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Let's add a link to the README.md file so that people can launch the repo on the campus datahub.
In case there are problems with a participant's local installation, using the campus datahub is a great alternative for the workshop. For UCB-only based trainings, the datahub which uses CalNet authentication is a better option than using Binder. The campus datahub provides long-term persistent storage, whereas the Binder storage gets deleted after an hour of inactivity.
Using the nbgitpuller link generator it's possible to pull any arbitrary (non-private!) github repo into datahub, such as with this link that I generated:
@Akesari12 this would (partly) address the local install issue that came up today so that people can participate even if their local install is broken.
Thanks, this is a good idea! @aculich , is there a similar solution we can use for R? Next semester we will transition to R for causal inference so I wonder if we can get ahead of any potential problems there.
Is it possible to document a quick start guide for novice users to add a repo to their datahub? The documentation is quite intense for the beginner. Unless one knows that you have to first visit nbgitpuller and know what a JupyterHub URL is then there is the risk that students will be frustrated with yet another tool that is incredibly powerful but not clear how to use.
Let's add a link to the README.md file so that people can launch the repo on the campus datahub.
In case there are problems with a participant's local installation, using the campus datahub is a great alternative for the workshop. For UCB-only based trainings, the datahub which uses CalNet authentication is a better option than using Binder. The campus datahub provides long-term persistent storage, whereas the Binder storage gets deleted after an hour of inactivity.
Using the nbgitpuller link generator it's possible to pull any arbitrary (non-private!) github repo into datahub, such as with this link that I generated:
https://datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FComputational-Social-Science-Training-Program&urlpath=tree%2FComputational-Social-Science-Training-Program%2F&branch=master
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