quay.io/bgruening/docker-jupyter-notebook:2021-03-05 8888 ipython/lab $__history_id__ $__galaxy_url__ 8080 $__galaxy_url__ Start with a fresh notebook Load a previous notebook

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Galaxy offers you to use Jupyter Notebooks directly in Galaxy accessing and interacting with Galaxy datasets as you like. A very common use-case is to do the heavy lifting and data reduction steps in Galaxy and the plotting and more `interactive` part on smaller datasets in Jupyter. You can start with a new Jupyter notebook from scratch or load an already existing one, e.g. from your colleague and execute it on your dataset. If you have a defined input dataset you can even execute a Jupyter notebook in a workflow, given that the notebook is writing the output back to the history. You can import data into the notebook via a predefined `get()` function and write results back to Galaxy with a `put()` function.