Description
data-to-paper is a sophisticated automation framework designed to revolutionize scientific research by leveraging AI agents. It systematically navigates the complete end-to-end research lifecycle, beginning with raw data and culminating in the creation of transparent, backward-traceable, and human-verifiable scientific papers. This project aims to establish a new standard for AI-driven research, emphasizing transparency, traceability, and verifiability while empowering scientists to oversee and direct the process.
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The framework guides interacting large language models (LLMs) and rule-based agents through conventional scientific pathways. This includes data exploration, literature search, hypothesis generation, data analysis, interpretation of results, and the step-by-step writing of a complete research paper. A key innovation is its "data-chained" manuscript generation, where any numerical value in the paper can be traced back to the specific code lines that produced it, ensuring a high degree of verifiability.
Users can choose between fully autonomous operation or a "Copilot" mode, which allows for human intervention. In Copilot mode, users can oversee and inspect the research process, set research goals, or allow the AI to autonomously raise and test hypotheses. It also facilitates AI-driven reviews, rewinding the process to prior steps, recording and replaying runs, and tracking API costs. The system incorporates coding guardrails to minimize common LLM coding errors, enhancing the reliability of the generated code.
The motivation behind data-to-paper is to explore the capabilities and limitations of LLM-driven scientific research and to develop methods for accelerating research while upholding core scientific values. The framework is described in the NEJM AI paper "Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers" and a pre-print on arXiv. It is designed for relatively simple research goals and datasets where statistical hypotheses are being raised and tested, inviting contributions for further development and extension.
- CategoryAI Research Assistants
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
data-to-paper's Core Features
End-to-end field-agnostic research automation
Backward-traceable "data-chained" manuscripts
Autonomous operation mode
Human-guided "Copilot" mode for oversight and direction
AI-driven hypothesis generation and testing
Automated literature search and data analysis
Step-by-step scientific paper writing
Coding guardrails to minimize LLM errors
Traceability of numerical values to source code
Ability to review, rewind, and replay research steps
Watermarked manuscripts for AI-created transparency
Getting Started with data-to-paper
Clone: Clone the data-to-paper repository from GitHub.
Install: Install the necessary dependencies using pip.
Configure: Set up API keys and any required configurations.
Execute: Run the data-to-paper command-line interface with your data.
Guide (Optional): Utilize the Copilot App for human oversight and direction.
Review: Carefully vet the generated manuscripts for accuracy and quality.
data-to-paper's Use Cases
- Automated Data Analysis
- Hypothesis Generation
- Literature Review Assistance
- Scientific Paper Drafting
- Research Process Oversight
- Reproducible Research
- Accelerated Scientific Discovery
