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

就像几乎所有领域的科学家一样，天文学家也在“数据洪流”下苦苦挣扎。虽然最新的天文台获得的越来越大的图像导致了尖端科学的发展，但它们也压倒了传统的工具，这些工具的设计目的是处理比最先进的文件小数百或数千倍的文件。特别是，天文学家们正在迅速失去简单地观察从望远镜中发出的天空图像的能力。FastTract项目将通过将AAS全球望远镜软件系统中现有的基于web的可视化技术与有效处理2020年大型天文图像所需的新功能和工具相结合来解决这一问题，使美国天文学家能够充分利用他们的世界级设施，特别是那些打开宇宙新窗口的设施。项目团队将综合在这项工作中获得的见解，建立小大数据大学（LBDU），为那些在“数据洪流”中挣扎的跨学科科学家提供学习资源。LBDU将遵循可汗学院的模式，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，例如小型机构和感兴趣的非专业人员。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成为可能。cii将建立在nsf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，使其与fits数据文件一起工作，开发轻松创建此类平铺所需的工具，并生成工作流和教程，允许广泛的天文社区拥有基础设施。该团队将与三个国家科学基金会资助的合作伙伴合作，研究具体的科学应用。年度研讨会将形成一个由项目用户-贡献者组成的核心小组，并确保开发工作满足更广泛社区的需要。该项目将采用开源、开放开发模式的最佳实践，为面临类似数据挑战的非天文社区利用FastTract基础设施奠定基础。与此同时，该团队将提炼其专业知识，创建小大数据大学（LBDU），这是一个面向不打算成为CI专家的领域科学家的在线专业发展资源。这些科学家将通过经验上成功的可汗学院模型学习处理越来越大的数据集的核心策略。遥测分析和焦点小组将指导课程设计和评估效果。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。

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.