Pipeline for semi-automated analysis of network-neuroscience literature

Requirements

• Jupyter Notebook running Python 3. https://docs.jupyter.org/en/latest/install.html
• Python packages and versions: python 3.11, notebook 6.5, ipywidgets 7.8, requests, html2text.
• Additional requirements for reproducing the full analysis:
  • Ability to download reference records from the Web of Science website.
  • Ability to download full-text articles from individual journal websites.

Setup via virtual environment

• The following commands establish a working virtual environment:

1. Using native Python (via requirements.txt):

cd /path/to/litrev
python3 -m venv litrev_env
source litrev_env/bin/activate
pip install -r requirements.txt

2. Using Anaconda (https://anaconda.org/):

conda create -n litrev_env python=3.11 notebook=6.5 ipywidgets=7.8 anaconda::requests conda-forge::html2text
conda activate litrev_env

Summary

• Step 1: Environment set up and loading of previously analyzed data.
• Step 2: Specification of the full literature search query and instructions for manually downloading all reference records that match this query from the Web of Science.
• Step 3: Automated download of all full-text articles that match the specified search query.
• Step 4: Automated curation and cleaning of article text for all downloaded articles.
• Step 5: Automated extraction of relevant text segments and emphasis of potential keywords.
• Step 6: Automated scoring of the presence or absence of circular analyses based on manual evaluation of specified criteria.
• Step 7: Automated storage of collated evaluations and scores in a simple database and a summary table.

Execution

• Launch the Jupyter Notebook environment.
• Open the file analysis.ipynb file in the Jupyter Notebook environment.
• Select the Run All option from the Cell menu.
• Scroll to Step 6 to interact with article text and evaluations:
  • All articles, except for Article 1, are sorted in pseudorandom order.
  • Use the Previous article and Next article buttons to navigate between articles.
  • Alternatively, enter the article number directly into the Article field.
  • Adjust the viewing window height using the Text-window height slider.
  • The Methods and Results tab (default selection) shows curated article text:
    • All potential keywords in the article text are highlighted in pink.
    • All paragraphs that contain potential keywords are highlighted in yellow.
    • The article accession number and abstract are highlighted in gray.
  • The Evaluation tab shows the scoring and evaluation for each article:
    • Condition 1 determines the suitability of the article for evaluation.
    • Conditions 2–3 determine the presence or absence of circular analyses.
    • Condition 4 evaluates the presence of circular analyses from Conditions 1–3.
• The collated evaluations and scores are stored in the ./results folder:
  • The file ./results/summary.csv contains a summary table with article accession numbers and evaluations.
  • The file ./results/results.zip/records.json contains the database of curated article data and evaluations.
  • All other files in ./results/results.zip and in ./results/eqns are images of article equations (used for displaying equations alongside article text).