Presentation slides:
g.co/earth/esip2018-widgetsFor many scientific questions in the Earth sciences, the sheer volume of observed and/or modeled data is a barrier to progress, as it is difficult to explore and analyze using the traditional paradigm of downloading datasets to a local computer for analysis. Furthermore, methods for communicating Earth science algorithms that operate on large datasets in an easily understandable and reproducible way are needed. The Jupyter project has created several tools for general data science that can be leverage for exploratory data analysis of tera- to peta-byte scale geospatial data datasets.
This session will be a hands-on introduction to:
- JupyterLab (the Jupyter project's next-generation UI)
- Jupyter Widgets (the interconnection between the UI and a Python kernel)
- Earth Engine (Google's cloud-based geospatial analysis API)
- Examples of satellite data exploration and analysis
In addition, we will be using the following technologies:
- JupyterHub for hosting the multi-user environment
- Docker for packaging up JupyterLab, the Earth Engine Python API, and dozens of scientific Python packages
- GitHub for sharing all of the session content
Learn more about Jupyter and attend the other workshops using ESIPhub:
* Tuesday morning includes a general overview of Jupyter usage in our community.
http://sched.co/Eype* Just after the overview session is a Metadata Improvement Lab focused on schema.org for datasets.
http://sched.co/Eypl* Wednesday afternoon is a workshop for cloud-based analysis.
http://sched.co/EyqK
