Neuromatch Academy: Teaching Computational Neuroscience with global accessibility

Viegen, Tara van; Akrami, Athena; Bonnen, Kate; DeWitt, Eric; Hyafil, Alexandre; Ledmyr, Helena; W., Lindsay, Grace; Mineault, Patrick J.; Murray, John D.; Pitkow, Xaq; Puce, Aina; Sedigh-Sarvestani, Madineh; Stringer, Carsen; Achakulvisut, Titipat; Alikarami, Elnaz; Atay, Melvin Selim; Batty, Eleanor; Erlich, Jeffrey C; Galbraith, Byron; Guo, Yueqi; Juavinett, Ashley L.; Krause, Matthew R.; Li, Songting; Pachitariu, Marius; Straley, Elizabeth; Valeriani, Davide; Vaughan, Emma; Vaziri-Pashkam, Maryam; Waskom, Michael; Blohm, Gunnar; Kording, Konrad; Sauseng, Paul; Wyble, Brad; Escola, Sean; Peters, Megan A. K.

doi:10.48550/arxiv.2012.08973

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2024

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“…To address this practically, and on short time scales, we should establish shared/common training material across our U19s and set up regular seminars or webinars covering common interests across U19. This could complement modern large scale online resources like the Neuromatch Academy 22 , or stand on its own.…”

Section: Sharingmentioning

confidence: 99%

Data science and its future in large neuroscience collaborations

Schottdorf,

Yu,

Walker

2024

Preprint

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The rise of large scientific collaborations in neuroscience requires systematic, scalable, and reliable data management. How this is best done in practice remains an open question. To address this, we conducted a data science survey among currently active U19 grants, funded through the NIH’s BRAIN Initiative. The survey was answered by both data science liaisons and Principal Investigators, speaking for ∼500 researchers across 21 nation-wide collaborations. We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and propose policies to improve data collection, use, publication, re-use and training in the neuroscience community.

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Section: Sharingmentioning

confidence: 99%