Overview#
This section is about data — the formats and conventions you’ll meet when working with climate and environmental datasets, and the practical ways to get data into Python.
Learning objectives#
By the end of this section, you should be able to:
Recognize common data formats in climate science — tabular (CSV, Parquet), gridded (NetCDF, Zarr), and raster (GeoTIFF).
Read metadata and understand why it’s essential to a dataset’s usefulness.
Explain the FAIR principles (Findable, Accessible, Interoperable, Reusable) and why they matter for scientific data.
Access data from different sources — over HTTP, via OPeNDAP, and via persistent identifiers (DOIs) with Pooch.
Find a DOI for a dataset and use it to retrieve the data reproducibly.
Pages in this section#
Formats and metadata — what kinds of data exist, how they’re described, and what FAIR means.
Loading data — practical try-its for the main access patterns.
Assignment 2 — explore and document a climate dataset of your choice.