Codeathon 2024 (#71)

* codeathon 2024 quick guide

* add jupyterlab docs for codeathon

docs/codeathon-2024.md

@@ -0,0 +1,46 @@
+# 2024 SPARC FAIR Codeathon Quick Start Guide
+
+![codeathon](https://images.ctfassets.net/6bya4tyw8399/2qgsOmFnm7wYIfRrPrqbgx/ae3255858aa12bfcebb52e95c7cacffe/codeathon-graphic.png)
+
+For participants of the [**FAIR Codeathon**](https://sparc.science/news-and-events/events/2023sparc-codeathon) August 10-12 2024, we have compiled the parts of the [full documentation](https://docs.osparc.io/#/) that are likely to be most useful to you. As a very brief introduction.
+
+o²S²PARC is a cloud-based computational platform for running code that is contained in various modules, called [**Services**](../docs/platform_introduction/services.md). These Services can be chained together which allows for information transfer (see [**Connecting Services**](../docs/study_setup/connecting_services.md)) from one Service to another\*. A project on **o²S²PARC**, which contains these chained pipelines is called a [**Study**](../docs/platform_introduction/studies.md).
+
+*Support for numerical, boolean, arrays, enums (e.g. categorical choices), string and file (any extension) data types is currently available.
+### Using Services
+Services come in generally two flavors: computational or interactive. Computational Services such as a [**Python Runner**](../docs/tutorials/python_runner.md) will allow setting of some parameters (e.g. input files) and will perform a computation with no user interaction required. Interactive Services only perform tasks with user interaction, as is the case for Services such as the [**JupyterLab**](../docs/study_setup/JupyterLabs.md). JupyterLab Services will allow you to write and execute code within the JupyterLab GUI, but as with all interactive Services, will not run automatically when a Study pipeline is [**launched**](../docs/study_setup/run_simulation.md). Computational Services, on the other hand, will automatically read all input parameters at runtime and execute the static code embedded inside them. 
+
+### Collaborating With Your Team
+o²S²PARC supports sharing and duplication of Studies so that you may share projects with your team members. The typical steps are:
+1. Check that all the team members have an o²S²PARC account (let us know ASAP who needs an account)
+2. Create an [**Organization**](../docs/study_setup/sharestudy?id=organization) and add all the members of your team
+
+If you want to collaborate **on the same instance of a Study**, you may then [**share**](../docs/study_setup/sharestudy?id=sharing-a-study) it, though you should note that **only one team member can access a Study at a time** to avoid conflicting changes. 
+
+When you are happy with your Study and you'd like to share with all the o²S²PARC users (including the judges), please publish your Study as a [**Template**](../docs/study_setup/sharestudy?id=publishing-a-study-as-a-template).
+
+### Bring your Data and Code into **o²S²PARC** 
+
+#### Add Local Files
+You can import any type of file in o²S²PARC using the [**File Picker Service**](../docs/study_setup/loading_data/loading_data.md) or Drag and Drop the file directly.
+
+#### JupyterLabs
+We have services where you can use [**JupyterLab**](../docs/study_setup/JupyterLabs.md) with different kernels (e.g. Python, Octave, C++, R, Julia) and git integration. You can also customise your environment by installing custom packages.
+
+*Hint*: for the purpose of the Codeathon, if you are developing Python (or Octave) code, the most useful service is called "JupyterLab Math (Python+Octave)"
+
+#### Custom Python Code as a Computational Service
+If you prefer to have your Python code as a Computational Service (e.g. if you want to build a pipeline that doesn't require user interaction), you can use the o²S²PARC [**Python Runner**](../docs/tutorials/python_runner.md).
+
+#### Create A New Service With Custom Code
+If you have other custom code, it needs to be containerized (using Docker) to create a new Computational Service, we have a [template library (cookiecutter)](https://github.com/ITISFoundation/cookiecutter-osparc-service) that can help you. If you need to create a new Service, please let us know ASAP through email or Slack to determine if we are able to meet Codeathon deadlines. Most probably, you can start by using the cookiecutter and sharing your prototype code on Github, that will be turned into a Service later on.
+
+#### API
+Extensive information about the o²S²PARC API is documented [here](https://itisfoundation.github.io/osparc-simcore-clients/#/) and it was created to allow for running of pipelines through a Python client. In particular, the [**Basic Tutorial**](https://itisfoundation.github.io/osparc-simcore-clients/#/clients/python/artifacts/docs/BasicTutorial_v0.6.0) is a great place to start! Certain issues to keep in mind, however, are that:
+1. The API can be used to set parameters for, run and export the outputs of only Computational Services, not interactive ones
+2. The API can run full Studies containing Computational Services, but this feature is not documented yet. If you wish to use this feature, please let us know, so we can help you.
+
+
+
+#### Best of luck to all participants!
+...and don't hesitate to reach out on Slack or e-mail if you have questions/problems.

