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神经信息学实践教育在在线与面授的十字路口:从神经黑客学院学到的经验教训。

Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

机构信息

Department of Psychology, University of Washington, 119 Guthrie Hall, Seattle, 98195, Washington, USA.

eScience Institute, University of Washington, 3910 15th Ave NE, Seattle, 98195, Washington, USA.

出版信息

Neuroinformatics. 2024 Oct;22(4):647-655. doi: 10.1007/s12021-024-09666-6. Epub 2024 May 20.

Abstract

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

摘要

神经黑客学院(https://neurohackademy.org)是一个为期两周的活动,旨在培训早期职业神经科学研究人员数据科学方法及其在神经影像学中的应用。该活动旨在通过向参与者介绍传统课程中经常忽略的数据科学方法和技能,来弥合大数据技能差距。由于协同数据收集工作,对于神经影像学研究而言,分析和解释大型和复杂数据集变得越来越重要,因此需要这些技能。在 2020 年,该活动迅速从线下活动转变为线上活动,吸引了来自世界各地的数百名参与者。这一经历和参与者的经历极大地改变了我们对大型在线可访问活动的看法。在随后的 2022 年和 2023 年的活动中,我们开发了一种“混合”格式,其中包括线上和线下参与者。我们讨论了混合活动的技术和社会技术元素,并讨论了我们在组织这些活动时学到的一些经验教训。我们特别强调了这些活动在创建神经影像学和数据科学交叉领域的全球和包容性实践社区方面可以发挥的作用。

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