CyberTraining: CIU: The LSST Data Science Fellowship Program

网络培训：CIU：LSST 数据科学奖学金计划

• 批准号: 1829740
• 负责人: Adam Miller
• 金额: $ 49.93万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829740&HistoricalAwards=false
• 关键词: CyberTraining; CIU; LSST; Data; Science

This National Science Foundation (NSF) Training-based Workforce Development for Advanced Cyberinfrastructure award supplements graduate education in astronomy by providing in-depth training in the skills necessary to make scientific discoveries using big data. Ongoing and future surveys, such as the NSF's flagship optical telescope project, the Large Synoptic Survey Telescope (LSST), are producing data at an unprecedented rate. The sheer size of these data sets requires new working practices: sophisticated computational software and data mining procedures are necessary to fully exploit the rich information present in the data. However, these skills are not typically a core component of the astronomy and astrophysics graduate curriculum. The LSST Data Science Fellowship Program (DSFP) supplements traditional educational programs by training students in a variety of data science methods to work with and ultimately analyze big data. DSFP students are selected from a wide variety of universities using an innovative admissions procedure that increases the participation of students from underrepresented groups. Furthermore, DSFP students are trained in science communication and receive a certification in teaching data science so they can tutor peers and lead training workshops in the material learned as part of the program. The project serves the national interest, as stated by the National Science Foundation's mission: to promote the progress of science, by training the next generation of astronomers to have the computing skills necessary to derive scientific insights from the largest telescopic surveys that have ever been conducted.DSFP students attend six week-long sessions over the course of two years as part of their program training. Each session is hosted by a different institution and designed to focus on a single topic including: the basics of managing and building code, statistics, machine learning, scalable programming, data management, image processing, visualization, and science communication. This curriculum empowers trainees to ask broader questions of their data, prepares them for the technical challenges associated with LSST, and exposes them to the tools and methods necessary to advance fundamental science research. Student participants spread the adoption of data science tools, methods, and resources via the aforementioned teaching workshops, fostering new pathways to discovery in the broader research community. Students must work in collaborative groups, which in conjunction with their science communication training, enhances their leadership and mentoring skills. To reach a broad audience, all materials developed as part of the program are made available to the public, and a guide to convert the material into a semester-long course at the undergraduate or graduate level is provided. This program prepares students for success in a wide range of careers, providing education in data science methodologies, domain-specificconsiderations, and professional skill development in research, teaching, and communication.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项以国家科学基金会(NSF)培训为基础的高级网络基础设施劳动力发展奖通过提供使用大数据进行科学发现所需技能的深入培训，补充了天文学研究生教育。正在进行的和未来的调查，如美国国家科学基金会的旗舰光学望远镜项目--大型天气观测望远镜(LSST)--正在以前所未有的速度产生数据。这些数据集的巨大规模需要新的工作实践：需要复杂的计算软件和数据挖掘程序来充分利用数据中存在的丰富信息。然而，这些技能通常不是天文学和天体物理学研究生课程的核心组成部分。LSST数据科学奖学金计划(DSFP)通过培训学生使用并最终分析大数据的各种数据科学方法来补充传统的教育计划。该计划的学生是从多所大学中挑选出来的，采用了一种创新的招生程序，增加了来自代表性不足群体的学生的参与。此外，DSFP学生接受科学交流方面的培训，并获得教授数据科学的证书，这样他们就可以辅导同行，并领导培训研讨会，将所学材料作为项目的一部分。正如国家科学基金会的使命所述，该项目符合国家利益：通过培训下一代天文学家拥有必要的计算技能，以从有史以来进行的最大规模的望远镜观测中获得科学见解，促进科学进步。作为项目培训的一部分，该项目的学生在两年的时间里参加了为期六周的课程。每次会议由不同的机构主办，旨在集中讨论单一主题，包括：管理和构建代码的基础、统计、机器学习、可伸缩编程、数据管理、图像处理、可视化和科学交流。这门课程使学员能够就他们的数据提出更广泛的问题，为他们应对与LSST相关的技术挑战做好准备，并使他们接触到推进基础科学研究所需的工具和方法。学生参与者通过上述教学研讨会传播数据科学工具、方法和资源的采用，在更广泛的研究社区中培养发现的新途径。学生必须在协作小组中工作，这与他们的科学交流培训相结合，可以增强他们的领导力和指导技能。为了接触到广泛的受众，作为该计划一部分开发的所有材料都向公众开放，并提供了将这些材料转换为本科生或研究生课程为期一学期的指南。该计划为学生在广泛的职业生涯中取得成功做好准备，提供数据科学方法方面的教育，提供特定领域的考虑，以及研究、教学和通信方面的专业技能发展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。