Neuroscience

Many of the resources below can also be found on Society for Neuroscience: Neuronline. 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).

  • Best Practices in Post-Experimental Data Analysis. Proper data handling standards, including appropriate use of statistical tests, are integral to rigorous and reproducible neuroscience research. Training in quantitative neuroscience is a specific area of emphasis for the BRAIN Initiative, and rigorous statistical analysis methods are included in the recent Proposed Principals and Guidelines for Reporting Preclinical Research [PDF, 69KB]. This webinar covers best practices in post-experimental data analysis.
     
  • Best Practices in Data Management and Reporting. Efforts to enhance scientific rigor, reproducibility and robustness critically depend on archiving and retrieving experimental records, protocols, primary data and subsequent analyses. In this webinar, presenters discuss best practices and challenges for data management and reporting, particularly when dealing with information security and sensitive material; archiving and disclosure of pre- and post-hoc data analytics; and data management on multidisciplinary teams that include collaborators around the globe.
     
  • Record Keeping and Data Management for High-Quality Science. Proper record keeping and data management are critical components of scientific rigor and responsibility. This Short Course focuses on what all scientists should know.

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    Statistical Applications in Neuroscience. How can neuroscientists improve their “statistical thinking” and make full and effective use of their data? This webinar covers common applications of statistics in neuroscience, including the types of research questions statistics are best positioned to address, modeling paradigms and exploratory data analysis. The presenters also share examples and case studies from their research.

  • Statistical Rigor and the Perils of Chance. By Katherine S. Button