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REU Site: Civic Data Science

REU 网站：公民数据科学

基本信息

批准号：
1659757
负责人：
Christopher Le Dantec
金额：
$ 36万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2020-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659757&HistoricalAwards=false
关键词：
REU Site Civic Data Science

项目摘要

The Georgia Tech Research Experience for Undergraduates (REU) on Civic Data Science is a 10-week immersive research experience for undergraduate students interested in contributing to the developing field of data science in areas of civic importance, such as public safety and services, transportation, sustainability and democratic participation. The 10-week course will mix formal technical training through a skills bootcamp and weekly group learning sessions presented by academic and industry practitioners working across the diverse practices that make up data science. The REU experience will expand the students' understanding of research and research process by placing students in teams that include other students at a similar level of research maturity as well as with graduate students who are further along in the transition from new to independent researcher. This exposure will inform students' future choices about potential career paths within academia as well as within industrial settings where the skills that compose data science are in increasing demand. Within a larger social context, by structuring the program's research activities around civic data science problems, it is expected that the products of the summer program to have near-term impact on partnering local, government and non-profit organizations, and long-term impact on the students by demonstrating opportunities to put technical skill to work in areas of civic importance. Each year the program will recruit 10 outstanding undergraduates both locally and nationally with diverse academic and cultural backgrounds.The focus of the REU site is on the data, analytics, and user interaction aspects of data science in a domain that has not traditionally benefited from advances in computing - the civic sector. While civic applications of data science are diverse, they share common themes: they are frequently place-based, they deal with data coming from siloed collection systems, they involve inherently incomplete and inaccurate data, and they produce information and insights for use by non-data experts in government, in community governance organizations, and by community members. These themes will provide coherence for the intellectual endeavors of the REU Site cohort. Students will work in teams comprised of at least one graduate student and at least two undergraduate students, mentored closely by a faculty member. The program culminates with a poster, demo, and reception showcase that is open to the public and held at an off-campus venue. The final event is meant to showcase the students' work and to connect them to the larger data science community (in Atlanta and elsewhere).

格鲁吉亚技术研究经验的本科生（REU）公民数据科学是一个为期10周的沉浸式研究经验的本科生有兴趣在数据科学的发展领域作出贡献的公民的重要性，如公共安全和服务，交通，可持续发展和民主参与领域。为期10周的课程将通过技能训练营和每周小组学习课程进行正式的技术培训，这些课程由学术和行业从业者在组成数据科学的各种实践中提供。REU的经验将扩大学生对研究和研究过程的理解，将学生放在团队中，包括其他学生在研究成熟度的类似水平，以及与研究生谁是进一步沿着从新的过渡到独立的研究人员。这种接触将告知学生未来在学术界以及工业环境中的潜在职业道路的选择，其中构成数据科学的技能需求日益增加。在更大的社会背景下，通过围绕公民数据科学问题构建该计划的研究活动，预计夏季计划的产品将对合作的地方，政府和非营利组织产生短期影响，并通过展示将技术技能应用于公民重要性领域的机会对学生产生长期影响。该计划每年将在当地和全国范围内招募10名具有不同学术和文化背景的优秀本科生。REU网站的重点是数据科学的数据，分析和用户交互方面，而这个领域传统上并没有从计算的进步中受益-公民部门。虽然数据科学的公民应用是多种多样的，但它们有着共同的主题：它们通常是基于地点的，它们处理来自孤立收集系统的数据，它们涉及固有的不完整和不准确的数据，它们产生的信息和见解供政府、社区治理组织和社区成员中的非数据专家使用。这些主题将为REU研究中心队列的智力努力提供一致性。学生将在至少一名研究生和至少两名本科生组成的团队中工作，由一名教师密切指导。该计划以海报，演示和接待展示为高潮，向公众开放，并在校外场地举行。最后的活动是为了展示学生的工作，并将他们连接到更大的数据科学社区（在亚特兰大和其他地方）。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Food for Thought: Analyzing Public Opinion on the Supplemental Nutrition Assistance Program

深思：分析公众对补充营养援助计划的意见

DOI：
10.1080/15228835.2017.1416514
发表时间：
2018
期刊：
Journal of Technology in Human Services
影响因子：
1.5
作者：
Scott, Dorris;Oh, Jihwan;Chappelka, Miriam;Walker-Holmes, Mizzani;DiSalvo, Carl
通讯作者：
DiSalvo, Carl

Evaluating electric vehicle user mobility data using neural network-based language models

使用基于神经网络的语言模型评估电动汽车用户移动数据