Promoting Awareness and Knowledge to Enhance Scientific Rigor in Neuroscience

Scientific rigor is the structured and controlled application of the scientific method using the highest standards in the field, including considerations in experimental design, data analysis, and reproducibility.

SfN has partnered with NIH and leading neuroscientists who are experts in the field of scientific rigor to offer the series, Promoting Awareness and Knowledge to Enhance Scientific Rigor in Neuroscience as a part of NIH’s Training Modules to Enhance Data Reproducibility (TMEDR).

All original materials produced in this series are provided open access to the field and are supported by Grant Number 1R25DA041326-01 from the National Institute on Drug Abuse (NIDA). The contents of this series are solely the responsibility of the Society for Neuroscience and do not necessarily reflect the official views of NIDA.

The six webinars created in 2016 through the Promoting Awareness and Knowledge to Enhance Scientific Rigor in Neuroscience series are:

  1. Improving Experimental Rigor and Enhancing Data Reproducibility in Neuroscience
  2. Minimizing Bias in Experimental Design and Execution
  3. Best Practices in Post-Experimental Data Analysis
  4. Best Practices in Data Management and Reporting
  5. Statistical Applications in Neuroscience
  6. Experimental Design to Minimize System Biases: Lessons from Rodent Behavioral Assays and Electrophysiology Studies

In addition to this webinar series, explore the other resources below in this collection, including Professional Development Workshops filmed at SfN's annual meetings and articles authored by PIs.

Related Resources

Research Practices for Scientific Rigor: A Resource for Discussion, Training, and Practice

The SfN Scientific Rigor Working Group's set of research practices for scientific rigor can serve as a foundation for ongoing discussion within the neuroscience field.

Record Keeping and Data Management for High Quality Science

Watch this Neuroscience 2016 Short Course to learn about different aspects of and best practices for record keeping and data management.

Scientific Rigor or Rigor Mortis by Christophe Bernard

eNeuro's editor-in-chief introduces a new commentary series.

A Rhumba of “R’s”: Replication, Reproducibility, Rigor, Robustness: What Does a Failure to Replicate Mean? by Oswald Steward

This eNeuro commentary considers how failures to replicate previous findings should be interpreted and suggests possible new journal practices that facilitate publishing replication studies and reporting negative findings.

Statistical Rigor and the Perils of Chance by Katherine Button

This eNeuro commentary, discusses concerns about the reliability and reproducibility of biomedical research and highlights methodological best practices to prevent being fooled by chance findings. 

NIH Efforts on Rigor and Reproducibility

A hub for information about NIH's efforts.

New NIH Funding Guidelines Concerning Rigor and Transparency

Information to assist the extramural research community in addressing rigor and transparency in NIH grant applications and progress reports. 

NIGMS Clearinghouse for Training Modules to Enhance Data Reproducibility

Hosts four NIH training modules focusing on rigor and reproducibility in the research endeavor, as well as training modules to enhance data reproducibility that are produced from NIH grants.   

Optimizing Experimental Design for High-Quality Science

This Neuroscience 2015 Short Course III explains scientific rigor terminology and shares case studies and best practices.

Coursebook: Optimizing Experimental Design for High-Quality Science

These materials, including case studies, from Neuroscience 2015 Short Course III, explore different facets of this topic.

Coursebook: Record Keeping and Data Management for High-Quality Science

These materials, including case studies, from Neuroscience 2016 Short Course III, explore different facets of this topic.

Sparking Global Conversations Around Neuroscience