Data-to-Paper

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SKU: data-to-paper
Data-to-Paper is an innovative AI-powered framework designed to automate the entire scientific research process. By integrating Large Language Models (LLMs) with rule-based agents, it guides the transformation of raw data into complete, transparent, and verifiable scientific manuscripts. The platform autonomously handles tasks such as hypothesis generation, research planning, analysis code writing and debugging, result generation and interpretation, and manuscript drafting. This approach aims to accelerate scientific discovery while upholding essential values of transparency, traceability, and verifiability in research.
AI research automation scientific manuscript generation data analysis Large Language Models research transparency
Researchers seeking to automate and streamline the scientific research workflow.
Academic institutions aiming to enhance research productivity and reproducibility.
Data scientists interested in leveraging AI for comprehensive data analysis and reporting.
Publishers looking to facilitate the generation of high-quality, verifiable scientific manuscripts.
Data-to-Paper demonstrates high autonomy by automating end-to-end scientific research processes including hypothesis generation, literature review, data analysis coding, results interpretation, and manuscript drafting. Its multi-agent system supports both fully autonomous operation (autopilot mode) and human-guided workflows while maintaining backward-traceable data chains. The framework implements automated error checking for statistical analysis and LLM outputs while preserving optional human oversight through GUI intervention points at each research stage.
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