SciDaEx: Scientific Data Extraction and Structuring System

An open-source system for extracting and structuring data from scientific literature using Large Language Models (LLMs). It integrates a computational backend with an interactive user interface to facilitate efficient data extraction, structuring, and refinement for evidence synthesis in scientific research.

Note: This repository contains two main branches:

• main: The latest version optimized for customization and extension
• scidasynth: The original version as described in our research paper

📞 Contact

Xingbo Wang - Website | Email

📋 Table of Contents

• SciDaEx: Scientific Data Extraction and Structuring System
  • 📞 Contact
  • 📋 Table of Contents
  • ✨ Features
  • 🚀 Installation
    • Configuration
  • 💻 Usage
    • Preprocess Documents
    • Running the Web Application
  • 👥 Contributors
    • Project Timeline
  • 📚 Citation

✨ Features

• 🔍 Automated data extraction from scientific papers (text, tables, and figures)
• 📊 Structured data table output in standardized formats
• 🖥️ Interactive user interface for data validation and refinement
• 🚀 Retrieval-augmented generation (RAG) for enhanced accuracy and speed
• 📈 Quality evaluation metrics for extracted data
• 👥 Support for both technical and non-technical users

🚀 Installation

# Clone the repository
git clone https://github.com/xingbow/SciDaEx.git
cd SciDaEx

# Set up a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install backend dependencies (python 3.10)
pip install -r requirements.txt && pip install "pdfservices-sdk==2.3.0"

# Install frontend dependencies
cd frontend
npm install

Configuration

1. Backend configuration
   • Create a config.yml file in the backend/app/dataService directory
   • Update the config.yml file with the required configurations:
     • Get Adobe service API credentials here
     • Get OpenAI API key here

   openai_key: your_openai_api_key
   
   adobe_credentials:
      client_id: your_adobe_client_id
      client_secret: your_adobe_client_secret

💻 Usage

Preprocess Documents

1. Place your PDF documents in the backend/app/dataService/data directory.
2. Run the preprocessing script:

   cd backend/app/dataService
   python preprocess.py --pdf_dir data --table_dir data/table --figure_dir data/figure --meta_dir data/meta

   This script will extract tables, figures, and metadata from the PDFs and store them in the respective directories.

For details, please refer to the preprocessing documentation.

Running the Web Application

1. Start the backend server

   cd backend
   python run-data-backend.py

2. Start the frontend server

   cd frontend
   npm run serve

3. Open your browser and navigate to http://localhost:8080 to access the SciDaEx interface.

👥 Contributors

Project Timeline

Period	Role	Contributor	Details
2024-08-06 to present	Project Maintainer	Xingbo Wang	-
Until 2024-08-06	Lead Developer	Xingbo Wang	63 commits, +20,575 lines
Until 2024-08-06	Contributor	Rui Sheng	14 commits, +166 lines
Until 2024-08-06	Contributor	Winston Tsui	2 commits, +106 lines

📚 Citation

If you use the repository, please cite the following paper:

@article{wang2024scidasynth,
  title={SciDaSynth: Interactive Structured Knowledge Extraction and Synthesis from Scientific Literature with Large Language Model},
  author={Wang, Xingbo and Huey, Samantha L and Sheng, Rui and Mehta, Saurabh and Wang, Fei},
  journal={arXiv preprint arXiv:2404.13765},
  year={2024}
}