Meta-Mar is a free online platform for meta-analysis research and education. ✓ Updated 2026
The platform currently consists of two services:
No installation or registration required. Both services integrate AI assistance to guide users through methodological decisions. Meta-Mar is designed for researchers, educators, and students across fields where evidence synthesis is valuable. The platform serves as both a research tool and an interactive learning environment, with context-specific methodological guidance at each step.
In 2025, 5,800+ researchers worldwide used Meta-Mar, with 200+ published papers citing it as their primary meta-analysis tool (Google Scholar).
Workshop in evidence synthesis — Department of Nursing, Faculty of Nursing and Occupational Therapy, University of Extremadura, Cáceres (May 2026)
20 participants completing 5–6 individual meta-analyses each across a three-day course on Synthesis of Evidence in Health Sciences II.
PhD course in meta-analysis — Center for Health Technology Assessment, Semmelweis University, Budapest (January 2026)
~30 concurrent users running real-time analyses with AI guidance tailored to each study.
| Capability | Description |
|---|---|
| Methodological Flexibility | Support for various outcome types (continuous, binary, correlations, effect size) and both fixed-effect and random-effects models with multiple estimators (REML, DL, PM, ML, HS, SJ, HE, EB). Implements different calculation methods for confidence intervals (classic, Hartung-Knapp, Kenward-Roger). |
| Visualization Tools | Generates publication-quality forest plots, funnel plots, Galbraith plots, L'Abbe plots, Baujat plots, and bubble plots for meta-regression with customization options for statistical presentation. |
| Heterogeneity Assessment | Calculates heterogeneity statistics including I², τ², and Cochran's Q, with subgroup analysis options to explore sources of variability across studies. |
| Publication Bias | Implements multiple methods for assessing publication bias including Egger's test, trim-and-fill analysis, and fail-safe N calculations (Rosenthal, Orwin, Rosenberg methods). |
| Meta-Regression | Provides tools for exploring relationships between study characteristics and effect sizes through meta-regression analysis with options for continuous and categorical moderators. |
| AI Assistance | Features an interactive AI chatbot that provides methodological guidance, helps with interpretation of statistical outputs, and supports learning about meta-analytic concepts through natural language interaction. Also includes AI-powered report generation for comprehensive summaries of meta-analysis results. |
This is an experimental feature. In comparative validation against human-verified, peer-reviewed datasets, the extractor achieved approximately 85% accuracy. We recommend verifying extracted values against the original paper before including them in a final analysis.
| Capability | Description |
|---|---|
| PDF-to-Data Extraction | Upload a study PDF and describe your meta-analysis scenario. The AI reads the full paper, identifies relevant statistical values, and returns a structured single-row dataset ready for Meta-Mar Meta-Analysis v4.0.2. |
| Four-Step Workflow | Upload & describe → Scout report & decisions → Targeted extraction → Results & export. Each step gives the researcher control over what is extracted and how ambiguities are resolved. |
| Supported Outcomes | Continuous outcomes (means, SDs, sample sizes) and binary outcomes (events, totals). Handles multiple reporting formats including change scores, pre–post designs, reported effect sizes, and derived statistics (t-values, p-values, SE-to-SD conversion). |
| Confidence & Provenance | Each extracted value is tagged with a confidence level (high, medium, low) and the source passage from the paper, enabling transparent verification. |
| CSV Combiner | Built-in utility to merge multiple single-study CSV exports into one combined dataset for meta-analysis. |
Meta-Mar Workshop: End-to-end meta-analysis tutorials using real clinical data (e.g., MIMIC-IV). From study design and cohort extraction to statistical analysis and AI-generated interpretation.
Coming soon.
Effective Date: 24.12.2024
Meta-Mar is committed to ensuring user privacy while providing an effective platform for conducting meta-analyses. This policy outlines our data practices regarding user-uploaded data and AI chatbot interactions.
For privacy inquiries, please contact: contact@meta-mar.com
Meta-Mar is a free and open-source platform for meta-analysis research and education. It consists of two services: Meta-Mar Meta-Analysis v4.0.2 for running interactive meta-analyses, and Meta-Mar Data Extractor v2 for AI-assisted extraction of study data from published research papers.
Everyone can use both services with a free monthly allowance. Need more? Purchase a one-time Pro pass to unlock additional capacity across both services.
Meta-Analysis v4.0.2
Data Extractor v2
Resets monthly
Meta-Analysis v4.0.2
Data Extractor v2
14-day access
Meta-Analysis v4.0.2
Data Extractor v2
60-day access
Meta-Analysis v4.0.2
Data Extractor v2
180-day access
Already have a token? Enter it when you launch Meta-Mar Meta-Analysis or Meta-Mar Data Extractor to unlock Pro features. The same token works across both services.
Same as Project Pass with a 26% student discount and 90 days of access instead of 60. Requires a university email for verification.
Meta-Analysis v4.0.2
Data Extractor v2
90-day access
Contact Us for Student PassSend your university email to get your token
Teaching meta-analysis? We provide free full-access tokens for academic courses and workshops. All participants get complete Pro access for the entire workshop period.
No cost, no limits for the workshop period
Explore Meta-Mar's capabilities with our built-in demo. The demo tab is always available to everyone, with or without a subscription.
The demo tab includes sample data and walks you through a complete meta-analysis workflow.
Enter your access pass code to view your current credit usage across all Meta-Mar services. Each pass includes credits for Meta-Analysis, AI Assistance, and Data Extraction.
Need an access pass? View available plans and pricing on the Pro page.
Help shape the platform's future through feedback and community engagement.
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If you use Meta-Mar in your research, please cite the preprint and/or the software as appropriate.
Beheshti, A., Sazmand, H., Chavanon, M.-L., & Christiansen, H. (2026). Meta-Mar: An AI-assisted web platform for accessible and rigorous meta-analysis. OSF Preprints. https://doi.org/10.31222/osf.io/psw4x_v1
@software{beheshti2026metamar,
author = {Beheshti, Ashkan and Sazmand, Hassan and Chavanon, Mira-Lynn and Christiansen, Hanna},
title = {Meta-Mar: An AI-Integrated Web Platform for Meta-Analysis},
year = {2026},
url = {https://www.meta-mar.com},
version = {4.0.2}
}
Software paper under review at the Journal of Open Source Software (JOSS).