Julia Wagemann Jupyter for Copernicus improve the use of Earth Observation data | JupyterCon 202

• Jupyter supports the uptake and use of Copernicus Earth observation data by providing a platform for working with data, handling data, data access, and coding.
• The Jupyter ecosystem is used to develop and provide executable training materials, such as notebooks, for Earth observation data scientists.
• The notebooks are hosted on GitLab and GitHub, and are accompanied by metadata that describe the content and purpose of each notebook.
• The notebooks are designed to be flexible and can be shared via JupyterHub, Colab, Binder, or Kaggle.
• The JupyterBooks platform provides a browsable version of all the available notebooks, with search functionality based on different application domains.
• The training materials are designed to cater to learners with different coding experiences and can range from short webinars to week-long intensive training schools.
• The Jupyter ecosystem is used to develop and provide thematic notebooks that focus on specific application domains, such as marine or dust monitoring.
• The JupyterBook platform provides a flexible way to share and collaborate on notebooks, and can be accessed via a website or a dedicated executable platform.
• The metadata schema allows for populating websites and provides a way to search and filter notebooks based on different criteria.
• The use of Jupyter and Copernicus data can help to grow the pool of Earth observation data scientists and promote the use of the data in different application domains.
• The Jupyter ecosystem is used to develop and provide a range of training materials, including notebooks, JupyterBooks, and executables, to support the use of Copernicus data.
• The Jupyter ecosystem provides a flexible and scalable way to manage and share large volumes of data, and can be used to develop custom applications and interfaces for different users and use cases.
• The JupyterBooks platform provides a way to browse and search for notebooks based on different criteria, and can be used to develop custom dashboards and interfaces for different users and use cases.

Note: This summary focuses on the main points related to the use of Jupyter and Copernicus data, and does not include all the specific details and examples mentioned in the talk.