An open, online course in neuronal data analysis for the practicing neuroscientist

面向执业神经科学家的神经元数据分析开放在线课程

• 批准号: 9043612
• 负责人: Jason W Bohland
• 金额: $ 19.38万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9043612
• 关键词: Address; Area; Automobile Driving; Award; Big Data; Biological; Biological Sciences; Boston; Case Study; Categories; Communities; Complex; Computational Technique; Computer software; Computing Methodologies; Custom; Data; Data Analyses; Data Collection; Data Set; Data Storage and Retrieval; Development; Disease; E-learning; Education; Education Projects; Effectiveness; Face; Familiarity; Floods; Foundations; Funding; Goals; Health; Human; Image; Image Analysis; Individual; Learning; Learning Module; Light; Mathematics; Measures; Methods; Multimedia; Neurologic; Neurons; Neurosciences; Neurosciences Research; Participant; Performance; Persons; Psychologist; Quantitative Evaluations; Research; Research Personnel; Resources; Self Assessment; Series; Students; System; Techniques; Technology; Testing; Time; Training; Universities; base; career; computer program; cost; experience; improved; innovation; insight; interest; lectures; neurophysiology; neuropsychiatry; programs; public health relevance; relating to nervous system; repository; skills; success; temporal measurement; theories

 DESCRIPTION (provided by applicant): Advances in technology for measuring neuronal activity at ever-larger scales and with increasing spatial and temporal resolution, concomitant with a decrease in costs of data storage, are driving a revolution in neuroscience. The era of "big data" is reflected in a number of major funding initiatives that have begun worldwide to support neuroscience research, and especially neuronal data collection. As a flood of neuronal data accumulates worldwide, a new challenge faces the global neuroscience community: how to make sense of these complex data to drive basic biological insight and to shed new light on neurological and neuropsychiatric disorders. This new, data-driven era of neuroscientific research demands that investigators master the fundamental methods in time series and image analysis and know when and how to appropriately apply these methods, either in custom applications or in existing software packages. Accessible - yet rigorous - resources to develop hands-on experience with modern data analysis techniques are lacking in neuroscience. To address directly this current and growing worldwide challenge, we propose to develop an innovative open online course (or "OOC"). To reach the largest target audiences - the biologists, psychologists, and clinicians immersed in neuronal data - we will assume only a basic mathematics background and limited familiarity with computer programming, common to those trained in biological sciences. The proposed OOC will target investigators at all career levels - spanning from the beginning undergraduate researcher to the established PI - to analyze and understand neuronal data. Through an interdisciplinary case-study approach, we will use real-world neurophysiological data (including data available from large, emerging public repositories) to motivate the study of modern quantitative analysis methods. The OOC will comprise 15 independent modules. The first two modules will emphasize programming in MATLAB for neuroscientists and computational techniques relevant for large datasets. Each additional module will focus on one category of neuroscience case-study data, and will consist of multimedia material combining video lectures, MATLAB-based examples, and quantitative assessments. The modular format will provide multiple coherent learning paths through the online content, and thereby allow personalized learning for individuals with varying quantitative backgrounds and research interests. The OOC format will also permit the developed resources to be widely available, disseminated, and discoverable. The proposed OOC will prepare researchers with the fundamental skills required for the analysis of neuronal "big data", and elevate the general competencies in data usage and analysis across the research workforce.

 描述（由申请人提供）：用于以越来越大的尺度和越来越高的空间和时间分辨率测量神经元活动的技术的进步，伴随着数据存储成本的降低，正在推动神经科学的革命。“大数据”时代反映在世界范围内已经开始支持神经科学研究，特别是神经元数据收集的一些重大资助计划中。随着世界范围内大量神经元数据的积累，全球神经科学界面临着一个新的挑战：如何理解这些复杂的数据，以推动基本的生物学见解，并为神经系统和神经精神疾病提供新的见解。这个新的、数据驱动的神经科学研究时代要求研究人员掌握时间序列和图像分析的基本方法，并知道何时以及如何在定制应用程序或现有软件包中适当地应用这些方法。在神经科学中，缺乏可解释的-但严格的-资源来开发现代数据分析技术的实践经验。为了直接应对这一当前和日益增长的全球性挑战，我们建议开发一个创新的开放式在线课程（或“OOC”）。为了接触到最大的目标受众--生物学家、心理学家和沉浸在神经元数据中的临床医生--我们将假设只有基本的数学背景和有限的计算机编程熟悉度，这与那些接受过生物科学培训的人一样。拟议的OOC将针对所有职业水平的研究人员-从开始的本科研究人员到既定的PI -分析和理解神经元数据。通过跨学科的案例研究方法，我们将使用真实世界的神经生理学数据（包括来自大型新兴公共存储库的数据）来激励现代定量分析方法的研究。OOC将包括15个独立模块。前两个模块将强调神经科学家在MATLAB中的编程以及与大型数据集相关的计算技术。每个额外的模块将集中在一类神经科学案例研究数据，并将包括多媒体材料结合视频讲座，基于MATLAB的例子，和定量评估。模块格式将通过在线内容提供多个连贯的学习途径，从而使具有不同数量背景和研究兴趣的个人能够进行个性化学习。OOC格式还将允许开发的资源被广泛使用、传播和共享。拟议的OOC将为研究人员提供分析神经元“大数据”所需的基本技能，并提高整个研究队伍在数据使用和分析方面的一般能力。