data2paper.ai is not a publication guarantee, ghostwriting shortcut, ethics approval substitute, or clinical decision service. We support the technical workflow; researchers remain responsible for research design, data provenance, clinical interpretation, authorship, and final submission decisions.
What data2paper.ai does
Data and statistics support
We help profile structured research data, identify missing values and distributions, prepare analysis plans, run statistical models, and document the rationale for method selection so results can be reviewed.
Figures and manuscript draft support
We prepare reviewable tables, figures, methods text, results text, and manuscript draft sections. Drafts are starting points for researcher review and revision, not final scientific decisions.
Reference verification
References should trace back to DOI, PubMed, journal, or other verifiable records where available. We do not accept fabricated references, and the researcher should verify final citations before submission.
Submission package assembly
When in scope, we help organize cover letters, author contribution statements, reporting checklists, reproducibility notes, and journal formatting materials for researcher review.
What data2paper.ai does not do
- We do not guarantee journal acceptance, indexing, impact factor outcomes, or publication speed.
- We do not fabricate data, references, ethics approval, informed consent, or author contributions.
- We do not provide clinical diagnosis, treatment advice, patient-care decisions, or regulatory approval advice.
- We do not help bypass authorship rules, duplicate-submission rules, institutional policy, or journal disclosure requirements.
Why the service exists
Many clinicians and research trainees have real data and research questions but lose time at the technical bridge: data cleaning, method selection, figure production, statistical wording, reference checking, and journal formatting. data2paper.ai focuses on that bridge.
Our work is designed to be reviewable: methods should be documented, key numbers should reconcile across abstract/text/tables/figures, references should be checkable, and code or analysis notes should be available when included in the written scope.
Core pages
| Question | Page |
|---|---|
| How can clinical data become a manuscript draft? | Clinical Data to Manuscript Draft |
| How are statistical methods selected and documented? | AI-Assisted Statistical Analysis |
| How are references and research data handled? | Reference Verification and Data Security |
| What does an example analysis package look like? | Heart Failure Cox Regression Case Study |
Request a project assessment
Send your study design, sample size, data format, target journal range, and expected deliverables. Do not send identifiable patient data during the free assessment stage.
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