⚠️ PROTOTYPE: This Jupyter Book is a work in progress and is currently in a prototype stage.
Access the current published version here: NC-UK Environmental Data Science Toolbox 🌱
Get Involved | Contributing a Method | Interactivity | Table of Content | Discussions | Feedback
This repository houses the NC-UK Jupyter Book that aligns with the NC-UK task of providing a suite of open-source, adaptable analytical methods for the academic community. The concept is to create a collection of user-friendly notebooks demonstrating sophisticated data science methods developed at UKCEH and external contributing organisations. These methods are expected to be generalizable across different areas of application and to have a strong focus on integrative modelling. This resource will enhance collaborative use of data science methods across different environmental themes and will support the UK's national capability in delivering world-leading environmental science. The book is currently in a prototyping stage with methodologies/notebooks expected to evolve over time.
If you would like to cite the Environmental Data Science Jupyter Book, please use the "Cite this repository" button on this repository's landing page in the right sidebar. If you would like to cite an individual method's Jupyter Notebook, click on the "Notebook Repository" button at the top of the notebook and then use the "Cite this repository" button on that repository's landing page. See here for more information on how GitHub generates these citation buttons from CITATION.cff files.
Inspired by the Environmental Data Science book produced by The Alan Turing Institute: EDS book community. Environmental Data Science book (Version v2025.7.1) [Computer software]
Logo Credits: The current Jupyter book logo was generated using OpenAI.
Accessibility Statement: This Jupyter Book is being developed with accessibility in mind, following guidelines from the UKCEH accessibility statement. We aim to ensure that all content is accessible to a wide range of users, including those with disabilities. If you encounter any accessibility issues or have suggestions for improvement, please let us know through the GitHub Discussions.
Funding Statement: This research was supported by NERC, through the UKCEH National Capability for UK Challenges Programme NE/Y006208/1