What is 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.

How does Data to Paper work?

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.

What are the key features?

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.

What can Data to Paper do?

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.

What are the advantages?

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.

What are the limitations?

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.

How do users interact with Data to Paper?

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.

Is using AI in research compliant with publication standards?

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.

Data to Paper

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Data-to-paper is an AI framework that automates the entire scientific research process, transforming raw data into transparent, human-verifiable research papers. It guides AI agents through data analysis, literature search, and code writing, offering autonomous or human-guided operation for accurate results.

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Overview Features Use Cases FAQ Integrations Pricing

About Data to Paper

Who It's For

This tool is for researchers and scientists who want to automate their work and turn raw data into verifiable research papers. It suits those who value transparent, traceable AI-driven research and prefer to oversee the AI's research process.

What You Get

You receive a complete research paper, created from your raw data, with 'data-chained' traceability. This means you can verify any fact back to its source code. The system handles data exploration, literature review, and analysis, operating either autonomously or with human guidance.

How It Works

The system guides interacting AI agents through the entire scientific method. It starts with raw data to form hypotheses, searches literature, writes and debugs analysis code, interprets findings, and then writes the research paper section by section, ensuring a transparent process.

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Features & Capabilities

⚙️ Core AI Research Automation

End-to-End Scientific Research

Automates the complete scientific workflow from raw data to literature search, analysis, interpretation, and final paper writing.

Hypothesis Generation & Testing

Enables the AI to autonomously raise and test hypotheses or allows users to set specific research goals.

Multi-Agent Guided Process

Systematically guides interacting LLM and rule-based agents through the entire conventional scientific research path.

LLM Coding Error Guardrails

Implements safeguards over standard statistical packages to minimize common coding errors made by large language models.

🔗 Transparency & Verifiability

Backward-Traceable Manuscripts

Generates verifiable research papers where any numeric value can be click-traced back to its specific originating code lines.

Transparent Information Flow

Creates manuscripts with a clear and traceable information flow, ensuring human verifiability of results.

Human-Verifiable Outputs

Produces transparent outputs designed for human inspection and verification of the AI's research process.

🤝 User Control & Workflow

Flexible Autopilot/Copilot Modes

Offers both fully autonomous research execution and human-guided interaction via a dedicated Copilot App.

Interactive Research Guidance

Allows users to oversee, inspect, and actively guide the research, including providing reviews or invoking on-demand AI-reviews.

Process Rewind & Replay

Provides functionality to rewind the research process to prior steps and record/replay runs for analysis and debugging.

API Cost Monitoring

Includes functionality to track and manage API usage and associated costs for language model interactions.

Use Cases

Automating End-to-End Scientific Paper Production

Scientists face significant time constraints in moving from raw data to publishable research. This tool automates the entire scientific workflow, including data exploration, hypothesis generation, analysis, and paper writing, accelerating the creation of complete, structured, and verifiable manuscripts.

Academia, Life Sciences, Social SciencesFor: Research Scientists, PhD Candidates, Academic Researchers

Ensuring Traceability and Verifiability in Scientific Research

Reproducibility and transparency are paramount in scientific discovery, yet often challenging to achieve and verify. The platform generates "data-chained" manuscripts, allowing every numeric value to be traced directly back to its originating code, thereby establishing a new standard for verifiable and transparent research.

Scientific Research, Pharmaceutical R&D, Clinical TrialsFor: Research Auditors, Data Scientists, Peer Reviewers

Accelerating Exploratory Research with AI-Assisted Hypothesis Testing

Researchers often need to efficiently explore complex datasets and develop testable hypotheses. In copilot mode, this AI agent assists by autonomously raising and testing hypotheses and integrating relevant literature, allowing scientists to guide the process and accelerate the discovery phase of their research.

Academia, Biomedical Research, Data ScienceFor: Data Analysts, Research Scientists, Exploratory Data Scientists

Generating Rapid Research Insights for Public Health and Policy

Policymakers and public health officials require timely, evidence-based insights to inform critical decisions. This framework quickly analyzes complex datasets, such as health indicators or treatment outcomes, to produce initial research drafts and analyses, providing a faster path to synthesizing evidence for urgent policy and clinical considerations.

Public Health, Healthcare Policy, Clinical ResearchFor: Public Health Analysts, Clinical Researchers, Policy Advisors

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