Scientific Method Automation

Automated scientific method using large language models.

Overview

This tool guides you through a complete scientific investigation by:

1. Observation/Question - Gathering initial observations and context
2. Hypothesis Formation - Creating testable hypotheses
3. Prediction - Generating specific, measurable predictions
4. Experimentation - Designing experiments to test predictions
5. Analysis - Analyzing experimental approaches and results
6. Conclusion - Synthesizing findings and suggesting next steps

Features

• Complete Scientific Method Pipeline - Automated workflow through all scientific method steps
• Web Search Integration - Uses web search for gathering observations and context
• Experimental Data Generation - Can simulate realistic experimental data
• Comprehensive Logging - Saves detailed logs with timestamps
• JSON Export - Results saved in structured JSON format
• Flexible Input - Multiple ways to specify research questions

TODO

• Write a comprehensive research paper documenting the scientific method automation framework
• Connect to real-world data gathering from actual experiments
• Integration with laboratory equipment APIs for automated data collection
• Support for importing experimental data from CSV/Excel files
• Add visualization capabilities for experimental results (charts, graphs)
• Implement statistical analysis tools for experimental data validation
• Integration with symbolic calculation engines (SymPy, Wolfram Alpha, Mathematica)
• Add support for multiple hypothesis testing and comparison
• Automated paper generation from results (abstract, introduction, methods, results, discussion, conclusion)
• Create web interface for easier interaction
• Add support for collaborative research (multi-user features)
• Integration with research databases (PubMed, arXiv, etc.)
• Export results to scientific paper format (LaTeX, academic templates)
• Give LLMs access to Python compiler
• Give LLMs access to all sorts of scientific tools
• Implement validation and double-checking mechanisms after each scientific method step
• Add step-by-step verification prompts to ensure quality and accuracy of each phase

Requirements

• Python 3.7+
• OpenAI API key with access to GPT-5 (or modify self.model in code for other models)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/scientific-method.git
cd scientific-method

2. Install dependencies:

pip install -r requirements.txt

3. Set up your OpenAI API key:

# Create a .env file
echo "OPENAI_API_KEY=your-api-key-here" > .env

Usage

Basic Usage

# Provide question as command-line argument
python scientific_method.py "Does rapamycin increase lifespan?"

# Use the --question flag
python scientific_method.py --question "How does photosynthesis work?"

# Set via environment variable
SCIENTIFIC_METHOD_QUESTION="What causes earthquakes?" python scientific_method.py

Options

• --question, -q - Specify the scientific question to investigate
• --no-data - Skip experimental data generation

Examples

# Full investigation with simulated data
python scientific_method.py "Why is the sky blue?"

# Theoretical analysis without data generation
python scientific_method.py --no-data "What is the nature of dark matter?"

# Using environment variable
export SCIENTIFIC_METHOD_QUESTION="Does coffee improve cognitive performance?"
python scientific_method.py

Output

The script generates two output files:

1. Log File - scientific_method_YYYYMMDD_HHMMSS.log

   • Timestamped log with all steps and outputs
   • Human-readable format for review

2. Results JSON - scientific_method_results.json

   • Structured data containing all results
   • Can be processed programmatically

Example Output Structure

{
  "question": "Does rapamycin increase lifespan?",
  "observations": "...",
  "hypothesis": "...",
  "predictions": "...",
  "experiments": "...",
  "experimental_data": "...",
  "analysis": "...",
  "conclusion": "..."
}

API Configuration

The script uses OpenAI's API. By default, it's configured for GPT-5:

self.model = "gpt-5"

To use a different model, modify this in the ScientificMethod.__init__ method or update the code to accept model as a parameter.

Development

Contributing

Contributions are welcome! Feel free to:

• Report bugs
• Suggest new features
• Submit pull requests
• Improve documentation

License

MIT License - feel free to use this project for any purpose.

Notes

• The observe step uses web search capabilities for gathering real-world context
• Experimental data generation is simulated by the LLM (not real experiments)
• Results quality depends on the LLM model capabilities
• Best used for theoretical investigations and research planning