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Data-to-Paper
Overview
Data-to-Paper is an MIT-licensed research framework that moves from annotated data through analysis to a draft scientific manuscript with traceable numerical results.
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Data-to-Paper is an MIT-licensed research framework that moves from annotated data through analysis to a draft scientific manuscript with traceable numerical results.
Provide annotated data and optionally a research goal. The framework explores data, proposes hypotheses, searches literature, writes analysis code and assembles a manuscript. Numerical results can be traced back to the code that produced them.
Install Python and the documented Pandoc/LaTeX dependencies, then configure OpenAI and Semantic Scholar keys, with optional DeepInfra. Use the Copilot interface to inspect, guide or rewind work. Domain experts must vet analysis, citations and conclusions; the project is intended for relatively simple datasets and research questions.
Use it for research on relatively simple datasets and statistical questions. A domain expert must inspect the analysis, citations, ethics and conclusions before using or publishing a manuscript.
Product overview
Capabilities, pricing and fit
Data-to-Paper is an MIT-licensed research framework that moves from annotated data through analysis to a draft scientific manuscript with traceable numerical results.
Information as of October 25, 2026
What it can do
- A traceable research draft
Provide annotated data and optionally a research goal. The framework explores data, proposes hypotheses, searches literature, writes analysis code and assembles a manuscript. Numerical results can be traced back to the code that produced them.[1], [2], [3]
- Setup and scientific responsibility
Install Python and the documented Pandoc/LaTeX dependencies, then configure OpenAI and Semantic Scholar keys, with optional DeepInfra. Use the Copilot interface to inspect, guide or rewind work. Domain experts must vet analysis, citations and conclusions; the project is intended for relatively simple datasets and research questions.[1], [2], [3]
- Historical demonstration
The April 2024 demonstration shows the project’s human-guided Copilot interface.[4]
Where it fits best
Pricing and availability
The source is MIT licensed, but external model usage and local compute can be substantial; monitor the run’s API budget.[1], [2], [3]
Platforms: Python, Local installation[1], [2], [3]
Access: Python, document-compilation dependencies and API keys require local setup and scientific expertise.[1], [2], [3]
Sources (4)
- github.com — product informationOfficial site · checked 2026-10-25
- github.com — product informationOfficial site · checked 2026-10-25
- arxiv.org — product informationOfficial site · checked 2026-10-25
- Historical product demonstrationCommunity · checked 2026-10-25
Autonomy level
89/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
Custom Comparisons
Some of the use cases of Data-to-Paper:
- A traceable research draft
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Popularity level: 67%
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