NRT-DESE: Interdisciplinary Graduate Training to Understand and Inform Decision Processes Using Advanced Spatial Data Analysis and Visualization
NRT-DESE:使用高级空间数据分析和可视化来理解和指导决策过程的跨学科研究生培训
基本信息
- 批准号:1633299
- 负责人:
- 金额:$ 299.39万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-09-15 至 2022-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This National Science Foundation Research Traineeship (NRT) award to SUNY at Stony Brook will provide science and engineering graduate students with unique interdisciplinary skills to assist and eventually lead in the translation of complex data-enabled research into informed decisions and sound policies. Within all sectors of industry and government, effective decision making depends on the ability of scientists to interpret data and communicate results in a way that supports the decision-making process. This new training program responds to this challenge with an interdisciplinary set of new courses and a suite of activities united by the theme of "Scientific Training and Research to Inform DEcisions" (STRIDE). It specifically will include the rarely explicitly taught skills of decision support, such as understanding the perspectives of stakeholders, science communication, and translating scientific uncertainty. The project anticipates training 90 PhD students, including at least 20 funded doctoral trainees, and a similar number of non-trainee MS and PhD students that will participate in program components from the departments of atmospheric and marine sciences, ecology and evolution, computer science, biomedical informatics, applied mathematics and statistics, journalism, and advanced computational science. STRIDE will initially target environmental sustainability (including climate change, marine ecology and natural resource management, and illegal deforestation) and energy sustainability, and will add population health in the third year. Research in advanced visual data analytics to support decisions will be pervasive in all areas. The cross fertilization between disciplines focused on decision making will prepare students to make discoveries in the domain sciences and will lead to innovations in visual data analytics. To develop research skills in new contexts and to diversify career perspectives, trainees will have summer externships at non-academic partners such as IBM Research, Brookhaven National Laboratory, and the National Marine Fisheries Service, with new partners being added each year. The program comprises three major components: 1) a set of specially designed courses on decision support, spatial data analysis, visualization, and communication required for all students; 2) training in a STEM domain discipline; and 3) a set of non-course-based program elements in which all students will participate, including recruitment, skill development, professional development, and personal development. In addition to degrees in their domain-science disciplines, students will receive a graduate certificate from STRIDE after completing the three proposed courses and program activities. Specific innovations to be tested by rigorous evaluation include the seminar course in scientific decision support that will feature many government/industry scientists, decision makers, and journalists remotely leading discussions on the science, societal, and other challenges associated with decision support in their respective fields. Another new course focusing on science communication for decision makers will be provided by the Alan Alda Center for Communicating Science in the School of Journalism.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.
这项授予纽约州立大学石溪分校的国家科学基金会研究实习生(NRT)奖将为科学和工程研究生提供独特的跨学科技能,以协助并最终领导将复杂的数据支持研究转化为明智的决策和健全的政策。在工业和政府的所有部门中,有效的决策取决于科学家以支持决策过程的方式解释数据和传达结果的能力。这个新的培训项目通过跨学科的新课程和一系列活动来应对这一挑战,这些活动以“科学培训和研究为决策提供信息”(STRIDE)为主题。它将特别包括很少明确教授的决策支持技能,例如理解利益相关者的观点、科学传播和翻译科学不确定性。该项目预计将培养90名博士生,其中包括至少20名受资助的博士生,以及类似数量的非博士生,他们将参与大气与海洋科学、生态与进化、计算机科学、生物医学信息学、应用数学与统计、新闻学和高级计算科学等部门的项目组成部分。STRIDE最初将以环境可持续性(包括气候变化、海洋生态和自然资源管理以及非法砍伐森林)和能源可持续性为目标,并将在第三年增加人口健康。支持决策的高级可视化数据分析研究将在所有领域普及。专注于决策制定的学科之间的交叉施肥将使学生在领域科学中做出发现,并将导致视觉数据分析的创新。为了在新的环境中发展研究技能,并使职业前景多样化,学员将在IBM研究院、布鲁克海文国家实验室和国家海洋渔业局等非学术合作伙伴进行暑期实习,每年都会增加新的合作伙伴。该计划包括三个主要部分:1)一套专门设计的课程,包括决策支持、空间数据分析、可视化和沟通,所有学生都需要;2) STEM领域学科的培训;3)所有学生都将参与的一套非课程项目元素,包括招聘、技能发展、专业发展和个人发展。除了在他们的领域科学学科的学位,学生将在完成三个建议的课程和项目活动后获得STRIDE的研究生证书。通过严格评估测试的具体创新包括科学决策支持的研讨会课程,该课程将由许多政府/行业科学家、决策者和记者远程领导讨论,讨论与各自领域的决策支持相关的科学、社会和其他挑战。新闻学院的艾伦·阿尔达传播科学中心将开设另一门新课程,重点关注决策者的科学传播。美国国家科学基金会研究实习生(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式,培训项目致力于在高优先级跨学科研究领域对STEM研究生进行有效培训。
项目成果
期刊论文数量(21)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Forests, Coca, and Conflict: Grass Frontier Dynamics and Deforestation in the Amazon-Andes
森林、古柯和冲突:亚马逊-安第斯山脉的草地边界动态和森林砍伐
- DOI:10.31389/jied.87
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Davalos, Liliana M.;Davalos, Eleonora;Holmes, Jennifer;Tucker, Clara;Armenteras, Dolors
- 通讯作者:Armenteras, Dolors
Opposition Support and the Experience of Violence Explain the Colombian Peace Referendum. Journal of Politics in Latin America 10(2): 99-122
反对派支持和暴力经历解释哥伦比亚和平公投。
- DOI:
- 发表时间:2018
- 期刊:
- 影响因子:1.3
- 作者:Dávalos, E.
