Within a year of its launch date, the ATLAS altimeter on board ICESat-2 is already providing a wealth of critical data of interest across and beyond the cryospheric sciences. With the satellite returning nearly 1 TB of raw data per day, traditional practices of individual research groups downloading large granules of data and then subsetting, processing, and storing them locally are ultimately impractical. We are leading the development of icepyx (formerly icesat2py), an open source Python library designed to easily query, filter, download, and pre-process ICESat-2 datasets. The project’s documentation will include interactive Jupyter Notebook examples, providing a starting point for researchers to create and customize workflows to address their research questions. We actively invite contributions from the community to ensure the project develops in a way that meets a wide range of research needs. The project aims to leverage existing libraries that enable easy parallelization and can be run locally or on cloud-based platforms. As a result, researchers will ultimately download and store minimally-sized subsets of ICESat-2 data and have the opportunity to contribute their code to an established open science project. This presentation will serve to introduce icepyx to the cryosphere community and encourage early adoption of the library by researchers with all levels of coding experience.
Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: interactive computing, computational narratives, and the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in Earth and climate science.