data-to-paper: AI-Driven Scientific Research Framework
data-to-paper is an innovative automation framework developed by Technion-Kishony-lab that leverages interacting AI agents to conduct complete end-to-end scientific research. Starting from raw data, it navigates through data exploration, literature search, hypothesis generation, data analysis, and culminates in the creation of transparent, backward-traceable, and human-verifiable scientific papers. This tool is designed to enhance research transparency and efficiency while allowing human oversight through a Copilot App.
Key Features
- End-to-End Research Automation: Covers the entire scientific process from data to publication-ready papers across various fields.
- Backward-Traceable Manuscripts: Ensures transparency by allowing users to trace numerical results back to the specific code lines that generated them.
- Autopilot or Copilot Modes: Operates autonomously or with human guidance, offering flexibility to set goals, review progress, and manage API costs.
- Coding Guardrails: Minimizes common LLM coding errors by overriding standard statistical packages with protective measures.
Use Cases
- Health Indicators Analysis: Applied to datasets like CDC’s Behavioral Risk Factor Surveillance System for hypothesis testing and result interpretation.
- Social Network Studies: Analyzes Twitter interactions among Congress members to derive insights through AI-driven research.
- Treatment Policy Evaluation: Assesses impacts of guideline changes in medical datasets, such as neonatal care outcomes.
- Treatment Optimization: Utilizes machine learning to predict optimal medical procedures, like tracheal tube intubation depth in pediatric patients.
Target Users: Researchers, data scientists, and academic institutions looking to accelerate scientific discovery with AI while maintaining rigorous standards of verifiability and transparency.
Unique Selling Points: The framework's ability to produce traceable, verifiable research outputs and its dual-mode operation (autonomous or guided) set it apart as a pioneering tool in AI-assisted scientific research.
