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神经信息学二十年:文献计量分析

Twenty Years of Neuroinformatics: A Bibliometric Analysis.

作者信息

Guillén-Pujadas Miguel, Alaminos David, Vizuete-Luciano Emilio, Merigó José M, Van Horn John D

机构信息

Department of Business, University of Barcelona, Av. Diagonal 690, Barcelona, 08034, Spain.

School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, 81 Broadway, Ultimo, NSW, 2007, Australia.

出版信息

Neuroinformatics. 2025 Jan 15;23(1):7. doi: 10.1007/s12021-024-09712-3.

Abstract

This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal's evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal's influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics. These themes highlight the journal's role in addressing key challenges in neuroscience through computational methods. Emerging topics like deep learning, neuron reconstruction, and reproducibility further showcase the journal's responsiveness to technological advances. We also track the journal's rising impact, marked by a substantial growth in publications and citations, especially over the last decade. This growth underscores the relevance of computational approaches in neuroscience and the high-quality research the journal attracts. Key bibliometric indicators, such as publication counts, citation analysis, and the h-index, spotlight contributions from leading authors, papers, and institutions worldwide, particularly from the USA, China, and Europe. These metrics provide a clear view of the scientific landscape and collaboration patterns driving progress. This analysis not only celebrates Neuroinformatics's rich history but also offers strategic insights for future research, ensuring the journal remains a leader in innovation and advances both neuroscience and computational science.

摘要

本研究对过去20年的神经信息学进行了全面的文献计量分析,深入洞察了该期刊在神经科学与计算科学交叉领域的发展历程。我们使用VOSviewer等先进工具以及共被引分析、文献耦合和关键词共现等方法,研究了该期刊的出版趋势、引用模式及其影响力。我们的分析揭示了神经成像、数据共享、机器学习和功能连接等持久的研究主题,这些构成了《神经信息学》的核心内容。这些主题凸显了该期刊在通过计算方法应对神经科学关键挑战方面的作用。深度学习、神经元重建和可重复性等新兴主题进一步展示了该期刊对技术进步的响应能力。我们还追踪了该期刊影响力的不断提升,其表现为出版物数量和引用量的大幅增长,尤其是在过去十年。这种增长凸显了计算方法在神经科学中的相关性以及该期刊所吸引的高质量研究。关键的文献计量指标,如发表数量、引用分析和h指数,突出了全球领先作者、论文和机构的贡献,特别是来自美国、中国和欧洲的贡献。这些指标清晰地展现了推动进步的科学格局和合作模式。这一分析不仅颂扬了《神经信息学》丰富的历史,还为未来研究提供了战略见解,确保该期刊在创新方面保持领先地位,并推动神经科学和计算科学的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eaa/11735507/f231eeeb5836/12021_2024_9712_Fig1_HTML.jpg

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