docs/study_setup/JupyterLabs.md

@@ -11,11 +11,21 @@ For a full demonstration of these functionalities, take a look at our webinar on
 This Service gives you a choice of creating notebooks with either Octave or Python. The ```Python (maths)``` kernel has preinstalled modules for mathematical modeling and data analysis (see details in [this *requirements* file](https://github.com/ITISFoundation/jupyter-math/blob/main/kernels/python-maths/requirements.txt)).
 
 ## Jupyter NEURON
-This Service is very similar to the Jupyter Math Service, but also includes NEURON-based packages by default, including NEURON, NetPyNE and Brian2, PyNN for the Python kernel. See details in [this *requirements* file](https://github.com/ITISFoundation/jupyter-neuron/blob/main/requirements.in)).
+This Service is very similar to the Jupyter Math Service, but also includes NEURON-based packages by default, including NEURON, NetPyNE and Brian2, PyNN for the Python kernel. See details in [this *requirements* file](https://github.com/ITISFoundation/jupyter-neuron/blob/main/requirements.in).
 
 ## JupyterLab Chaste
 This Service is another JupyterLab environment with C++ kernels pre-installed as well as the [Chaste](https://www.cs.ox.ac.uk/chaste/) modeling framework. The C++ kernels, contributed by [xeus-cling](https://xeus-cling.readthedocs.io/en/latest/) allows for interactive scripting with C++.
 
+## JupyterLab R
+This Service is another JupyterLab coding environment for creating interactive Jupyter Notebooks and scripts with R (or Python). Some common packages are already installed, see details in [this *requirements* file](https://github.com/ITISFoundation/jupyterlab-r/blob/main/env-config/r/requirements.txt)
+
+## JupyterLab Julia
+This Service is another JupyterLab coding environment for creating interactive Jupyter Notebooks and scripts with Julia (or Python). Some common packages are already installed, see details in [this *Project.toml* file](https://github.com/ITISFoundation/jupyterlab-julia/blob/main/env-config/julia/Project.toml)
+
+## Other JupyterLabs
+We have many more JupyterLabs available, e.g. generic ones for machine learning applications, containing PyTorch and TensorFlow, or for more specific use-cases, e.g. medical image processing. Just search for "jupyterlab" on the platform to see what is available.
+![image](https://github.com/user-attachments/assets/0fc95bff-528a-4128-a693-84e8474505d0)
+
 ## Switching Kernels within a Notebook
 You may even change the kernel of a notebook from Python to Octave or vice versa in the middle of a notebook, but note that variables are not shared across kernels. To change the kernel associated to a given notebook, double click the notebook in the file tree on the left to ensure it is being actively viewed in the main window. Then, select "Kernel" from the menu at the top of the page and choose "Change Kernel...". This will open a window with a dropdown menu containing alternative kernel options.  Note that changing the kernel within a single notebook does NOT copy the predefined variables.