LAUSR

Thousands of pharmacology experiments are performed each day, generating hundreds of drug discovery programs, scientific publications, grant submissions, and other efforts. Discussions of the low reproducibility and robustness of some of this research have led to myriad efforts to increase data quality and thus reliability. Across the scientific ecosystem, regardless of the extent of concerns, debate about solutions, and differences among goals and practices, scientists strive to provide reliable data to advance frontiers of knowledge. Here we share our experience of current practices in nonclinical neuroscience research across biopharma and academia, examining context-related factors and behaviors that influence ways of working and decision-making. Drawing parallels with the principles of evidence-based medicine, we discuss ways of improving transparency and consider how to better implement best research practices. We anticipate that a shared framework of scientific rigor, facilitated by training, enabling tools, and enhanced data sharing, will draw the conversation away from data unreliability or lack of reproducibility toward the more important discussion of how to generate data that advances knowledge and propels innovation.