A Streamlit-based interactive dashboard for managing researcher profiles, uploading publication datasets, filtering academic records, and visualizing publication trends using Python. This project was made during the Data Science and Machine Learning Bootcamp by CHPC & NITheCS.
This application provides a simple but powerful interface for researchers to:
- View academic profile information
- Upload and manage publication data (CSV format)
- Filter publications using keywords
- Visualize publication trends over time
- Access contact information
It is built using Streamlit + Pandas, focusing on simplicity, usability, and data-driven insights.
Displays structured researcher information including:
- Name
- Field of study
- Institution
- Upload CSV file containing publication data
- View dataset in interactive table format
- Supports dynamic filtering by keyword
- Enables quick exploration of research output
- Keyword-based search across all publication fields
- Case-insensitive matching
- Instant filtered results display
- Year-based analysis of publications
- Interactive bar chart visualization using Streamlit
- Helps identify research productivity trends
- Displays researcher contact email for collaboration opportunities
- Python
- Streamlit
- Pandas
- Data Visualization (Streamlit Charts)
researcher-dashboard/
│
├── app.py # Main Streamlit application
├── requirements.txt # Dependencies
└── README.md # Documentation