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

这个项目将建立我们对如何使公民科学项目中的非专业志愿者通过搜索潜在的因果关系为大量数据的分析做出贡献的理解。越来越多地使用自动化科学数据收集仪器，导致收集的科学数据数量激增，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、来源和过程的背景知识较少。提供此类背景知识的系统将使非专家也能理解数据。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和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的实践和技术的认识和理解。研究结果应直接适用于涉及公民科学志愿者导航和分析大量科学数据的目标环境，并推广到其他具有大数据的环境。在这项研究中，志愿者对激光干涉仪引力波天文台（LIGO）产生的噪声事件（小故障）进行分类。除了在主引力波通道中观测到的小故障外，探测器还记录了大约40万个辅助通道的数据，这些数据可能提供有关小故障起源的信息。这项研究将测试关于额外信息的假设，这些信息可以使非专家有效地浏览这个庞大的动态数据集，找到相关信息，并将开发流程、技术和工具，使志愿者能够管理和有效地处理数据。它将发展我们对如何以及何时引入哪些不同类型的数据背景知识以使非专家能够完成任务的理解，例如在志愿者工作过程中最需要的时候提供特定数据和关系的地图和可视化。引力物理学和天文学界将直接受益于LIGO探测器特性、数据质量否决和信号搜索方面的进步。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。

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.