- 通讯作者:Dávalos, E.
Spatial autocorrelation reduces model precision and predictive power in deforestation analyses
- DOI:10.1002/ecs2.1824
- 发表时间:2017-05-01
- 期刊:
- 影响因子:2.7
- 作者:Mets, Kristjan D.;Armenteras, Dolors;Davalos, Liliana M.
- 通讯作者:Davalos, Liliana M.
Perceptions of Barriers to Career Progression for Academic Women in STEM
- DOI:10.3390/soc11020027
- 发表时间:2021-06-01
- 期刊:
- 影响因子:2.1
- 作者:O'Connell, Christine;McKinnon, Merryn
- 通讯作者:McKinnon, Merryn
Perceptions of stereotypes applied to women who publicly communicate their STEM work
- DOI:10.1057/s41599-020-00654-0
- 发表时间:2020-12-25
- 期刊:
- 影响因子:0
- 作者:McKinnon, Merryn;O'Connell, Christine
- 通讯作者:O'Connell, Christine
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Robert Harrison其他文献
LEVOSIMENDAN REDUCES MORTALITY IN PATIENTS WITH REDUCED EJECTION FRACTION UNDERGOING CARDIAC SURGERY: A META-ANALYSIS OF RANDOMIZED CLINICAL TRIALS
- DOI:
10.1016/s0735-1097(12)60960-7 - 发表时间:
2012-03-27 - 期刊:
- 影响因子:
- 作者:
Robert Harrison;Victor Hasselblad;Ricardo Levin;Rajendra Mehta;Robert Harrington;John Alexander - 通讯作者:
John Alexander
Detection of pion-induced radioactivity by autoradiography and positron emission tomography.
通过放射自显影和正电子发射断层扫描检测π介子诱发的放射性。
- DOI:
10.1118/1.596426 - 发表时间:
1989 - 期刊:
- 影响因子:3.8
- 作者:
Hiroki Shirato;Robert Harrison;R. O. Kornelsen;Gabriel K. Y. Lam;Cristopher C. Gaffney;George B. Goodman;Ed Grochowski;Brian Pate - 通讯作者:
Brian Pate
Cancer Risk in the Semiconductor Industry: A Call for Action
半导体行业的癌症风险:呼吁采取行动
- DOI:
10.1179/107735202800338948 - 发表时间:
2002 - 期刊:
- 影响因子:0
- 作者:
J. Bailar;M. Greenberg;Robert Harrison;J. LaDou;E. Richter;A. Watterson - 通讯作者:
A. Watterson
The in vitro identification of dimethyltryptamine (DMT) in mammalian brain and its characterization as a possible endogenous neuroregulatory agent.
哺乳动物大脑中二甲基色胺 (DMT) 的体外鉴定及其作为可能的内源性神经调节剂的表征。
- DOI:
- 发表时间:
1977 - 期刊:
- 影响因子:0
- 作者:
Samuel T. Christian;Robert Harrison;Elizabeth Quayle;J. Pagel;John A. Monti - 通讯作者:
John A. Monti
Performance On Guideline Directed Medical Therapy Remains Low In A Cluster-randomized Trial: Results From CONNECT-HF
- DOI:
10.1016/j.cardfail.2022.03.114 - 发表时间:
2022-04-01 - 期刊:
- 影响因子:8.200
- 作者:
Bradi Granger;Adam Devore;Lisa Kaltenbach;Gregg Fonarow;Hussein Al-Khalidi;Nancy Albert;Eldrin Lewis;Javed Butler;Ileana Pina;Paul Heidenreich;Larry Allen;Clyde Yancy;Lauren Cooper;Michael Felker;Andrew McRae;David Lanfear;Robert Harrison;Maghee Disch;Dan Ariely;Julie Miller;Adrian Hernandez - 通讯作者:
Adrian Hernandez
Robert Harrison的其他文献
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{{ truncateString('Robert Harrison', 18)}}的其他基金
MRI: Acquisition of a computer system for Research and Education – Seawulf
MRI:购买用于研究和教育的计算机系统 – Seawulf
- 批准号:
2215987 - 财政年份:2022
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
Collaborative Research: Frameworks: Production quality Ecosystem for Programming and Executing eXtreme-scale Applications (EPEXA)
合作研究:框架:用于编程和执行超大规模应用程序的生产质量生态系统 (EPEXA)
- 批准号:
1931387 - 财政年份:2019
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
Category II : Ookami: A high-productivity path to frontiers of scientific discovery enabled by exascale system technologies
第二类:Ookami:通过百亿亿次系统技术实现科学发现前沿的高生产力之路
- 批准号:
1927880 - 财政年份:2019
- 资助金额:
$ 299.39万 - 项目类别:
Cooperative Agreement
SPX: Collaborative Research: Dependence Programming and Optimization of Scalable Irregular Numerical Applications
SPX:协作研究:可扩展不规则数值应用的依赖编程和优化
- 批准号:
1725428 - 财政年份:2017
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
MRI: Acquisition of SeaWulf - A Reconfigurable Computer System for Research and Education
