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

协作研究：HCC：中：为非专家导航大型信息空间提供智能支持

• 批准号: 2106865
• 负责人: Kevin Crowston
• 金额: $ 51.11万
• 依托单位: Syracuse 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=2106865&HistoricalAwards=false
• 关键词: Collaborative; Research; HCC; Medium; Intelligent

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.

这个项目将建立我们的理解，如何使非专家志愿者在公民科学项目，以有助于通过搜索潜在的因果关系，分析大量的数据。自动化科学数据收集工具的使用越来越多，导致收集的科学数据数量激增，对科学家分析这些数据的能力提出了挑战。志愿人员对数据的目的、背景、内容、来源和相关过程的背景知识少于专家。提供这种背景知识的系统将使非专家能够理解数据。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。公民科学项目为向公众传播科学实践、知识和研究结果提供了一个工具，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公民科学志愿者参与导航和分析大量科学数据的目标背景，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。沿着在主引力波通道中观察到的故障，探测器记录了大约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找到相关信息所需的额外信息的假设，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。它将发展我们对如何以及何时引入关于数据的不同类型的背景知识的理解，以使非专家能够完成任务，例如在志愿者的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。引力物理和天文学界将直接受益于LIGO探测器表征、数据质量否决和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。

项目成果

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

• DOI: 10.1088/1361-6382/ac1ccb
• 发表时间: 2021-03
• 期刊: Classical and Quantum Gravity
• 影响因子: 3.5
• 作者: S. Soni;C. Berry;S. Coughlin;M. Harandi;C. Jackson;Kevin Crowston;C. Osterlund;O. Patane;A. Katsaggelos;L. Trouille;V-G Baranowski;W. Domainko;K. Kamiński;M. A. L. Rodriguez;U. Marciniak;P. Nauta;G. Niklasch;R. Rote;B. T'egl'as;C. Unsworth;C. Zhang

Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

重力间谍志愿者对 O1、O2、O3a 和 O3b 观测运行中的 LIGO 故障进行分类

• DOI: 10.5281/zenodo.5911226
• 发表时间: 2022
• 期刊: Zenodo
• 作者: Zevin, Michael;Coughlin, Scott;Chase, Eve;Allen, Sara;Bahaadini, Sara;Berry, Christopher;Crowston, Kevin;Harandi, Mabi;Jackson, Corey;Kalogera, Vicky
• 通讯作者: Kalogera, Vicky

Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

引力间谍机器学习对观测运行 O1、O2、O3a 和 O3b 中的 LIGO 故障进行分类

• DOI: 10.5281/zenodo.5649211
• 发表时间: 2021
• 期刊: Zenodo
• 作者: Coughlin, Scott;Zevin, Michael;Bahaadini, Sara;Rohani, Neda;Allen, Sara;Berry, Christopher;Crowston, Kevin;Harandi, Mabi;Jackson, Corey;Kalogera, Vicky
• 通讯作者: Kalogera, Vicky

Artificial Intelligence in Information Systems: State of the Art and Research Roadmap

信息系统中的人工智能：最新技术和研究路线图

• DOI: 10.17705/1cais.05017
• 发表时间: 2022
• 期刊: Communications of the Association for Information Systems
• 影响因子: 2
• 作者: Ågerfalk, Pär J.;Conboy, Kieran;Crowston, Kevin;Eriksson Lundström, Jenny S.;Jarvenpaa, Sirkka;Ram, Sudha;Mikalef, Patrick
• 通讯作者: Mikalef, Patrick

Design Principles for Background Knowledge to Enhance Learning in Citizen Science

加强公民科学学习的背景知识设计原则

• 发表时间: 2023
• 期刊: vol 13972
• 作者: Crowston, K.;Jackson, C.;Corieri, I.;Østerlund, C.
• 通讯作者: Østerlund, C.