Elements: FastTract: Web-Based Exploratory Visualization of Gigapixel Astronomical Images

元素：FastTract：基于 Web 的十亿像素天文图像探索性可视化

• 批准号: 2004840
• 负责人: Peter Williams
• 金额: $ 43.13万
• 依托单位: American Astronomical Society
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004840&HistoricalAwards=false
• 关键词: Elements; FastTract; Web; Based; Exploratory

Like scientists in virtually every field, astronomers are struggling under a"data deluge". While the ever-larger images obtained by the newestobservatories lead to cutting-edge science, they also overwhelm traditionaltools designed to work with files hundreds or thousands of times smallerthan the state-of-the-art. In particular, astronomers are rapidly losing theability to simply look at the images of the sky that are coming out of theirtelescopes. The FastTract Project will solve this problem by marrying existingWeb-based visualization technologies in the AAS WorldWide Telescope softwaresystem with new features and tools needed to efficiently work with the largeastronomical images of the 2020's, enabling US astronomers to fully exploittheir world-class facilities, in particular the ones that open new Windows onthe Universe. The project team will synthesize the insight gained in thisundertaking to establish the Little Big Data University (LBDU), a learningresource for scientists across disciplines who are struggling to stay afloatin the "data deluge". LBDU will follow the Khan Academy model and extensivelyuse interactive environments to help researchers and others learn strategiesfor Harnessing the Data Revolution. These efforts will especially benefitpeople who do not have access to top-tier computational resources, such asthose at small institutions and interested non-specialists.The FastTract Project will create a sustainable cyberinfrastructure (CI)system and associated community of practice that enable exploratory scientificvisualization of large (gigapixel+) astronomical images over the Web. The CIwill build on the NSF-funded AAS WorldWide Telescope (WWT) software system.The research team will extend WWT's image tiling architecture to work withFITS data files, develop the tooling needed for easy creation of such tiles,and produce workflows and tutorials allowing the broad astronomical communityto take ownership of the infrastructure. The team will work with threeNSF-funded partners on specific science applications. Annual workshops willnucleate a core group of project user-contributors and ensure that developmenteffort meets the needs of the broader community. The project will adopt bestpractices in the open-source, open-development paradigm, laying the groundworkfor FastTract infrastructure to be leveraged by non-astronomical communitiesfacing similar data challenges. In parallel, the team will distill itsexpertise to create the Little Big Data University (LBDU), an onlineprofessional development resource aimed at domain scientists who do not intendto become CI specialists. These scientists will learn core strategies forcoping with ever-larger data sets through the empirically successful KhanAcademy model. Telemetry analysis and focus groups will guide curriculumdesign and assess efficacy.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.

就像几乎所有领域的科学家一样，天文学家也在“数据洪流”中挣扎。虽然最新天文台获得的越来越大的图像带来了尖端科学，但它们也压倒了传统的工具，这些工具旨在处理比最先进的文件小几百倍或几千倍的文件。特别是，天文学家正在迅速失去简单地观察望远镜发出的天空图像的能力。FastTract项目将通过将AAS WorldWide Telescope软件中现有的基于Web的可视化技术与有效处理2020年代大型天文图像所需的新功能和工具相结合来解决这个问题，使美国天文学家能够充分利用他们的世界级设施，特别是那些打开新的宇宙窗口的设施。该项目团队将综合在这一过程中获得的洞察力，建立小大数据大学（LBDU），为那些努力在“数据洪流”中生存的跨学科科学家提供学习资源。LBDU将遵循Khan Academy的模式，并广泛使用交互式环境来帮助研究人员和其他人学习利用数据革命的策略。这些努力将特别有利于那些无法获得顶级计算资源的人，比如那些在小型机构工作的人和感兴趣的非专业人士。FastTract项目将创建一个可持续的网络基础设施（CI）系统和相关的实践社区，使大型（十亿像素以上）天文图像的探索性科学可视化能够在Web上实现。CI将建立在美国国家科学基金会资助的AAS全球望远镜（WWT）软件系统的基础上。研究团队将扩展WWT的图像平铺架构，以使用FITS数据文件，开发轻松创建此类平铺所需的工具，并制作工作流程和教程，使广大天文学界能够拥有基础设施。该团队将与三个NSF资助的合作伙伴在特定的科学应用上合作。年度研讨会将培养一批核心的项目用户贡献者，并确保开发成果满足更广泛社区的需求。该项目将采用开源、开放式开发模式中的最佳实践，为FastTract基础设施奠定基础，供面临类似数据挑战的非天文学社区使用。与此同时，该团队将提取其专业知识，创建小大数据大学（LBDU），这是一个在线专业发展资源，旨在帮助那些不打算成为CI专家的领域科学家。这些科学家将通过经验上成功的KhanAcademy模型学习应对越来越大的数据集的核心策略。该奖项反映了NSF的法定使命，并被认为值得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来支持。