1. About
2. Quilted Tutorials
3. Dependencies
4. Getting started
5. License
6. Team
7. Acknowledgments

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is supported by the NIH Common Fund to provide a scientific and technological foundation for future bioelectronic medicine devices and protocols. The initiative is made up of over 60 research teams scattered around the globe, all working together on a common objective. The entire project is Open Source and follows the Findable, Accessible, Interoperable, and Reusable (FAIR) guidelines for data management.

In 2021, the amazing team of the Data and Resource Center (DRC) organised a Codeathon to improve various elements of the SPARC program. In 2022, they've done it again!

In our project, we have designed an online hub and populated it with tutorials to showcase how tools within the SPARC ecosystem can be combined to achieve unified workflows. Each tutorial is contained within an individual Jupyter Notebook. The website is made from Hugo and hosted on Github Pages from the SPARC-guru repository of the Quilted Tutorials organisation.

The first Quilted Tutorial is designed to guide users on how to project 2D spatial data sets onto a 3D organ scaffold using tools within the SPARC ecosystem. The objective for this tutorial is to project the spatial locations of 3 different types of neurites on the surface of the rat stomach onto a 3D scaffold of the organ. The experimental data and 3D scaffold are downloaded from the SPARC portal and SPARC Scaffold Mapping Tools, respectively. Below is a figure of the workflow for the tutorial: the experimental data is acquired using Pennsieve and piped into a Jupyter Notebook where the 2D data is mapped to the 3D organ scaffold and visualised.

The second Quilted Tutorial is designed to guide the users on how to modify data points for other applications. The objective of this tutorial is to resample some 2D data points for future simulations. Below is a figure of the workflow for the tutorial: the experimental data is acquired using Pennsieve and piped into a Jupyter Notebook where the 2D data is processed before being visualised.

The tutorial has been tested using Ubuntu 22.04 LTS and Python 3.10.4

Here is the list of all the dependencies that are needed to run this tutorial. These are all the dependencies needed to run both tutorials. The list of dependencies required for each individual tutorial can be found in the requirements.txt files inside their folders.

• pandas, version 1.4.3
• openpyxl, version 3.0.10
• numpy, version 1.23.0
• numpy-stl, version 2.17.1
• matplotlib, version 3.5.2
• ipympl, version 0.9.1
• jupyterlab, version 3.4.4
• ipywidgets, version 7.7.1
• tqdm, version 4.64.0

Start by cloning the GitHub repository onto your local machine into the SPARC-tutorial folder with the following command:

$ git clone [email protected]:SPARC-FAIR-Codeathon/SPARC-Tutorial.git SPARC-tutorial

The tutorial is contained in a Jupyter Notebook and requires JupyterLab to run.

Open JupyterLab and navigate to the SPARC-tutorial folder. Select the tutorial you want to try and enter the corresponding folder. Open the SPARC-tutorial.ipynb file to begin. You can see which folder corresponds to which tutorial by checking out the available tutorials here.

Not sure if JupyterLab is installed on your machine? No worries, we've got you covered! Check out if it is installed on your machine using this command in a terminal:

$ which jupyter-lab

If there is no output, install JupyterLab with the following command:

$ pip install jupyterlab

Once this is done or if you already have JupyterLab installed on your machine simply navigate into the cloned directory and run JupyterLab:

$ cd SPARC-tutorial && jupyter-lab --LabApp.token=''

From your browser, you can try out a tutorial by navigating to the folder and opening the notebook by clicking on SPARC-tutorial.ipynb and follow the tutorial from there. You can see which folder corresponds to which tutorial by checking out the available tutorials here.

This tutorial is available under the MIT License, thereby making it fully accessible to anyone.

• Yuda Munarko (Team leader)
• Omkar Athavale (Tutorial developer)
• Mathias Roesler (Writer and Web developer)
• Niloofar Shahidi (Writer)
• Kenneth Tran (Writer and Tutorial developer)

We would like to thank the organizers of the 2022 SPARC FAIR Codeathon as well as the SPARC DRC teams for their guidance and help during this Codeathon. Find out more about them in the About section!