This repository was created to help scientists analyze data from the scientific literature

It contains a 3 notebook series where the main goal was to review the scientific bibliography related to the mathematical modelling of biotechnological wastewater treatment processes (WWTP) through the lattice Boltzmann method.

Through a literature screening, we found 72 papers reporting this issue and, in order to track the main authors and keywords of these studies, it was created the first notebook (Part 1).

In the second notebook (Part 2), we performed an analysis of the scientific studies. To do it, it was created a dataset containing the main information extracted from the papers assessed and our analysis is based on it!

The third notebook shows some statistical insights comparing the studies reported in the literature and their respective references for model validation.These results can be observed in Part 2 of this series, in the column 'Normalized error for model validation (%)' from our dataset. Data presented here were extracted from original papers by using the tool WebPlotDigitalizer. The analysis performed was: Root mean squared error, Pearson's correlation and Normalized error.

We are updating the repo, in order to automate the paper's search task and select the ones that match with the characteristics of the study

My special thanks to José Pedro by the contribution to this project.

To run, follow the instruction bellow:

1) Creating a virtual environment for python3

• python3 -m pip install --upgrade pip (upgrading pip)

• pip3 install virtualenv(installing virtualenv)

• virtualenv venv (naming virtualenv)

• source venv/bin/activate (activating virtualenv)

2) Jupyter Notebook

• python3 -V (cheking python version)

• pip3 install jupyter notebook (intalling)

• jupyter notebook (running)

• deactivate ( to exit)

3) Installing libraries

• pip3 install pandas

• pip3 install wordcloud

• pip3 install scikit-learn

• pip3 install scipy

• pip3 install maths

• pip3 install seaborn

• pip3 install matplotlib

• pip3 install pylab-sdk

    				*This study is open to contribution!*