Collaborative Research: Elements: Towards A Scalable Infrastructure for Archival and Reproducible Scientific Visualizations

今天的科学围绕着前沿数据集-科学家需要仔细分析的数据，以便他们可以得出可靠的科学结论。这些前沿数据集变得越来越大、越来越复杂的速度每天都在加快。在许多方面，访问数据集并不等于，甚至不接近，访问数据集中的见解。这种细微但关键的可访问性差异为科学结果的可重复性创造了深刻的障碍。为此，《科学》杂志2010年出版的《可复制的可再现研究》提出了一个可再现研究的系统。 不幸的是，十年后，可获得的可重复研究仍处于起步阶段。事实证明，这个障碍比以前认为的要根本得多，尽管表面上看起来可以通过投入资源和制定指导方针和政策来解决。真实的挑战是，原始科学家团队的计算工具集、工作环境和工作流程很难被另一个科学家团队精确地重新创建。这种困难源于计算技术的快速发展;因此以实际的方式冻结计算环境几乎是不可能的。此外，科学直觉很难编纂，简单地记录一个新想法并不足以传达科学家在追求这个想法之前所看到的东西。从这方面来说，要使可获得的可复制研究成为现实，就需要更好的方法和工具。在这个项目中，研究人员将专注于数据分析的可视化步骤，这是科学发现的核心组成部分。该项目的目标是开发可再现交互式可视化（AIRIV）的可重用基础设施。通过这个基础设施，研究人员将演示如何可靠地捕获、高效存储、轻松共享并由任何用户自由重用大型复杂数据的视觉探索。该项目将提高可重复性研究的可及性，促进科学进步。对于医学和制药研究等领域，该项目将提供一个前所未有的渠道，以加速转化研究和促进国民健康。该项目将建立在由先前NSF CISE研究基础设施奖资助的研究基础上。在之前的项目中，研究人员发现了一种方法来捕获可视化工具的交互式用户体验，并在不需要共享原始软件或原始数据的情况下共享捕获的体验。此外，在重复使用捕获的体验期间，用户可以自由地探索先前用户如何使用称为Loom的方法使用该工具的确切顺序。在这个创建AIRIV的新项目中，研究人员将专注于基于Web的可视化仪表板，这代表了世界各地科学家与数据交互并获得见解的标准方式。该项目将首先构建一个通用的AIRIV Javascript库，可以由任何基于Web浏览器的应用程序导入。使用AIRIV库，基于Web的可视化仪表板的开发人员可以轻松地在仪表板中实现自动生成Loom对象。开发人员也将能够使用Loom对象来测试他们的应用程序以存储新的出处信息。然后，研究人员将进行性能和扩展测试，以了解在本地，机构集群和社区共享数据基础设施的设置下托管选择之间的权衡。科学设施的运营商可以利用这些发现来帮助科学界就在哪里以及如何托管科学可视化档案做出明智的选择，以提高共享能力和成本效益。研究人员还将开发机器学习方法，可以比较Loom对象，并将整个Loom对象档案中的共性和模式具体化。这种新方法将导致创建一个搜索的例子功能的AIRIV档案。对于需求收集、持续改进和部署测试，研究人员将与马约诊所伊利诺伊联盟合作，该联盟是医疗保健领域多种技术的框架，其中许多技术围绕仪表板/分析工具的研发。&我们的目标是两个这样的分析工作，OmiX和KnowEnG，这两个都是在国家超级计算应用中心（NCSA）开发的。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。

Today’s science revolves around leading edge datasets – data that scientists need to carefully analyze so that they can draw reliable scientific conclusions. The rate at which these leading-edge datasets are becoming larger and more complex is accelerating every day. In many ways, having access to a dataset does not equal to, or even come close to, having access to the insights in the dataset. This nuanced but crucial difference in accessibility creates a deep barrier to making scientific results reproducible. To this end, “Accessible Reproducible Research”, published by Science in 2010, presented a system for reproducible research. A decade later, unfortunately, accessible reproducible research is still in its infancy. It turns out that this barrier is much more fundamental than previously believed, even though on the surface it seems solvable by investing resources and setting guidelines and policies. The real challenge is that the computing toolsets, the working environments, and the work processes of the original team of scientists are very difficult for a different team of scientists to recreate with precision. Such difficulty stems from the rapid speed at which computing technology is advancing; so that freezing a computing environment in a practical manner is nearly impossible. In addition, scientific intuition is difficult to codify, simply documenting a new idea is not enough to communicate what a scientist saw before pursuing that idea. From that respect, making accessible reproducible research a reality requires better methods and tools. In this project, the investigators will focus on the visualization step of data analysis, which is a central component of scientific discovery. This project’s aim is to develop an Archiving Infrastructure for Reproducible Interactive Visualization (AIRIV). Through this infrastructure, the investigators will demonstrate how visual explorations of large and complex data can be reliably captured, efficiently stored, easily shared, and freely reused by any user. This project will improve accessibility of reproducible research and promote the progress of science. For areas such as medicine and pharmaceutical research, this project will provide an unprecedented channel to accelerate translational research and advance the national health.This project will build upon research funded by a prior NSF CISE Research Infrastructure award. In that previous project, the investigators found a method to capture interactive user experience of visualization tools, and to share the captured experience without the need to share the original software or the original data. Furthermore, during the reuse of a captured experience, the user has freedom to explore beyond the exact sequence of how the previous user has used the tool with a method called Loom. In this new project to create AIRIV, the investigators will focus on web-based visualization dashboards, which represent the standard way for scientists around the world to interact with their data and derive insights. This project will first build a general AIRIV Javascript library that can be imported by any web browser-based application. Using the AIRIV library, developers of web-based visual dashboards can easily implement automatic generation of Loom objects into their dashboards. Developers will be able to instrument their applications to store new provenance information with Loom objects as well. The investigators will then conduct performance and scaling tests to understand the tradeoffs between hosting choices under settings of local, institutional clusters, and community shared data infrastructures. Operators of scientific facilities can use the findings to help science communities make informed choices as to where and how to host scientific visualization archives for better share-ability and cost efficiency. The investigators will also develop machine learning methods that can compare Loom objects and externalize commonalities and patterns in an entire archive of Loom objects. Such new methods will lead to creating a search by example functionality for AIRIV archives. For requirements collection, continuous improvement, and deployment testing, the investigators will engage the Mayo Clinic & Illinois Alliance, which serves as a framework for several technologies in healthcare, many of which center around the research and development of dashboard/analytical tools. We target two such analytics efforts, OmiX and KnowEnG, both of which are developed at National Center for Supercomputing Applications (NCSA).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.