Rigorous Research Principles for Practicing Neuroscientists

神经科学家的严格研究原则

• 批准号: 10721722
• 负责人: Uri Tzvi Eden
• 金额: $ 8.98万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721722
• 关键词: Active Learning; Address; Area; Automobile Driving; Behavioral Research; Biological; Biological Sciences; Biomedical Research; Case Study; Clinical Research; Collaborations; Communities; Competence; Data; Data Analyses; Data Storage and Retrieval; E-learning; Education; Education Projects; Educational Models; Exercise; Face; Familiarity; Feedback; Goals; Individual; Learning; Mathematics; Measures; Modernization; Multimedia; Neurons; Neurosciences; Neurosciences Research; Psychologist; Pythons; Research; Research Personnel; Research Technics; Resources; Students; Technology; Text; Training; Writing; career; community center; complex data; computer code; computer program; cost; education research; educational atmosphere; experience; hands-on learning; improved; insight; interest; learning materials; lectures; nervous system disorder; neural; neuropsychiatric disorder; personalized learning; pragmatic implementation; skills; temporal measurement; undergraduate student

PROJECT SUMMARY Advances in technology for measuring neural 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. As neural data accumulates worldwide, a new challenge faces the global neuroscience community: how to make sense of these complex data to drive biological insight and to shed new light on neurological and neuropsychiatric disorders. This new, data-driven era of neuroscientific research demands that investigators master rigorous research principles and know when and how to apply these principles. Accessible resources to develop hands-on experience with these rigorous research techniques are lacking in neuroscience. To address this current and growing challenge, we propose to develop educational units focused on specific principles of rigorous research. To reach the largest target audiences - the biologists, psychologists, and clinicians working directly with neural data - we will assume only a basic mathematics background and limited familiarity with computer programing, common to those trained in biological sciences. The proposed educational units will target investigators at all career levels - spanning from the beginning undergraduate researcher to the established PI - to understand and apply principles of rigorous research to neural data. Through an interdisciplinary case-study approach, we will use real-world data from neuroscience to motivate the study and application of rigorous research principles. To engage learners, we will develop multimedia material combining written text, short video lectures, and computer programming exercises, that promote hands-on learning of the material. 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 educational unit format will also permit the developed resources to be widely available, disseminated, and discoverable, and easily integrated into the CENTER framework. The proposed educational units will prepare researchers with the fundamental skills required for the analysis of neural data and elevate the general competencies in rigorous research principles across the research workforce.

项目摘要 在越来越大的尺度以及空间和越来越多的水平和 时间分辨率（随之而来的数据存储成本降低）正在推动一场革命 神经科学。随着神经数据在全球范围内积累，全球神经科学面临的新挑战 社区：如何理解这些复杂的数据以推动生物学见解并为 神经和神经精神疾病。这个新的，数据驱动的神经科学研究需求的时代 该研究人员掌握了严格的研究原则，并知道何时以及如何应用这些原则。 缺乏这些严格的研究技术来开发动手经验的可访问资源 神经科学。为了应对当前和日益增长的挑战，我们建议开发教育单位 专注于严格研究的具体原则。到达最大的目标受众 - 生物学家， 心理学家和直接处理神经数据的临床医生 - 我们将仅假设基本数学 背景和对计算机编程的熟悉程度有限，这是对生物科学培训的人常见的。 拟议的教育单位将针对各个职业水平的调查员 - 从一开始就跨越 既定PI的本科研究人员 - 了解和应用严格的研究原则 神经数据。通过跨学科的案例研究方法，我们将使用来自神经科学的现实世界数据 激励严格研究原则的研究和应用。吸引学习者，我们将发展 多媒体材料结合了书面文本，简短的视频讲座和计算机编程练习， 促进对材料的动手学习。模块化格式将提供多个连贯的学习路径 通过在线内容，从而为具有不同定量的个人提供个性化学习 背景和研究兴趣。教育单位格式还将允许开发的资源 可以广泛使用，传播和可发现，并容易集成到中心框架中。这 拟议的教育单位将使研究人员拥有分析所需的基本技能 神经数据并提高了整个研究中严格研究原则的一般能力 劳动力。