Gallery of Jupyter Books

This book provides an introduction for freshman students into the world of classical mechanics and special relativity theory

NumPy tutorials & educational content in notebook format

A comprehensive guide to applying artificial intelligence to geospatial data, including remote sensing, deep learning, and foundation models for Earth observation.

A Jupyter Book presenting an exploratory and educational computational workflow to study protein–peptide molecular mimicry in the HLA-B27 protein, which is associated with autoimmune diseases such as ankylosing spondylitis. The book covers peptide screening, docking, molecular dynamics simulations, trajectory correction, and post-MD analysis, with an emphasis on methodological understanding and hands-on learning.

A practical guide to managing and analyzing large-scale spatial data using DuckDB, focusing on efficient SQL workflows and modern geospatial analytics.

JupyterHub’s impact on the world deserves to be celebrated. This report aims to celebrate these accomplishments. These conversations have also highlighted areas for improvement, from those that support better software to those that encourage a healthier, more vibrant community.

The Turing Way is an open science, open collaboration, and community-driven project. We involve and support a diverse community of contributors to make data science accessible, comprehensible and effective for everyone.

This is the online textbook for BDI 475 - Introduction to Data Analytics Applications in Business, a course offered by the University of Illinois Urbana-Champaign. The course is designed to introduce students to the fundamentals of data analytics and its applications in business contexts.

The official website of the University of California Network of Open Source Program Offices (OSPOs).

Computational mechanics demos of dolfiny, an extension module of the finite element library FEniCSx, rendered as jupyter book.

An extensive graduate course on topology in condensed matter physics

The guide for the PyRIT open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.

The Fornax Initiative is a NASA Astrophysics project developed by the HEASARC, IRSA, and MAST archives to bring data, software, and computing together on the cloud so that researchers can focus on science. This repository contains Python notebook tutorials demonstrating a range of topics, including data access on-premises and on the cloud, cross-archive analysis, big data workflows, and computationally intensive science.

Project Pythia is the education working group for Pangeo and is an educational resource for the entire geoscience community. Together these initiatives are helping geoscientists make sense of huge volumes of numerical scientific data using tools that facilitate open, reproducible science, and building a community of practice around these goals.

An introduction to linear algebra in the context of machine learning, with some statistics, calculus, and Python sprinkled in.

Gain a deep understanding of the methodologies and specialized tools used to conduct professional-grade security validation for Python applications.

A hands-on introduction to GIS programming with Python, covering core concepts, geospatial data processing, and interactive mapping using open-source tools.

The Fornax Initiative is a NASA Astrophysics project developed by the HEASARC, IRSA, and MAST archives to bring data, software, and computing together on the cloud so that researchers can focus on science. This repository holds the source for the user documentation and therefore is an example of a non-notebook based narrative documentation project.

These tutorials demonstrate access methods and techniques for working with data served by the [NASA/IPAC Infrared Science Archive (IRSA)](https://irsa.ipac.caltech.edu/). They cover topics like querying IRSA, working with catalogs in Parquet format, visualizing with Firefly, and general other techniques.

An interactive textbook exploring probability theory through practical Python programming. Covers foundations, conditional probability, random variables, distributions, limit theorems, and advanced topics like Bayesian inference and Markov chains. Designed for students, data scientists, and developers using Jupyter notebooks with hands-on examples and exercises.

A GitHub Template repository designed for use in Jupyter Book 2 and MyST workshops.

A Jupyter Book v2 (MyST) template with editable, syntax-highlighted Python code cells powered by JupyterLite (Pyodide), enabling client-side execution in the browser without a backend, including offline use after initial load.