Rigorous Research Principles for Practicing Neuroscientists

神经科学家的严格研究原则

基本信息

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
项目类别：
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

项目摘要

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.

项目概要测量神经活动的技术不断进步，尺度越来越大，空间和范围越来越广。时间分辨率的提高以及数据存储成本的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的积累，全球神经科学面临新的挑战社区：如何理解这些复杂的数据以推动生物学洞察并揭示新的线索神经系统和神经精神疾病。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严格的研究原则，并知道何时以及如何应用这些原则。目前缺乏可利用的资源来培养这些严格研究技术的实践经验。神经科学。为了应对当前这一日益严峻的挑战，我们建议建立教育单位专注于严格研究的具体原则。为了接触最大的目标受众——生物学家，直接使用神经数据的心理学家和临床医生 - 我们只假设有基本的数学知识对计算机编程的背景和熟悉程度有限，这是受过生物科学培训的人所共有的。拟议的教育单位将针对所有职业级别的调查人员 - 从一开始本科生研究员到既定的 PI - 理解并应用严格研究的原则神经数据。通过跨学科的案例研究方法，我们将使用来自神经科学的真实世界数据激励严格研究原则的学习和应用。为了吸引学习者，我们将开发结合书面文字、短视频讲座和计算机编程练习的多媒体材料促进材料的实践学习。模块化格式将提供多种连贯的学习路径通过在线内容，从而允许具有不同数量的个人进行个性化学习背景和研究兴趣。教育单元的形式也将允许开发的资源广泛可用、传播和发现，并轻松集成到 CENTER 框架中。这拟议的教育单位将为研究人员提供分析所需的基本技能神经数据并提高整个研究中严格研究原则的一般能力劳动力。