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CORD-19 Metadata Dashboard

This project is part of the Frameworks Assignment. It uses the CORD-19 dataset to explore COVID-19 research papers through data analysis and a simple Streamlit web app.

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Features

• Load and explore the CORD-19 metadata.csv file

• Data cleaning and sampling (avoid memory errors)

• Interactive filters:

  • Select year range
  • Filter by journal

• Visualizations:

  • Publications per year (bar chart)
  • Heatmap of publications per journal per year
  • Word cloud of paper titles

• Download filtered data as CSV

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Tools and Libraries

• Python 3.7+
• pandas (data manipulation)
• matplotlib & seaborn (visualizations)
• wordcloud (word cloud generation)
• streamlit (web application)

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Installation

Clone the repository:

git clone https://github.com/iampunit123/week-8-python-assignment-frameworks-.git
cd Frameworks_Assignment

Install dependencies:

pip install -r requirements.txt

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Usage

Run the Streamlit app:

streamlit run app.py

or (if streamlit is not in PATH):

python -m streamlit run app.py

The app will open in your browser at:

http://localhost:8501

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Files

• app.py → Streamlit app
• metadata.csv → dataset file (or metadata_sample.csv if dataset is too big)
• requirements.txt → dependencies list
• README.md → this file

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Expected Outputs

1. Publications by Year (bar chart)
2. Heatmap of publications per journal vs year
3. Word Cloud of paper titles
4. Download Button to export filtered results as CSV

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Reflection

During this project, I learned how to:

• Load and clean real-world datasets (handling missing data, sampling large files)
• Perform basic exploratory data analysis with pandas
• Create visualizations with matplotlib, seaborn, and wordcloud
• Build an interactive dashboard with Streamlit
• Document and share my work using GitHub

Challenges included dealing with the very large dataset (20+ GB). To solve this, I used only the metadata.csv file and sampled rows (nrows=5000) to make the app lightweight and fast.