Collaborative Research: HCC: Medium: RUI: Intelligent support for non-experts to navigate large information spaces

协作研究：HCC：中：RUI：为非专家导航大型信息空间提供智能支持

• 批准号: 2106896
• 负责人: Ryan Fisher
• 金额: $ 21.3万
• 依托单位: Christopher Newport University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106896&HistoricalAwards=false
• 关键词: Collaborative; Research; HCC; Medium; RUI

This project will build our understanding of how to enable non-expert volunteers in a citizen-science project to contribute to analyses of large volumes of data by searching for potentially causal relations. The increasing use of automated scientific-data-collection instruments has led to an explosion in the amount of scientific data collected, challenging the ability of scientists to analyze them. Volunteers have less background knowledge than experts about the purpose, context, content, provenance and processes associated with the data. A system that provides such background knowledge will enable non-experts to make sense of the data. The research plan also includes building system support to augment the capabilities of the volunteers, for example by searching for related data and by performing causal inference in conjunction with volunteers. Citizen-science projects provide a vehicle to disseminate scientific practice, knowledge and findings to the general public to increase awareness and understanding of the practices and techniques of data-intensive science. Findings should be directly applicable to the target context of involving citizen-science volunteers in navigating and analyzing large quantities of science data and generalize to other settings with big data. In this research, volunteers classify noise events (glitches) produced by the Laser Interferometer Gravitational-wave Observatory (LIGO). Along with glitches observed in the main Gravitational Wave channel, the detectors record around 400,000 auxiliary channels of data that may provide information about the origins of the glitch. The research will test hypotheses about the kind of additional information needed to enable non-experts to productively navigate this large dynamic dataset to find related information and will develop processes, techniques and tools to allow the volunteers to manage and efficiently process the data. It will develop our understanding of how and when to introduce which different types of background knowledge about the data to enable non-experts to work on a task, such as by providing maps and visualizations of particular data and relationships at the time they are most needed in the volunteers' work process. The gravitational physics and astronomy communities will directly benefit from advances in LIGO detector characterization, data quality vetoes and hence signal searches.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.

该项目将帮助我们了解如何让公民科学项目中的非专家志愿者通过寻找潜在的因果关系来分析大量数据。自动化科学数据收集仪器的使用越来越多，导致收集的科学数据量激增，对科学家分析数据的能力提出了挑战。与专家相比，志愿者对与数据相关的目的、背景、内容、来源和流程的背景知识较少。提供此类背景知识的系统将使非专家能够理解数据。该研究计划还包括建立系统支持以增强志愿者的能力，例如通过搜索相关数据并与志愿者一起进行因果推理。公民科学项目提供了向公众传播科学实践、知识和发现的工具，以提高对数据密集型科学实践和技术的认识和理解。研究结果应直接适用于让公民科学志愿者参与导航和分析大量科学数据的目标环境，并推广到其他大数据环境。在这项研究中，志愿者对激光干涉引力波天文台 (LIGO) 产生的噪声事件（毛刺）进行分类。除了在主引力波通道中观察到的故障之外，探测器还记录了大约 400,000 个辅助通道的数据，这些数据可能提供有关故障起源的信息。该研究将测试关于使非专家能够有效地浏览这个大型动态数据集以查找相关信息所需的附加信息类型的假设，并将开发流程、技术和工具以允许志愿者管理和有效处理数据。它将加深我们对如何以及何时引入有关数据的不同类型背景知识的理解，以使非专家能够完成任务，例如在最需要时提供特定数据和关系的地图和可视化在志愿者的工作过程中。引力物理学和天文学界将直接受益于 LIGO 探测器表征、数据质量否决以及信号搜索方面的进步。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。