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more content for institutional faq
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@@ -20,14 +20,14 @@ Here is a quick breakdown of these three tools:
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* **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
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narrative text with code cells and their outputs. It is also a graphical interface
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that allows users to edit these documents
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that allows users to edit these documents. There are also several other graphical interfaces
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that allow users to edit the `.ipynb` format (nteract, Jupyer Lab, Google Colab, Kaggle, etc).
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* **JupyterLab** is a flexible and extendible user interface for interactive computing. It
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has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
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for other parts of the data science stack.
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* **JupyterHub** is an application that can manage **multiple users** with interactive computing
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sessions, as well as connect with infrastructure those users wish to access. It can provide
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remote access to Jupyter Notebooks and Jupyter Lab for many people, and can connect them with
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other compute infrastructure.
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* **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
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It also connects them with infrastructure those users wish to access. It can provide
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remote access to Jupyter Notebooks and Jupyter Lab for many people.
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# For management
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@@ -36,7 +36,7 @@ Here is a quick breakdown of these three tools:
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JupyterHub provides a shared platform for data science and collaboration.
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It allows users to utilize familiar data science workflows (such as the scientific python stack,
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the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
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some control over access to resources, security, authentication, and user identity.
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some control over access to resources, security, environments, and authentication.
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## Is JupyterHub mature? Why should we trust it?
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@@ -57,17 +57,18 @@ industry, and governmental research labs. It is most-commonly used by two kinds
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## How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
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Like the tools listed above, JupyterHub provides access to interactive computing
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environments in the cloud. However, JupyterHub is more flexible, more customizable,
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free, and gives administrators more control over their setup and hardware.
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JupyterHub puts you in control of your data, infrastructure, and coding environment.
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In addition, it is vendor neutral, which reduces lock-in to a particular vendor or service.
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JupyterHub provides access to interactive computing environments in the cloud (similar to each of these services).
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Compared with the tools above, it is more flexible, more customizable, free, and
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gives administrators more control over their setup and hardware.
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Because JupyterHub is an open-source, community-driven tool, it can be extended and
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modified to fit an institution's needs. It plays nicely with the open source data science
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stack, and can serve a variety of computing enviroments, user interfaces, and
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computational hardware.
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Finally, JupyterHub can be deployed anywhere - on enterprise cloud infrastructure, on
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High-Performance-Computing machines, on local hardware, or even on a single laptop.
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computational hardware. It can also be deployed anywhere - on enterprise cloud infrastructure, on
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High-Performance-Computing machines, on local hardware, or even on a single laptop, which
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is not possible with most other tools for shared interactive computing.
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# For IT
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@@ -108,6 +109,11 @@ The short answer: yes. JupyterHub as a standalone application has been battle-te
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level for several years, and makes a number of "default" security decisions that are reasonable for most
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users.
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* For security considerations in the base JupyterHub application,
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[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html)
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* For security considerations when deploying JupyterHub on Kubernetes, see the
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[JupyterHub on Kubernetes security page](https://zero-to-jupyterhub.readthedocs.io/en/latest/security.html).
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The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
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in a variety of deployment setups. This often entails connecting your JupyterHub to **other** infrastructure
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(such as a [Dask Gateway service](https://gateway.dask.org/)). There are many security decisions to be made
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