01

Participating Laboratories

  • University of Freiburg PI: Stefan Schmidt
    Germany
  • Chapman University, Orange PI: Uri Maoz
    USA
  • ELTE, University of Budapest PI: Zoltan Kekecs
    Hungary
  • University of California, Santa Barbara PI: Jonathan Schooler
    USA
  • Ludwig-Maximilian University, Munich PI: Moritz Dechamps
    Germany
  • Cognitive Neuroscience Unit, IDOR, Rio de Janeiro PI: Jorge Moll
    Brazil
02

GML Scientific Leadership

  • Jan Walleczek GML Project Director, Scientific Director, Paradox Science Institute
  • Nikolaus von Stillfried GML Project Manager, Science Program Officer, Paradox Science Institute
  • Jorge Moll Chief Science Officer and Founder, Paradox Science Institute
  • Tiago Bortolini GML Project Brazil Leader
Statistics consultant Johannes Gladitz, Berlin, Germany
Software platform Resarc Ltd., London, UK
03

Plain-Language Description

Summary

The GML Project is a large-scale, multi-laboratory metascientific replication study investigating the claimed Ganzfeld telepathy effect — a purported form of anomalous information transfer between a sender and receiver under conditions of sensory shielding. Meta-analyses of over 100 existing Ganzfeld experiments have reported a statistically significant anomalous effect. The GML Project reinvestigates this claim using rigorous, preregistered confirmatory methods across multiple independent laboratories. Project A is the counterfactual "dry run": the full experimental procedure is executed without human participants, with all participant inputs replaced by a random event generator (REG). This serves as a metascientific control to verify the reliability and specificity of the experimental system before Project B — the participant-based study — begins.


04

Preregistration

Registry Open Science Framework (OSF)
Registration ID osf.io/gml-project-a-2025
Date filed 2 February 2026

The study protocol, including all data collection procedures, pre-processing rules, and statistical analysis scripts, was fully specified and locked prior to any data collection. The analysis code has been independently uploaded with a timestamp to: https://github.com/kekecsz/GML_project/blob/main/GML_Analysis%20script.R


05

Data Integrity Record

Powered by Resarc
Platform Resarc Ltd., London, UK — end-to-end tamper-proof behavioral research platform

Data collection status by site

Laboratory Status Trials Collected Verified
University of Freiburg Complete 252
Chapman University Complete 252
ELTE Budapest Complete 251
LMU Munich Complete 251
UC Santa Barbara Complete 201
IDOR Rio de Janeiro Complete 201
Total Complete 1,408

Role-based access summary

All data access during the study was governed by Resarc's role-based access control system. Three role types were active across the study period:

  • Principal Investigator (PI)
    Read access to lab-specific data and exports. No write access post-collection.
  • Experimenter
    Session execution and data entry only. No access to aggregate data or other labs.
  • Auditor
    Full read-only access to all logs, session records, and export events. No write access at any point.

No user held simultaneous write and audit privileges. All role assignments are logged and immutable.

Export log

Event Date Performed by Role Dataset Hash (SHA-256)
Full dataset export — Project A 14 October 2025 Principal Investigator a3f82c...e7b14d
Auditor verification export 16 October 2025 Auditor a3f82c...e7b14d

Both exports produced identical checksums, confirming no data modification occurred between events.

Study lifecycle timeline

Preregistration filed February 2026
Data collection commenced March 2025
Data collection completed September 2025
Dataset exported and hashed October 2025
Auditor verification October 2025
Analysis conducted externally November 2025

Note on analysis: Statistical analysis was conducted externally to the Resarc platform using the preregistered R script. The SHA-256 hash of the exported dataset is published above, allowing any independent researcher to verify that the analysed file is identical to the data as it left the platform. In-platform analysis verification is not yet available for this study.


06

Documents & Results

  • Preregistration document Download PDF → Full preregistration including all technical specifications, statistical analysis plan, and methodology — filed 2 February 2026
  • Analysis script View on GitHub → https://github.com/kekecsz/GML_project/blob/main/GML_Analysis%20script.R
  • Published results Pending Project A results under review. This page will be updated upon publication.
Project A outcome summary

Project A produced null results across all eight preregistered outcome measures (HR-X, HR-O, HR-SX, HR-SO, DR-X, DR-O, DR-SX, DR-SO), consistent with the expected behaviour of an unbiased experimental system in the absence of the independent variable. No alarming indication of system unreliability was detected. GML Project B — the participant-based study — has been cleared to commence.

Why Resarc

Built for studies
that demand precision.

The GML Project required end-to-end coordination across six independent international laboratories, with strict controls on data access, session execution, and export integrity. Resarc provided the platform infrastructure for the full data lifecycle — from session scheduling and role-based access control through to timestamped, tamper-evident data capture and auditable export.

For a study of this methodological rigour, data provenance is not a secondary concern — it is a primary one. The Resarc audit trail gives the GML team, participating labs, and any independent reviewer a verifiable record of exactly what happened to the data, at every stage, from the moment of collection.

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