BEFANA: A Tool for Biodiversity-Ecosystem Functioning Assessment by Network Analysis

Interactive Jupyter notebooks and python code for soil food web visualization and analysis. Please refer to our paper in pre-print for further details: https://arxiv.org/abs/2203.11687.

Tool structure:

BEFANA consists of these jupyter notebooks:

• Part 1: Preparing the environment and loading the data
• Part 2: Network construction, editing and visualization
• Part 3: Network analysis
• Part 4: Modeling with experimental data
• Part 5: Machine learning on graphs

How to use

MyBinder

The easiest way to run BEFANA is through a public web interface at mybinder.org: https://mybinder.org/v2/gh/MartinMarzi/BEFANA/HEAD

Click on the link above, wait that the repository loads, and run the Jupyter notebooks in order 1-5.

Docker

The notebooks work best in a local installation containing Jupyter lab and other required packages. If you prefer docker or if you experience difficulties running the notebooks on your host operating system, you can try using the provided docker-compose.yml file as follows:

git clone git@github.com:vpodpecan/representation_learning.git
cd representation_learning
docker-compose up

When the container is up and running it will return a link to the Jupyter environment such as http://127.0.0.1:8888/?token=159090399d58b41041bfc812cf2bf5aa1779fb54a6170005. There you can open and run the provided notebooks.

Local installation

Requirements

• python 3.8+
• jupyterlab

In addition, each notebook has its own requirements which are installed when the notebook is executed for the first time.

Preparing the environment

1. Create and activate a virtual environment.

   • Linux

     python3 -m venv myEnv
     source myEnv/bin/activate

   • Windows

     python3 -m venv myEnv
     myEnv\Scripts\activate

2. Clone the repository

   git clone https://github.com/vpodpecan/representation_learning.git

3. Install and run jupyterlab. The following commands install jupyterlab and run it.

   pip install jupyterlab
   cd representation_learning
   jupyter lab

4. Open the link in a web browser and select a notebook.

How to contribute

Contributions are welcome! You are welcome to contribute corrections, new notebooks, examples, figures or any other material related to the contents of the book.

License

The code and materials in this repository are licensed under the MIT license except where stated otherwise.