It is now widely accepted that openness and transparency are keys to improving the reproducibility of scientific research, but many challenges remain to adoption of these practices. I will discuss the growth of an ecosystem for open science within the field of neuroimaging, focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

In this talk I will present recent developments in data sharing, organization, and analyses that allow to build fully reproducible workflows. First, I will present the Brain Imaging Data structure and discuss how this allows to build workflows, showing some new tools to read/import/create studies from EEG data structured that way. Second, I will present several newly developed tools for reproducible pre-processing and statistical analyses. Although it does take some extra effort, I will argue that it largely feasible to make most EEG data analysis fully reproducible.

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 of audience Q&A. In total, the event will last one hour and 15 minutes. Afterwards, some of the speakers will join us for an informal chat and Q&A reserved only for students/postdocs.