神经科学跨学科合作培训:人脑项目教育计划的启示。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-10-01 Epub Date: 2024-11-06 DOI:10.1007/s12021-024-09682-6
Alice Geminiani, Judith Kathrein, Alper Yegenoglu, Franziska Vogel, Marcelo Armendariz, Ziv Ben-Zion, Petrut Antoniu Bogdan, Joana Covelo, Marissa Diaz Pier, Karin Grasenick, Vitali Karasenko, Wouter Klijn, Tina Kokan, Carmen Alina Lupascu, Anna Lührs, Tara Mahfoud, Taylan Özden, Jens Egholm Pedersen, Luca Peres, Ingrid Reiten, Nikola Simidjievski, Inga Ulnicane, Michiel van der Vlag, Lyuba Zehl, Alois Saria, Sandra Diaz-Pier, Johannes Passecker
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引用次数: 0

摘要

神经科学教育面临着快速发展的技术和跨学科脑研究方法的挑战。人脑项目(HBP)教育计划旨在通过培养具备神经科学、医学和信息技术技能的新一代研究人员,满足脑研究对跨学科专业知识的需求。在十年的时间里,该计划聘请了 1 300 多名专家,吸引了来自不同科学学科的 5 500 多人参加其混合学习课程、专门学校和讲习班,以及促进早期研究人员之间对话的活动。该计划的主要原则包括促进跨学科性、适应不断变化的研究环境和基础设施,以及营造一个以增强早期研究人员能力为重点的合作环境。计划结束后,我们在此对各种教育形式和活动进行了分析和深入探讨。我们的结果表明,教育计划在广泛的地理覆盖范围、参与者的多样性以及横向合作的建立方面取得了成功。在这些经验和成就的基础上,我们介绍了如何利用数字工具和平台提供便捷和高度专业化的培训,从而加强针对在分散的欧洲合作空间工作的下一代脑研究人员的现有教育计划。最后,我们介绍了所吸取的经验教训,以便类似的倡议可以借鉴我们的经验并将我们的建议纳入他们自己的计划中。
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Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.

Neuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme's approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme's conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme.

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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
Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T. Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease. A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.
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