Oklahoma Nathan Shock Center of Excellence in Basic Biology of Aging

在俄克拉荷马州大学健康科学中心举办的数据科学研讨会（DSW） （OUHSC）起源于当地科学计算专家的小型聚会， 方法.然而，随着高吞吐量数据生成和AI/ML的日益突出， 大大增加了非专业人士的需求，包括PI，博士后研究员和研究生 其工作主要涉及实验室生物学，用于数据各个方面的入门和中级培训 采集、存储和分析。他们通常已经精通计算机，并能够编程到一些 他们的兴趣是简单地动手演示如何将AI/ML方法应用于数据， 通过他们的研究产生或获得。对数据科学课程的兴趣在OUHSC越来越大，但这 需求仍未得到满足。目前，DSW是一个志愿者的努力，新生的OUHSC数据科学课程 关于如何使数据AI/ML准备就绪或如何遵循FAIR原则的指导很少。这种补充剂 将使我们能够开发有关这些主题的材料，并通过研讨会，课程和免费发布 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。

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.