipynb in the same <weekN>/ folder. Add one code cell that loads the dataset using whichever of the three patterns from the loading lecture fits its format (pandas.read_csv, xarray.open_dataset, or pooch.retrieve). Run it — you should see something (a DataFrame, a Dataset, or a file path). No analysis needed; this just confirms the access works.

5. Commit and push:

   cd ..
   git status
   git add <weekN>/dataset.md <weekN>/dataset_load.ipynb
   git commit -m "Add <weekN> assignment"
   git push
   

Refresh your repo page on GitHub and confirm both files appear in <weekN>/.