Topic: data collection

Reliable research requires detailed protocols, careful data collection, and thorough reporting of findings and limitations. Pressure to publish quickly can lead to rushed statistical coding, inadequate model checking, and, in some cases, manipulated or fabricated results. Rising publication volumes strain peer review, while artificial intelligence makes it easier to produce papers and code outside a researcher’s established expertise. This talk examines how these pressures undermine research quality and argues for changes that make published findings more reliable. Detailed records of a projec

When meta-research (research on research) makes an observation or points out a problem (such as a flaw in methodology), the project should be repeated later to determine whether the problem remains. For this we need meta-research that is reproducible and updatable, or living meta-research. In this talk, we introduce the concept of living meta-research, examine prequels to this idea, and point towards standards and technologies that could assist researchers in doing living meta-research. We introduce technologies like natural language processing, which can help with automation of meta-research,

This virtual, interactive panel on reproducibility in neuroscience will focus on practical advice that researchers at all career stages could implement to improve the reproducibility of their work, from power analyses and pre-registering reports to selecting statistical tests and data sharing. The event will comprise introductions of our speakers and how they came to be advocates for reproducibility in science, followed by a 25-minute discussion on reproducibility, including practical advice for researchers on how to improve their data collection, analysis, and reporting, and then 25 minutes o