comfort-db: A Python toolkit to access, preprocess and explore the oceanographic COMFORT dataset

Yvonne Jenniges, Are Olsen, Boris Peter Koch, Sebastian Maneth

comfort-db is a Python library that facilitates working with the COMFORT SQLite dataset, version 2. The dataset assembles global oceanographic measurements from ten datasets and contains a total of 458,724,734 in situ measurements from the years 1772 to 2020. comfort-db enables easy access to the database and provides preprocessing and exploratory analysis utilities. Preprocessing supports variable scaling and filtering by space, time and quality. Unit conversions can be applied during data loading or separately via a converter object. Gridding is supported and optionally works with bathymetry data to exclude grid cells on land. Moreover, the library supports general and field-specific exploratory data analysis through plotting functions, an interactive Dash app and functions to compute oceanographic profile and water mass statistics. comfort-db is open source (MIT license) and available from PyPI and GitHub.