Ideas Labs: Data-Intensive Research in Science and Engineering

创意实验室：科学与工程领域的数据密集型研究

• 批准号: 1923632
• 负责人: Suzanne Weekes
• 金额: $ 99.82万
• 依托单位: Society For Industrial and Applied Math (SIAM)
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923632&HistoricalAwards=false
• 关键词: Ideas; Labs; Data; Intensive; Research

In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering. The Big Ideas represent unique opportunities to position our Nation at the cutting edge of global science and engineering leadership by bringing together diverse disciplinary perspectives to support convergence research. NSF's Harnessing the Data Revolution (HDR) Big Idea is a national-scale activity to enable new modes of data-driven discovery that will allow fundamental questions to be asked and answered at the frontiers of science and engineering. This project describes a series of Ideas Labs on "Data-Intensive Research in Science and Engineering (DIRSE)". Ideas Labs are intensive workshops focused on finding innovative and bold transdisciplinary solutions to grand challenge problems. The overarching goal of the DIRSE Ideas Labs is to foster convergent approaches to enable data-intensive research in science and engineering through a series of facilitated activities bringing together scientists and engineers working on important data-intensive science and engineering problems with data scientists.There are numerous science and engineering challenges that require, or will soon require, data science to help address research and technological questions. Advancing knowledge in these areas requires solutions to many modeling and data challenges such as real-time sensing, learning, and decision making; social, political, and behavioral implications of machine learning and impacts of new data uses; issues related to ethics and fairness; and integrating heterogeneous data for explaining or predicting complex phenomena. There is also a need for approaches that combine physical models with data driven models for learning and decision making. Data science tools, such as signal and image processing, visualization, statistical modeling and inference, machine learning, and optimization, offer a starting point for solving important scientific and engineering challenges. However, extracting new information and knowledge from data will benefit from new, convergent strategies that capitalize on existing NSF investments in data and cyberinfrastructure and that build synergy between the researchers with expertise in the generation or measurement of data and those with expertise in processing and analyzing that data.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.

2016年，美国国家科学基金会（NSF）公布了一系列“大创意”，这是10个大胆的长期研究和过程创意，旨在确定未来在科学和工程前沿的投资领域。 大创意代表了独特的机会，通过汇集不同的学科观点来支持融合研究，将我们的国家定位在全球科学和工程领导的前沿。 NSF的利用数据革命（HDR）大创意是一项全国性的活动，旨在实现数据驱动发现的新模式，从而允许在科学和工程的前沿提出和回答基本问题。 该项目描述了一系列关于“科学与工程中的数据密集型研究（DIRSE）”的想法实验室。 Ideas Labs是一个密集的研讨会，专注于为重大挑战问题寻找创新和大胆的跨学科解决方案。 DIRSE Ideas Labs的总体目标是通过一系列促进活动，将致力于重要数据密集型科学和工程问题的科学家和工程师与数据科学家聚集在一起，促进科学和工程领域的数据密集型研究。有许多科学和工程挑战需要或即将需要数据科学来帮助解决研究和技术问题。 推进这些领域的知识需要解决许多建模和数据挑战，例如实时感知，学习和决策;机器学习的社会，政治和行为影响以及新数据使用的影响;与道德和公平相关的问题;以及整合异构数据以解释或预测复杂现象。 还需要将联合收割机物理模型与数据驱动模型相结合以进行学习和决策的方法。 数据科学工具，如信号和图像处理、可视化、统计建模和推理、机器学习和优化，为解决重要的科学和工程挑战提供了起点。 然而，从数据中提取新的信息和知识将受益于新的，融合战略，利用现有的NSF在数据和网络基础设施的投资，并建立协同作用的研究人员之间的专业知识，在生成或测量数据和那些具有专业知识的处理和分析该数据。这一奖项反映了NSF的法定使命，并已被认为是值得支持，通过评估使用基金会的学术价值和更广泛的影响审查标准。