CORD-19 Data Analysis and Streamlit App

This project analyzes the CORD-19 metadata dataset and presents findings through a Streamlit web application.

Overview

The CORD-19 dataset contains information about COVID-19 research papers. This analysis focuses on:

• Publication trends over time
• Top publishing journals
• Word frequency in paper titles
• Distribution by data source

Dataset

The metadata.csv file from the CORD-19 dataset includes:

• Paper titles and abstracts
• Publication dates
• Authors and journals
• Source information

Download from: https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge

Installation

1. Clone this repository
2. Create a virtual environment: python -m venv .venv
3. Activate the environment: .venv\Scripts\activate (Windows)
4. Install dependencies: pip install -r requirements.txt

Usage

Jupyter Notebook

Run the cord19_analysis.ipynb notebook for data exploration and analysis.

Streamlit App

Run the Streamlit app: streamlit run app.py

The app allows interactive filtering by year range and data source.

Findings

• Most COVID-19 papers were published in 2020
• Top journals include various medical and scientific publications
• Common words in titles: covid, coronavirus, sars, etc.
• Sources are primarily from PMC, bioRxiv, etc.

Challenges

• Handling large dataset size
• Dealing with missing values in abstracts
• Parsing dates in various formats

Technologies Used

• Python
• Pandas
• Matplotlib
• Seaborn
• Streamlit
• WordCloud