Data2Paper is an AI academic research platform that helps researchers turn structured datasets into editable research papers. It is designed for students, faculty members, clinical researchers, survey analysts, and research teams that need to move from raw data to a structured manuscript without switching between many separate tools.

Users can upload CSV or Excel files containing survey, questionnaire, experimental, scale, panel, or clinical data. Data2Paper analyzes the dataset, selects appropriate statistical methods, produces tables and figures, and drafts structured sections for a research paper. The generated work can be previewed online and exported as PDF, Word, LaTeX, or a downloadable source package for continued editing and collaboration.

The platform also supports literature-review generation from a research topic and multi-reviewer feedback for uploaded manuscripts. These workflows help users organize prior research, identify gaps, improve argument structure, and receive actionable comments before submitting a paper to a journal or conference.

Data2Paper is useful when a researcher needs a reproducible first draft that combines data analysis with academic writing. It does not replace the judgment of a qualified researcher, statistician, or domain expert. Users remain responsible for checking assumptions, interpreting results, reviewing citations, and validating every conclusion.

Key features include spreadsheet upload, statistical analysis, automatic figures and tables, structured manuscript drafting, literature reviews, AI peer-review feedback, online previews, and editable export formats. The service follows a freemium model so new users can evaluate the workflow before choosing a paid plan for larger or more frequent projects.