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
{"title":"Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers.","authors":"Mathew Abrams, John Darrell Van Horn","doi":"10.1007/s12021-024-09693-3","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"619-622"},"PeriodicalIF":2.7000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12021-024-09693-3","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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)。
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1