Data2Paper is an AI-powered academic research platform that generates complete research papers from uploaded survey or clinical data, creates literature reviews from topics, and produces multi-reviewer peer review feedback — all within a single web application.
What is Data2Paper?
Data2Paper is a web-based AI platform that accepts CSV, XLSX, and XLS data files up to 10MB and generates full research papers with abstract, introduction, methodology, results, and discussion sections. It also produces literature reviews from a research topic with sourced citations and simulates multi-reviewer peer review on uploaded PDF papers. The platform supports eight output languages and delivers downloads in PDF, Word, LaTeX, and ZIP formats with all figures and source files. Account-scoped paper access ensures each user's data and outputs are isolated.
Key Features
- Data-to-paper generation — Upload survey, questionnaire, or clinical data; the AI handles data cleaning, variable selection, statistical analysis, and full paper writing in one pass.
- Statistical analysis pipeline — Runs Cronbach's α, t-test/ANOVA, Pearson correlation, linear/multiple regression with VIF, mediation/moderation, Kaplan-Meier survival, ROC/AUC, and logistic/Cox regression.
- Multi-format export — Download papers as PDF, Word, LaTeX, and ZIP archives containing figures and source files for editing.
- Multilingual output — Generate papers and reviews in Chinese, English, Arabic, Japanese, Korean, French, German, and Spanish.
- Literature review generation — Enter a topic; the system retrieves, summarizes, and synthesizes academic sources into a structured review with citations and bibliography.
- AI peer review — Upload a PDF paper to receive independent reviews from simulated reviewers, an editorial decision, a revision roadmap, and an integrity check.
- Demo downloads — Test the platform with downloadable demo files for research paper (student wellbeing dataset), literature review, and peer review outputs.
Who should use Data2Paper?
- Survey and questionnaire researchers — Upload CSV/XLSX exports from survey tools and receive a complete paper with reliability analysis, correlation matrices, and regression models.
- Clinical researchers — Use the medical research analysis type to generate papers with survival curves, ROC analysis, and clinical outcome comparisons from patient data.
- Graduate students and academics — Generate literature reviews from a research topic, or upload a draft paper for AI peer review feedback before journal submission.
Use cases
- Survey data to paper: A researcher uploads a survey CSV, selects "Survey Analysis," and receives a paper with Cronbach's α, correlation heatmap, and regression output in their chosen language.
- Literature review from a topic: A PhD student enters "machine learning in healthcare" and receives a structured review with thematic sections, source summaries, and bibliography, downloadable as PDF.
- Pre-submission peer review: A researcher uploads a manuscript PDF and gets multiple AI reviewer reports, an editorial decision, and a prioritized revision roadmap.
Pricing
Data2Paper uses a credit-based freemium model. The Free tier ($0/month) allows submitting tasks and previewing results but requires credits to unlock full outputs. Paid tiers: Basic ($20/month, 50 credits), Pro ($40/month, 150 credits), and Enterprise ($80/month, 400 credits). Each task costs 30 credits. Students get 40% off and educators 20% off through an education discount program.
FAQ
What data formats are supported?
CSV, XLSX, and XLS files up to 10MB, ideal for survey exports, scale data, questionnaire responses, and clinical databases.
What output languages are available?
Eight languages: Chinese, English, Arabic, Japanese, Korean, French, German, and Spanish. All sections of the generated paper are produced in the selected language.
What can I
