Leibniz Open Science Day 2026: Scientific Rigor and Collaborative Research in the Age of AI

Research conference on scientific reliability, transparency, reproducibility, and the opportunities and challenges of AI in social science research.

The Leibniz Open Science Day 2026 brings together researchers committed to strengthening scientific rigor, transparency, and collaborative research in the social sciences. Under the theme “Scientific Rigor and Collaborative Research in the Age of AI,” the event examines how research practices can become more reliable, reproducible, and transparent.

The program covers open science incentives, data and code sharing, replication studies, research robustness, and experimental design. In his keynote, Felix Holzmeister from the University of Innsbruck presents research on the reliability of peer review and approaches to improving scientific evaluation.

A dedicated afternoon session focuses on AI in research, exploring the reliability of AI-powered literature review tools, differences in results across large language models, and the methodological risks of combining human research data with synthetic responses. Further contributions address AI-assisted replication and the challenges of reproducible research.

Date: November 17, 2026
Time: 9:00 AM–5:10 PM; registration from 8:45 AM
Location: Leibniz Association, Chausseestraße 111, 10115 Berlin
Organizers: ZBW – Leibniz Information Centre for Economics, DIW Berlin, WZB Berlin Social Science Center, RWI – Leibniz Institute for Economic Research, Lab² – Metalab for Better Science
Admission: Free, registration required
Audience: Researchers and scholars working in social sciences, economics, open science, research methodology, and AI-assisted research
Focus: Open Science, AI in research, reproducibility, replication, research transparency, peer review, LLMs, synthetic data
Keynote: Felix Holzmeister, University of Innsbruck, on the reliability and improvement of peer review
Morning session: Open Science – Incentives, Sharing & Publishing
Parallel sessions: Replication, Reproducibility & Robustness; Measurement, Experiments & Generalizability
AI session: AI in Research – Opportunities, Reliability & Epistemic Risks, 3:30–5:00 PM
Program: Research presentations, keynote, parallel sessions, and closing remarks