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Abstract:
Modern NeuroAI research\u2014characterized by large-scale collaborations, complex neuronal recordings, and sophisticated machine learning analyses\u2014demands robust data and code management practices. Yet, many research labs struggle to uphold best data science practices, hindering high-throughput research and compromising research reproducibility. In this interactive session, I will first present findings from our recent survey of U19 grants\u2014multi-institutional neuroscience research projects funded through the NIH’s BRAIN Initiative in the USA\u2014that reveal systematic challenges in current data science approaches across laboratories.
We will then examine practical challenges in coordinating diverse expertise and managing complex datasets and analyses across institutions, demonstrating how effective data science practices and tools are essential for project success. Finally, I will present our ongoing development of a flexible data processing pipeline framework that integrates data and code management, designed to scale from individual researchers to multi-laboratory collaborations. Researchers at all levels\u2014from students to faculty\u2014are encouraged to participate and share their perspectives throughout the session. Together, we aim to identify key challenges and potential solutions in data science to advance robust and reproducible research practices in our field.<\/p>\n\n\n\n
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