Commit 81022807 authored by Maria Moreno's avatar Maria Moreno
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Update README to include information for users

parent 2385c6bf
# tutorials-and-use-cases
# Tutorials and use cases for data handlingg
Tutorials and use cases for data handling.
## Information for model data users
This project aims at providing short (5min) jupyternotebooks about all parts of data handling at dkrz.
Another focus is on highlighting use case notebooks that show the feasibility of simple analysis.
Welcome to the tutorials and use cases repository!
The notebooks must be
In the folder "notebooks" you can find Jupyter notebooks with coding examples showing how to use Big Data and High-Performance Computing software for applications in geoscience.
This Jupyter notebook is meant to run in the Jupyterhub server of the German Climate Computing Center [DKRZ](https://www.dkrz.de/) which is an [ESGF](https://esgf.llnl.gov/) repository that hosts 4 petabytes of [CMIP6](https://pcmdi.llnl.gov/CMIP6/) model data (more info on the data pool [here](https://www.dkrz.de/up/services/data-management/cmip-data-pool)).
Please, choose the Python 3 unstable kernel on the Kernel tab (upper tool bar in the Jupyterhub). This kernel contains all the common geoscience packages.
Besides the information of the Jupyterhub, in this [link](https://www.dkrz.de/up/systems/mistral/programming/jupyter-notebook) you can find how to run this Jupyter notebook in the DKRZ server out of the Jupyterhub, which will entail that you create the environment accounting for the required package dependencies. Running this Jupyter notebook in your premise, which is also known as [client-side](https://en.wikipedia.org/wiki/Client-side) computing, will also require that you install the necessary packages on you own but it will anyway fail because you will not have direct access to the data pool. Direct access to the data pool is one of the main benefits of the [server-side](https://en.wikipedia.org/wiki/Server-side) data-near computing we demonstrate in these tutorials and use cases.
## Information for notebook developers
This project aims at providing short (5min) jupyternotebooks about all parts of data handling at DKRZ. The focus is to show the feasibility of simple analysis.
Since this project is **public**, the notebooks must be:
- clear and
- maintained
since the project will be made **public** at the end.
We provide an environment.yml to make the repo portable.
Workflow:
Developers workflow:
1. Create Merge request to develop
2. Request for review from someone inside this group
3. Mark it as ready
4. The canban meeting decides if it is merged to master
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1. Create an issue in the Kanban board
2. Modify the repo in the develop branch of your fork
3. Request a merge from your forked developer branch to master
4. Assign a reviewer inside this group
5. Mark the issue as done in the Kanban
6. The stand-up meeting decides if it is merged to master and the issue is closed
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