Oklahoma Nathan Shock Center of Excellence in Basic Biology of Aging

俄克拉荷马州内森休克衰老基础生物学卓越中心

• 批准号: 10404833
• 负责人: ARLAN G. RICHARDSON
• 金额: $ 13.92万
• 依托单位: UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10404833
• 关键词: Biology; Biology of Aging; Computers; Data; Data Science; Educational Curriculum; Educational workshop; FAIR principles; Generations; Health Sciences; Instruction; Methods; Oklahoma; Postdoctoral Fellow; Research; Shock; Specialist; Techniques; Training; Universities; Work; curriculum development; data acquisition; graduate student; interest; machine learning method; programs; scientific computing; volunteer

A Data Science Workshop (DSW) hosted at the University of Oklahoma Health Sciences Center (OUHSC) originated as a small gathering of local scientific computing specialists to share techniques and methods. However, as high-throughput data generation and AI/ML have gained prominence, there has been greatly increased demand from non-specialists, including PIs, postdoctoral fellows, and graduate students whose work mostly entails bench biology, for introductory and intermediate training in all aspects of data acquisition, storage, and analysis. They are generally computer-savvy already and able to program to some degree, and their interest is in short, hands-on demonstrations of how to apply AI/ML methods to data either generated or accessible via their research. The interest in data science classes is growing at OUHSC, but this need is still not met. Currently, the DSW is a volunteer effort, and the nascent OUHSC data science curriculum has minimal instruction on how to make data AI/ML ready or how to follow FAIR principles. This supplement will enable us to develop material on these topics and promulgate it via workshops, coursework, and freely accessible online content. Importantly, it will allow us to reach a much broader audience.

在俄克拉荷马大学健康科学中心举办的数据科学研讨会(DSW) (OUHSC)最初是当地科学计算专家的小型聚会，旨在分享技术和 方法：研究方法。然而，随着高通量数据生成和AI/ML的日益突出，出现了 大大增加了对非专业人员的需求，包括个人投资、博士后研究员和研究生 其工作主要涉及板凳生物学，对数据的所有方面进行入门和中级培训 采集、存储和分析。他们通常已经精通计算机，并能够对一些程序进行编程 学位，简而言之，他们的兴趣是动手演示如何将AI/ML方法应用于数据 通过他们的研究产生或获取。OUHSC对数据科学课程的兴趣正在增长，但这 需求仍然没有得到满足。目前，DSW是一项志愿者努力，新成立的OUHSC数据科学课程 对如何使数据AI/ML做好准备或如何遵循公平原则的指导很少。本副刊 将使我们能够开发关于这些主题的材料，并通过研讨会、课程作业和自由 可访问的在线内容。重要的是，它将使我们能够接触到更广泛的受众。