The Einstein Foundation Berlin, in partnership with the Public Library of Science (PLOS), has opened the call for entries for the 2026 Einstein Foundation Award for Promoting Quality in Research, inviting applications and nominations from researchers, institutions, and organizations worldwide whose work advances the quality, transparency, and reproducibility of science and research across disciplines.
The annual international award, worth a total of €350,000, is conferred in cooperation with the QUEST Center for Responsible Research at the Berlin Institute of Health at Charité (BIH). Submissions are open until April 30, 2026, at 10:00 pm UTC, with awardees to be announced by the end of 2026.
The award recognizes contributions in three categories. The Individual Award, valued at €150,000, is open to individuals or small teams demonstrating a significant impact on research quality and is available through self-nomination or nomination by others. The Institutional Award, valued at €100,000, recognizes organizations, large collaborative networks, or other entities making exceptional contributions to advancing research quality. The Early Career Award, also valued at €100,000, encourages early career researchers and small teams to apply with innovative projects that strengthen transparency, robustness, and integrity in research.
In 2025, the Individual Award was presented to personality psychologist Simine Vazire, Professor of Psychology Ethics and Wellbeing at the University of Melbourne, for advancing methodological rigor, reproducibility, and collaborative research in psychology, including contributions to the Society for the Improvement of Psychological Science (SIPS) and the journal Collabra.
The Institutional Award was awarded to the Brazilian Reproducibility Initiative, recognized as the largest coordinated replication effort in laboratory biology worldwide. The Early Career Award was received by Maximilian Sprang for the project Erring Rigorously, which focuses on improving reproducibility in functional genomics by distinguishing biological signals from technical errors in high-throughput sequencing.
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