MRI:收购 SeaWulf - 用于研究和教育的可重构计算机系统
- 批准号:
1531492 - 财政年份:2015
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
Collaborative Research: SI2-SSI: Task-Based Environment for Scientific Simulation at Extreme Scale (TESSE)
合作研究:SI2-SSI:基于任务的超大规模科学模拟环境 (TESSE)
- 批准号:
1450344 - 财政年份:2015
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
Novel immuno-proteomic strategies to develop a polyspecific, non-cold chain liquid snake antivenom with unparalleled sub-Saharan African efficacy
新型免疫蛋白质组学策略,用于开发具有无与伦比的撒哈拉以南非洲功效的多特异性、非冷链液体蛇抗蛇毒血清
- 批准号:
MR/L01839X/1 - 财政年份:2014
- 资助金额:
$ 299.39万 - 项目类别:
Research Grant
Scientific Software Innovation Institute for Computational Chemistry and Materials Modeling (S2I2C2M2) Software Summer School
计算化学与材料建模科学软件创新研究院(S2I2C2M2)软件暑期学校
- 批准号:
1450986 - 财政年份:2014
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
Knowledge Driven Configurable Manufacturing (KDCM)
知识驱动的可配置制造(KDCM)
- 批准号:
EP/K018191/1 - 财政年份:2013
- 资助金额:
$ 299.39万 - 项目类别:
Research Grant
Collaborative Research: A Scientific Software Innovation Institute for Computational Chemistry and Materials Modeling (S2I2C2M2)
合作研究:计算化学和材料建模科学软件创新研究所(S2I2C2M2)
- 批准号:
1341315 - 财政年份:2012
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
相似海外基金
Collaborative Research: NRT-DESE: Interdisciplinary Research Traineeships in Data-Enabled Science and Engineering of Atomic Structure
合作研究:NRT-DESE:数据支持的原子结构科学与工程跨学科研究实习
- 批准号:
1633094 - 财政年份:2016
- 资助金额:
$ 299.39万 - 项目类别:
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NRT-DESE: Network Biology: From Data to Information to Insights
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- 批准号:
1632976 - 财政年份:2016
- 资助金额:
$ 299.39万 - 项目类别:
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NRT-DESE: Data Intensive Research Enabling Clean Technologies (DIRECT)
NRT-DESE:数据密集型研究支持清洁技术(直接)
- 批准号:
1633216 - 财政年份:2016
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
NRT-DESE: Team Science for Integrative Graduate Training in Data Science and Physical Science
NRT-DESE:数据科学和物理科学研究生综合培训的团队科学
- 批准号:
1633631 - 财政年份:2016
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
NRT-DESE: NRT in Integrated Computational Entomology (NICE)
NRT-DESE:综合计算昆虫学 (NICE) 中的 NRT
- 批准号:
1631776 - 财政年份:2016
- 资助金额:
$ 299.39万 - 项目类别:
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NRT-DESE: Preparing Resilient and Operationally Adaptive Communities through an Interdisciplinary, Venture-based Education (PROACTIVE)
NRT-DESE:通过跨学科、基于风险的教育(主动)打造有弹性和适应性强的社区
- 批准号:
1633608 - 财政年份:2016
- 资助金额:
$ 299.39万 - 项目类别:
Standard Grant
NRT-DESE Intelligent Adaptive Systems: Training computational and data-analytic skills for academia and industry
NRT-DESE 智能自适应系统:为学术界和工业界培训计算和数据分析技能
- 批准号:
1633722 - 财政年份:2016
- 资助金额:
$ 299.39万 - 项目类别:
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Collaborative Research: NRT-DESE: Interdisciplinary Research Traineeships in Data-Enabled Science and Engineering of Atomic Structure
合作研究:NRT-DESE:数据支持的原子结构科学与工程跨学科研究实习
- 批准号:
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- 资助金额:
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NRT-DESE: Graduate Training in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms
NRT-DESE:人类行为及其认知和神经机制的数据支持研究研究生培训
- 批准号:
1449828 - 财政年份:2015
- 资助金额:
$ 299.39万 - 项目类别:
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NRT-DESE: Training in Data-Driven Discovery - From the Earth and the Universe to the Successful Careers of the Future
NRT-DESE:数据驱动发现培训 - 从地球和宇宙到未来成功的职业生涯
- 批准号:
1450006 - 财政年份:2015
- 资助金额:
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