在线与面授交汇处的神经信息学实践教育:从 NeuroHackademy 学到的经验。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-05-20 DOI:10.1007/s12021-024-09666-6
Ariel Rokem, Noah C Benson
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引用次数: 0

摘要

NeuroHackademy ( https://neurohackademy.org ) 是一项为期两周的活动,旨在培训早期神经科学研究人员掌握数据科学方法及其在神经成像中的应用。该活动旨在通过向学员介绍传统课程中经常忽略的数据科学方法和技能,弥补大数据技能方面的差距。这些技能是分析和解释大型复杂数据集所必需的,而随着数据收集工作的开展,这些数据集在神经成像研究中变得越来越重要。2020 年,该活动迅速从现场活动转变为在线活动,包括来自世界各地的数百名参与者。这次经历和参与者的经历大大改变了我们对大型在线活动的评价。在 2022 年和 2023 年举办的后续活动中,我们开发了一种 "混合 "形式,既包括在线参与者,也包括现场参与者。我们讨论了混合活动的技术和社会技术要素,并讨论了我们在组织这些活动时吸取的一些经验教训。我们特别强调了这些活动在神经成像和数据科学交叉领域创建全球性和包容性实践社区方面所能发挥的作用。
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Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

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.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury. Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value. A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers.
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