Ideas Labs: Data-Intensive Research in Science and Engineering

2016年，美国国家科学基金会（NSF）公布了一系列“大创意”，其中包括10项大胆的长期研究和流程创意，旨在确定科学和工程前沿的未来投资领域。大创意代表了独特的机会，通过汇集不同学科的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。美国国家科学基金会的“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“科学与工程数据密集型研究（DIRSE）”的想法实验室。创意实验室是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的密集研讨会。DIRSE Ideas Labs的总体目标是通过一系列便利的活动，将科学家和工程师聚集在一起，与数据科学家一起研究重要的数据密集型科学和工程问题，从而促进科学和工程数据密集型研究的融合方法。有许多科学和工程挑战需要或将很快需要数据科学来帮助解决研究和技术问题。在这些领域推进知识需要解决许多建模和数据挑战，如实时传感，学习和决策；机器学习对社会、政治和行为的影响以及新数据使用的影响；与道德和公平有关的问题；整合异构数据来解释或预测复杂现象。还需要将物理模型与用于学习和决策的数据驱动模型相结合的方法。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一个起点。然而，从数据中提取新的信息和知识将受益于新的、融合的战略，这些战略将利用现有的NSF在数据和网络基础设施方面的投资，并在具有数据生成或测量专业知识的研究人员与具有数据处理和分析专业知识的研究人员之间建立协同作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.