activity
20242026
most citedOvercoming Barriers to Computational Reproducibility

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CY2026

Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop

Valerie Bürger, Marlie Besouw, Jana Fehr +26

Meta-research and Trustworthy AI (TAI) share common goals, namely improving evidence, robustness, and transparency, yet there is very little interplay between the two fields. To in…

cs.DL20261 cited

Overcoming Barriers to Computational Reproducibility

Roman Hornung, László Németh, Oleksandr Zadorozhny +13

Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the…

stat.ME2025

On "Confirmatory" Methodological Research in Statistics and Related Fields

F. J. D. Lange, Juliane C. Wilcke, Sabine Hoffmann +2

Empirical substantive research, such as in the life or social sciences, is commonly categorized into the two modes exploratory and confirmatory, both of which are essential to scie…

stat.ME2024

Rethinking the handling of method failure in comparison studies

Milena Wünsch, Moritz Herrmann, Elisa Noltenius +3

Comparison studies in methodological research are intended to compare methods in an evidence-based manner to help data analysts select a suitable method for their application. To p…

cs.LG2024

Position: Why We Must Rethink Empirical Research in Machine Learning

Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger +7

We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to un…