Data to Paper

Data to Paper is an automation framework that uses AI agents to complete end-to-end scientific research processes. It transforms raw data into traceable, scientifically rigorous manuscripts automatically, enabling transparent and verifiable research automation. The system was archived on April 24, 2024.

Data to Paper systematically navigates interacting AI agents through a complete scientific research workflow. The system can design research plans, raise hypotheses, write and debug analysis code, and create information-traceable papers. It operates by observing data, generated research goals, analysis code, and previously generated output, then takes actions such as writing and executing analysis code, exploring data and metadata, searching literature through the Semantic Scholar API, and writing hyperlinked LaTeX papers section-by-section.

The platform includes several important capabilities: a multi-agent system for converting data to research papers; multiple operational modes, including both human-guided and autopilot modes; backward traceability, which creates manuscripts with data-chaining to verify the research process; literature integration that searches academic literature to inform research generation; code automation that writes, debugs, and executes analysis code autonomously; and flexible input options that accept data, data descriptions, and optionally fixed analysis goals.

The system enables several research automation tasks: automated hypothesis generation and testing, cross-disciplinary scientific research exploration, data-driven manuscript generation and analysis, accelerated scientific literature production, and complete research workflows from raw data to publishable papers.

Data to Paper offers several key benefits: it enables fully autonomous scientific research processes, creates backward-traceable manuscripts with data-chaining for verification, provides flexible human-guided or autopilot modes, minimizes common AI coding research errors, and supports cross-disciplinary research automation frameworks.

Users should be aware of potential challenges: it requires sophisticated AI agent coordination, faces potential reliability challenges in complex research scenarios, and has high computational and API cost requirements.

Users can interact with the system through a graphical user interface (GUI) app that allows operation in copilot mode. Users provide data, data descriptions, and optionally specify a fixed analysis goal, after which the system produces research output.

Yes, generative AI tools can be used to prepare manuscripts according to major publication policies. However, if entire sections of a work (including tables, graphs, images, and other content) are generated by AI tools, you should disclose which sections and which tools and versions you used by preparing an appendix or supplementary material document that describes the use, including the specific tools, versions, prompts provided as input, and any post-generation editing.