Meta-Mar is a free online platform for meta-analysis research and education. ✓ Updated 2026
The platform currently consists of two services:
- Meta-Mar Meta-Analysis v4.0.2 — an interactive web application for conducting meta-analyses. Supports continuous, binary, correlation, and generic effect size data. Includes AI-assisted interpretation, publication-quality visualisations, and report generation.
- Meta-Mar Data Extractor v2 — an AI-assisted tool that reads published research papers (PDF) and extracts the quantitative outcome data needed for meta-analysis. Currently supports continuous and binary outcomes.
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).
Used in Academic Teaching
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.
Meta-Analysis v4.0.2 — Core Features
| 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. |
Data Extractor v2 — Experimental
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. |
Case Studies
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.
Meta-Mar Privacy Policy
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.
1. Data Collection and Usage
1.1 User-Uploaded Data
- Uploaded datasets (CSV/Excel) are stored only for the duration of the analysis session.
- Data is automatically deleted when the session ends unless you download the results.
- We do not encourage including personally identifiable information in datasets.
1.2 AI Chatbot Interactions
- Text provided to the AI assistant is processed in real-time for generating responses.
- Chat interactions are not stored beyond the user session unless included in a report.
- Automated anonymization is applied to detect and mask potential personal information.
1.3 Analytics Data
- Basic usage analytics help us improve the platform's performance and features.
- You can opt out of non-essential cookies upon accessing the platform.
- We collect only anonymized metadata (e.g., feature usage patterns).
2. Data Security
- All communications use TLS 1.3 encryption (HTTPS).
- Temporary data is encrypted using industry-standard protocols.
- No user data is retained beyond the active session unless explicitly downloaded.
3. User Rights
- Access: You can request information about data stored during your session.
- Deletion: All data is automatically deleted when your session ends.
- Consent: You can decline non-essential cookies and analytics tracking.
4. Contact Information
For privacy inquiries, please contact: contact@meta-mar.com
Meta-Mar Pro
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.
Access Options
Free
Meta-Analysis v4.0.2
- 2 analyses per month
- 10 AI credits
- All statistical methods & plot types
Data Extractor v2
- 2 study extractions per month
Resets monthly
Starter Pass
Meta-Analysis v4.0.2
- 15 analyses
- 50 AI credits
- All statistical methods & plot types
Data Extractor v2
- 5 study extractions
14-day access
Project Pass
Meta-Analysis v4.0.2
- 100 analyses
- 200 AI credits
- All statistical methods & plot types
- Priority support
Data Extractor v2
- 30 study extractions
60-day access
Extended Pass
Meta-Analysis v4.0.2
- 200 analyses
- 500 AI credits
- All statistical methods & plot types
- Priority support
Data Extractor v2
- 100 study extractions
180-day access
How It Works
- Choose a pass and complete payment via Stripe
- Receive your unique access token on the confirmation page
- Enter the token in Meta-Mar when prompted
- Enjoy Pro features for the duration of your pass
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.
Students & Workshops
Student Project Pass
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
- 100 analyses, 200 AI credits
- All methods, plots, priority support
Data Extractor v2
- 50 study extractions
90-day access
Contact Us for Student PassSend your university email to get your token
University Courses & Workshops
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.
- Course name and institution
- Expected number of participants
- Workshop dates
No cost, no limits for the workshop period
Try the Demo
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.
My Access
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.
Access Pass Status
Need an access pass? View available plans and pricing on the Pro page.
Contribute to Meta-Mar
Help shape the platform's future through feedback and community engagement.
Development Priorities
Which areas matter most to you? Select all that apply:
Other Ways to Help
- Cite — Reference Meta-Mar in your publications
- Report — Submit bugs or ideas via GitHub Issues
- Share — Recommend to colleagues who might benefit
- Feedback — Email contact@meta-mar.com
Citation
@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}
}
Paper under review at Journal of Open Source Software (JOSS)