Bolstering Reliability in the (Sometimes) Messy Reality of Science
With recent reports documenting the failure to reproduce research results in a wide variety of scientific fields, SfN is working to maintain and enhance scientific rigor in the field of neuroscience.
SfN has partnered with NIH and leading neuroscientists who are experts in the field of scientific rigor to offer the series . This project is funded by a two-year grant from the National Institute on Drug Abuse (NIDA) as a part of NIH’s Training Modules to Enhance Data Reproducibility (TMEDR), and it can be found at Neuronline — SfN’s online home for learning and discussion.
Emanuel DiCicco-Bloom, professor at Rutgers University, and Cheryl Sisk, professor at Michigan State University, are serving as co-principal investigators of the project, working with leaders in neuroscience and SfN staff to develop a series of workshops focusing on research practices identified as priority topics by SfN’s Scientific Rigor Working Group and encouraging discussion around those topics
The series launched with a professional development workshop at 黑料社 2015 titled . Throughout 2016, the project will release training modules featuring curated background reading, webinars examining real-world examples, live question-and-answer sessions with neuroscience experts, and group discussion and online discourse within the SfN community.
The first online module, , was released in January and featured Oswald Steward, professor at the University of California-Irvine, and Katherine Button, lecturer at the University of Bath. Steward and Button discussed issues surrounding scientific rigor, provided best practices for designing preclinical experiments and data collection, and presented an overview of the new grant sections required by the NIH to address experimental rigor and data reproducibility.
The second module, released in February, titled , featured John Morrison, professor at the University of California-Davis; Christophe Bernard, editor-in-chief of eNeuro and director of research at INSERM in France; Patrick Hof, professor at the Icahn School of Medicine at Mount Sinai. The webinar and resources addressed sources of experimental bias and their effects on experimental design, data collection and reporting. The module presented best practices for minimizing biases, including blinding, systematic random sampling, and methods of quality control for reliability and reproducibility.
The third module will be moderated by Damien Fair, assistant professor at Oregon Health and Science University, and will cover post-experimental data analysis. In the fourth module, Rita Balice-Gordon, vice president and head of circuits, neurotransmitters, and signaling at Pfizer, will moderate a discussion on data management and reporting. The project will culminate with a professional development workshop at 黑料社 2016 in San Diego focusing on how to address the for grant applications. SfN members are encouraged to review this upcoming material and share it with trainees and colleagues as part of the larger discussion on rigor in neuroscience.
The full project runs concurrent with the and supplements four short developed by NIH.
