缩小差距:神经信息学如何培养下一代神经科学研究人员》(Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers)。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-10-01 DOI:10.1007/s12021-024-09693-3
Mathew Abrams, John Darrell Van Horn
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引用次数: 0

摘要

神经技术和大数据是两个快速发展的领域,它们有可能改变我们对大脑及其功能的认识。神经技术的进步使研究人员能够在功能、分子和解剖层面以前所未有的精细程度研究大脑的功能。因此,收集到的数据不仅更多,而且数据集也更大。要充分利用大数据的潜力和神经技术的进步来提高我们对神经系统的认识,就需要培养新一代的神经科学家,他们不仅要具备相关领域的专业知识,还要掌握查询和整合大数据所需的计算和数据科学技能。重要的是,神经信息学是神经科学的一个分支学科,致力于开发神经科学数据和知识库以及计算模型和分析工具,以共享、整合和分析实验数据,并推进有关神经系统功能的理论。虽然目前只有少数几个正规的神经信息学培训项目,而且神经信息学很少被纳入传统的神经科学培训项目,但神经信息学界一直试图通过社区活动和研讨会来弥补传统神经科学教育项目与下一代神经科学研究人员需求之间的差距。因此,本特辑的目的是重点介绍几项此类社区活动,这些活动从面对面的研讨会到大规模的全球虚拟培训联盟,从培训学生到培训培训师,不一而足。
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Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers.

Neurotechnology and big data are two rapidly advancing fields that have the potential to transform our understanding of the brain and its functions. Advancements in neurotechnology have enabled researchers to investigate the function of the brain at unprecedented levels of granularity at the functional, molecular, and anatomical levels. Thus, resulting in the collection of not only more data, but also larger datasets. To fully harness the potential of big data and advancements in neurotechnology to improve our understanding of the nervous system, there is a need to train a new generation of neuroscientists capable of not only domain expertise, but also the computational and data science skills required to interrogate and integrate big data. Importantly, neuroinformatics is the subdiscipline of neuroscience devoted to the development of neuroscience data and knowledge bases together with computational models and analytical tools for sharing, integration and analysis of experimental data, and advancement of theories about the nervous system function. While there are only a few formal training programs in neuroinformatics, and since neuroinformatics is rarely incorporated into traditional neuroscience training programs, the neuroinformatics community has attempted to bridge the gap between the traditional neuroscience education programs and the needs of the next generation of neuroscience researchers through community initiatives and workshops. Thus, the purpose of this special collection is to highlight several such community efforts which span from in-person workshops to large-scale, global virtual training consortiums and from training students to training-the-trainers.

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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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