The OOI strives to make data accessible and usable so scientists can easily apply it to their research questions and teachers can integrate it into their classroom instructions. Two ways in which we do this is to use popular programming languages like Python and tools like Jupyter Notebooks, both of which are widely used in the research community. Python excels at scalability, making it possible for scientists to approach problems in many different ways. Jupyter Notebooks, an open-source web application, allows users to create and share documents containing live code, equations, visualizations, and other text, which help expand understanding of data and its potential applications.

The following are some ways Python is applied to OOI data and examples of Jupyter Notebooks using OOI data. Other programming languages and tools can be found here.

Python Quick Start Python Access to OOI Data OOI High Definition video camera system Python module Broadband Hydrophone Python module