SRF2025 Conference Data Scraper and Explorer

Author: Ming Liu (刘铭)

Overview

This project extracts conference contribution data from the SRF2025 conference PDF and creates an interactive web-based data explorer. The scraper processes the complete contributions.pdf file (357 pages) and extracts 278 conference papers with full metadata including titles, abstracts, authors, presenters, dates, and session information.

File Description

• srf2025_pdf_extractor.py - Main PDF processing script
• srf2025_data_explorer.html - Interactive web data explorer
• srf2025_data.js - External JavaScript data file (301KB)
• index.html - Project homepage with navigation
• requirements.txt - Python dependencies
• README.md - This documentation

Requirements

• Python 3.7+
• PyPDF2 library
• Modern web browser with JavaScript enabled

Installation

1. Ensure Python 3.7 or higher is installed
2. Install dependencies:

   pip install -r requirements.txt

Usage

1. Extract Data from PDF

python srf2025_pdf_extractor.py

2. View Results

Open index.html in your web browser to access:

• Project homepage with navigation
• Interactive data explorer with multiple filters

Features

Data Extraction

• Complete PDF text extraction (pages 2-357)
• 278 conference contributions processed
• Author and presenter information
• Session and track classifications
• Date/time information
• Abstract content
• Contribution codes and types

Interactive Web Explorer

• Search: Full-text search across titles, codes, and abstracts
• Filters:
  • Session filter (16 different sessions)
  • Type filter (Oral, Poster, Student presentations)
  • Author filter (278 unique authors)
  • Presenter filter (278 unique presenters)
  • Date filter (conference dates)

Modern UI Design

• Gradient backgrounds with particle animations
• Responsive design for mobile devices
• Smooth hover effects and transitions
• Card-based layout with expandable abstracts
• Real-time filter statistics

Output Files

SRF2025_Data/
├── SRF2025_All_Contributions.csv    # Complete CSV dataset
├── SRF2025_Complete_Index.json      # JSON data index
└── SRF2025_Extraction_Report.txt    # Processing statistics

Web Interface Features

Data Explorer (srf2025_data_explorer.html)

• Real-time Search: Instant filtering as you type
• Multiple Filters: Combine any number of filters
• Statistics Display: Shows filtered vs total results
• Expandable Abstracts: Click "Read more" for full content
• Responsive Cards: Beautiful card layout with session tags
• Clear Filters: One-click reset functionality

Homepage (index.html)

• Project overview and navigation
• Author attribution
• Links to data explorer and GitHub
• Modern gradient design

Technical Details

Data Structure

{
  "scrape_info": {
    "extraction_time": "2025-09-29 20:29:00",
    "total_contributions": 278,
    "sessions_processed": 16
  },
  "sessions": [
    {
      "session_info": {"id": "MOA", "name": "Monday Opening and Awards"},
      "papers": [
        {
          "contribution_id": "2",
          "contribution_code": "MOA01",
          "type": "Invited Oral Presentation",
          "title": "5 year operation of RIKEN super-conducting linac",
          "date_time": "Monday, September 22, 2025 8:30 AM",
          "abstract": "...",
          "footnotes": "Author: SAKAMOTO, Naruhiko...",
          "session": "Monday Opening and Awards"
        }
      ]
    }
  ]
}

Filter Categories

1. Session Filter: 16 sessions (MOA, MOP, TUA, etc.)
2. Type Filter: Oral, Poster, Student presentations
3. Author Filter: All contributing authors
4. Presenter Filter: All presenters
5. Date Filter: Conference dates

Browser Compatibility

• Chrome 80+
• Firefox 75+
• Safari 13+
• Edge 80+

Performance

• Data file: 301KB (external loading)
• Initial load: <2 seconds
• Filter response: <100ms
• Memory usage: Minimal (client-side processing)

Configuration

The web explorer is fully client-side and requires no server configuration. Simply open the HTML files in any modern web browser.

Important Notes

1. Data Source: Original contributions.pdf must be present for extraction
2. File Size: The JavaScript data file is 301KB - ensure sufficient bandwidth
3. Browser Security: Some browsers may block local file access for JavaScript
4. Mobile Friendly: Responsive design works on all screen sizes

FAQ

Q: How do I run the PDF extraction?

A: Place contributions.pdf in the same directory and run python srf2025_pdf_extractor.py

Q: The web explorer doesn't load data?

A: Ensure srf2025_data.js is in the same directory as the HTML files

Q: Can I host this on a web server?

A: Yes, all files are static and can be served from any web server

Q: How to modify the filters?

A: Edit the JavaScript in srf2025_data_explorer.html to add custom filters

Q: Data appears corrupted?

A: Re-run the PDF extraction script to regenerate clean data files

Technical Support

If you encounter issues, please check:

1. Verify Python 3.7+ is installed
2. Check all dependencies are installed
3. Ensure contributions.pdf is accessible
4. Open browser console for JavaScript errors
5. Check file permissions for data access

License

This project is for academic and research purposes. Please respect copyright and conference data usage policies.

Version History

v1.0 (Current)

• Complete SRF2025 data extraction (278 contributions)
• Interactive web explorer with 5 filter categories
• Modern UI with animations and responsive design
• External data file for performance
• Comprehensive documentation

Author

Ming Liu (刘铭)

• GitHub: @iuming
• Project: SRF2025 Data Scraper and Explorer

Acknowledgments

• SRF2025 Conference organizers
• PyPDF2 library developers
• Open source community