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）：测量神经元活动的技术不断进步，空间和时间分辨率不断提高，同时数据存储成本不断降低，正在推动神经科学的一场革命。 “”体现在全球范围内开始支持神经科学研究，特别是神经数据收集的一系列重大资助计划中。随着全球神经数据的大量积累，全球神经科学界面临着一个新的挑战：如何理解这些数据。复杂的数据来驱动基本的生物学洞察力，并为神经学和神经精神疾病提供新的认识，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适当地应用这些方法。神经科学领域缺乏可利用的（但严格的）资源来开发现代数据分析技术的实践经验，以直接应对这一当前不断增长的全球挑战。开设在线课程（或为了接触到最大的目标受众——生物学家、心理学家，尤其是那些沉浸在神经数据中的人——我们将假设只有基本的数学背景和对计算机编程的有限熟悉，这对于那些接受过生物科学培训的人来说是常见的。将针对各个职业级别的研究人员（从刚开始的本科研究员到已建立的 PI），通过跨学科的案例研究方法来分析和理解神经数据，我们将使用现实世界的神经生理学数据（包括来自大型、新兴的可用数据）。民众OOC 将包含 15 个独立模块，其中前两个模块将强调神经科学家的 MATLAB 编程以及与大型数据集相关的计算技术。 -学习数据和多媒体材料的意愿，结合了视频讲座、基于 MATLAB 的示例和定量评估，模块化格式将通过在线内容提供多种连贯的学习路径，并允许具有不同定量背景的个人进行个性化学习。 OOC 格式还将允许广泛使用、传播和发现所开发的资源。拟议的 OOC 将为研究人员提供分析神经“大数据”所需的基本技能，并提高其一般能力。研究人员的数据使用和分析。