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Dashboard-Project - Using Pandas and Dash

The dashboard is deployed at diffusion.herokuapp.com

I created this dashboard for a seminar. The "Learn More" description wasn't written by me and parts of the data preparation wasn't done by me. Because the dataset dataset/papers.parquet is not distributed with this repository, you won't be able to run it locally. However, you can see what the dashboard looks like on the link above.

Installation

Install using PyCharm

  • Download and install PyCharm
  • New Project -> Get from VCS -> Github
  • Sign in to your Github account
  • Then you can select the repo and clone it
  • When PyCharm asks, create a virtual environment in a folder called "env" using python3.8 and the requirements.txt.
  • If the virtual environment dialog doesn't appear automatically: Select File -> Settings -> Project -> Python Interpreter -> Add. Then it is probably also necessary to open the requirements.txt and click on "install requirements". If you have problems installing pyarrow, make sure you are using python 3.8 or try installing the nightly: pip install --extra-index-url https://pypi.fury.io/arrow-nightlies/ --pre pyarrow.

Install using the terminal

Clone the repo:

git clone <Repo-URL>
cd pandas-dash-dashboard

Set up the environment on macOS and Linux:

python3 -m venv env
source env/bin/activate
pip install -r requirements.txt

Set up the environment on Windows:

py -m venv env
.\env\Scripts\activate
pip install -r requirements.txt

Run as development server

(activate the virtual environment again, necessary)

python index.py
# To run a dtale application with the data set:
python dtale_app.py

The app is then running at 127.0.0.1:8050

On Windows you might need to adjust the 'SDK_HOME' path in the .run files to match the structure of your env directory (eg. to $PROJECT_DIR$/env/Scripts/python).

To run in PyCharm, select the app on the top right and click the green arrow.

Deployment

The files runtime.txt, Procfile and the requirement gunicorn are used for deployment on Heroku.

Dependencies

This project uses:

Dataset

This dataset includes a total of 287544 scientific publications which use deep learning. These papers were published in the Web of Science Core Collection. The papers were retrieved through web scraping. The use of deep learning was identified through keyword search in the title and abstract. The author metadata was used for classifying the publications into company, academia or collaborations of both.

Feature Description Data Type
PY Year Published integer
SC Research Areas string / category
ArtsHumanities Research Area float between 0 and 1
LifeSciencesBiomedicine Research Area float between 0 and 1
PhysicalSciences Research Area float between 0 and 1
SocialSciences Research Area float between 0 and 1
Technology Research Area float between 0 and 1
ComputerScience A Subset of Technology integer 0 or 1
Health A Subset of LifeSciencesBiomedicine integer 0 or 1
NR Cited Reference Count integer
TCperYear Web of Science Core Collection Times Cited Count per Year float
NumAuthors Number of Authors integer
Organisation Either "Academia", "Company" or "Collaboration" string / category
Region 9 Different Regions string / category
Country Country Name of Author string / category
CountryCode ISO 3166-1 Alpha-3 Country Code string / category

The classification of research areas can be found here: webofknowledge.com