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).
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Improving Experimental Rigor and Enhancing Data Reproducibility in Neuroscience. The topics of scientific rigor and data reproducibility have been increasingly covered in the scientific and mainstream media, and they are being addressed by publishers, professional organizations and funding agencies. This webinar addresses topics of scientific rigor as they pertain to preclinical neuroscience research.
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Minimizing Bias in Experimental Design and Execution. Investigations into the lack of reproducibility in preclinical research often identify unintended biases in experimental planning and execution. This webinar covers random sampling, blinding and balancing experiments to avoid sources of bias.
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Experimental Design to Minimize Systemic Biases: Lessons from Rodent Behavioral Assays and Electrophysiology Studies. Common sources of bias in animal behavior and electrophysiology experiments can be minimized or avoided by following best practices of unbiased experimental design and data analysis and interpretation. In this webinar, presenters discuss experimental design and hypothesis testing for mouse behavioral assays, as well as sampling, interpretational bias and referencing in in vitro and in vivo electrophysiology recording studies.
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Tackling Challenges in Scientific Rigor: The (Sometimes) Messy Reality of Science. This webinar explores practical examples of the challenges and solutions in conducting rigorous science from neuroscientists at various career stages. It focuses on development of the interpersonal, scientific and technical skills needed to address various issues in scientific rigor, such as what to do when you can't replicate a published result, how to get support from a mentor and how to cope with various career pressures that might affect the quality of your science.
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Research Practices for Scientific Rigor: A Resource for Discussion, Training, and Practice. From Society for Neuroscience's working group on scientific rigor.
- 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.
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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.
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Statistical Rigor and the Perils of Chance. By Katherine S. Button
- Rigor or Mortis: Best Practices for Preclinical Research in Neuroscience by Oswald Steward and Rita Balice-Gordon
- Guidelines for Preclinical Animal Research in ALS/MND: A Consensus Meeting by Albert C. Ludolph et. al.
- Accelerating Drug Discovery for Alzheimer's Disease: Best Practices for Preclinical Animal Studies by Diana W. Shineman et. al.