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.
- 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.
Clone the repo:
git clone <Repo-URL>
cd pandas-dash-dashboardSet up the environment on macOS and Linux:
python3 -m venv env
source env/bin/activate
pip install -r requirements.txtSet up the environment on Windows:
py -m venv env
.\env\Scripts\activate
pip install -r requirements.txt(activate the virtual environment again, necessary)
python index.py
# To run a dtale application with the data set:
python dtale_app.pyThe 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.
The files runtime.txt, Procfile and the requirement gunicorn are used for
deployment on Heroku.
This project uses:
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