8 papers · ranked by Valyu relevance
Peng Wang, Hongyuan Cao, Xiaoquan Wen, Macha Nikolski
We further extend (0-3)$(1)$ to model observed effects, along with the corresponding standard errors. The resulting hierarchical model, incorporating a user-defined $P_{\text{mis}}$ threshold, fully characterizes the properties of replicable signals given a group of estimated effects and their corresponding standard…
María Paula Fernández-García, Guillermo Vallejo-Seco, Pablo Livácic-Rojas
The replicability crisis in the behavioral sciences should no longer be understood as a merely technical problem confined to the failure to reproduce specific findings. Rather, it reflects a deeper structural issue: a growing misalignment between data, method, and inference. When these three levels cease to be…
Rita Banzi, Monika Varga, Yuri Andrei Gelsleichter, Constant Vinatier + 2 more
Evidence-based solutions are needed to help improve reproducibility in research. This Consensus View presents a consensus-based list of core reproducibility items for research that has been developed by a multidisciplinary group interested in research, open science, and reproducibility. The set of minimum requirements…
Jonna Brenninkmeijer, Maarten Derksen, Stephanie Meirmans, Jeannette Pols
Ethnography and other empirical studies of replication played a significant role in the sociology of scientific knowledge (SSK) during the 1970s and 1980s. Collins and other proponents of SSK highlighted that exact replication was impossible, knowledge was often tacit and hard to explicate, and that results were always…
Jan Walleczek, Nikolaus von Stillfried, Stefan Schmidt, Marc Wittmann + 4 more
This metascientific project studied the replicability of Bem Experiment 1, which had claimed a precognitive effect, i.e., the ability to successfully guess the outcome of future random events (Bem. J Pers Soc Psychol. 2011;100: 407−25). The use of advanced methodologies-based on the advanced meta-experimental protocol…
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Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
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The value of generative artificial intelligence (AI) for teaching and learning is currently hotly debated. Concerns regarding the accuracy of information produced by generative AI as well as student over-reliance on this tool coexist with excitement about tailored opportunities that AI may provide for educational…
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Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…