sortgs is a Python tool for ranking Google Scholar publications by the number of citations. It is useful for finding relevant papers in a specific field. The data acquired from Google Scholar includes Title, Citations, Links, Rank, and a new column with the number of citations per year. In the background, it first try to fetch results using python requests. If it fails, it will use selenium to fetch the results.

• No install requirements! Limitations: Can't handle robot checking, so use it carefully.

You can now install sortgs directly using pip:

pip install sortgs

This will install the latest version of sortgs and its dependencies.

Once installed, you can run sortgs directly from the command line:

sortgs "your keyword"

Replace "your keyword" with any keyword you'd like to search for. A CSV file with the name your_keyword.csv will be created in your current directory.

If this project was helpful to you in any way, feel free to buy me a cup of coffee :)

For a feedback, send me an email: fernando [dot] wittmann [at] gmail [dot] com

usage: sortgs [-h] [--sortby SORTBY] [--nresults NRESULTS] [--csvpath CSVPATH]
              [--notsavecsv] [--plotresults] [--startyear STARTYEAR]
              [--endyear ENDYEAR] [--debug] kw

positional arguments:
  kw                    Keyword to be searched. Use double quote followed by
                        simple quote for an exact keyword. 
                        Example: sortgs "'exact keyword'"

optional arguments:
  -h, --help            show this help message and exit
  --sortby SORTBY       Column to be sorted by. Default is "Citations". To sort
                        by citations per year, use --sortby "cit/year"
  --nresults NRESULTS   Number of articles to search on Google Scholar. Default
                        is 100. (careful with robot checking if value is high)
  --csvpath CSVPATH     Path to save the exported csv file. Default is the 
                        current folder
  --notsavecsv          By default, results are exported to a csv file. Select
                        this option to just print results but not store them
  --plotresults         Use this flag to plot results with the original rank on
                        the x-axis and the number of citations on the y-axis.
                        Default is False
  --startyear STARTYEAR
                        Start year when searching. Default is None
  --endyear ENDYEAR     End year when searching. Default is current year
  --debug               Debug mode. Used for unit testing. It will get pages
                        stored on web archive

1. Default Search:

   sortgs "machine learning"

   This command searches for the top 100 results related to "machine learning" and saves them as a CSV file.

2. Sort by Citations per Year:

   sortgs "machine learning" --sortby "cit/year"

   Search for "machine learning" and sort by the number of citations per year.

3. Specify Date Range:

   sortgs "machine learning" --startyear 2005 --endyear 2015

   Search for papers from 2005 to 2015.

4. Search for an Exact Keyword:

   sortgs "'machine learning'"

5. Save Results in a Specific Path:

   sortgs 'neural networks' --csvpath './examples/'

   This will save the results under a subfolder called 'examples'.

6. Multiple Keywords:

   sortgs '"deep learning" OR "neural networks" OR "machine learning"' --sortby "cit/year"

While running, sortgs will provide updates in the terminal:

❯ sortgs "'machine learning'"
Running with the following parameters:
Keyword: 'machine learning', Number of results: 100, Save database: True, Path: /Users/wittmann/sort-google-scholar, Sort by: Citations, Plot results: False, Start year: None, End year: 2023, Debug: False
Loading next 10 results
Loading next 20 results
...

1. Install Python 3 and its dependencies from Requirements (suggestion: use Ananconda https://www.anaconda.com/distribution/)
2. In the terminal (or cmd if using Windows), run pip install sortgs
3. Use the command sortgs "your keyword" (replace "your keyword" to any keyword that you'd like to search)
4. A CSV file with the name your_keyword.csv should be created.

If those steps are too complicated for you, send me an email with a list of keyworks that you'd like them ranked to: fernando [dot] wittmann [at] gmail [dot] com

This guide will walk you through the process of installing Docker, pulling the fernandowittmann/sort-google-scholar Docker image, and running the project.

1. Download Docker Desktop: Go to the Docker Desktop website and download the appropriate installer for your operating system.
2. Install Docker Desktop: Run the installer and follow the on-screen instructions.
3. Verify Installation: Open a terminal (or command prompt on Windows) and run docker --version to verify that Docker has been installed successfully.

1. Update Package Index: Run sudo apt-get update to update your package index.
2. Install Docker: Run sudo apt-get install docker-ce docker-ce-cli containerd.io to install Docker.
3. Start Docker: Run sudo systemctl start docker to start the Docker daemon.
4. Verify Installation: Run docker --version to ensure Docker is installed correctly.

1. Pull Image: Run the following command to pull the fernandowittmann/sort-google-scholar image from Docker Hub:

   docker pull fernandowittmann/sort-google-scholar

1. Create a Results Directory: Create a directory on your host machine where you want the results to be saved. For example, mkdir ~/results.

2. Run the Docker Container: Use the following command to run the container. This command mounts your results directory to the /results directory in the container and starts the sorting process for Google Scholar results based on your specified parameters.

   docker run -v "$PWD/results:/results" -it fernandowittmann/sort-google-scholar ./sortgs.py --kw "machine learning" --sortby "cit/year" --csvpath /results

   Replace $PWD/results with the absolute path to your results directory if you are not in the parent directory of results.

Just run:

$python -m unittest

And check if all tests passes. Alternativelly send a PR, github actions will run the tests for you.

• MIT