SI2-SSE: Collaborative Research: A Sustainable Future for the Glue Multi-Dimensional Linked Data Visualization Package

GLue是一个免费的开源应用程序，允许科学家和数据科学家探索相关数据集内部和之间的关系。使用GLUE可以轻松创建各种可视化数据(如散点图、条形图、图像)，包括三维视图。Glue的独特之处在于它能够将数据集连接在一起，而不是将它们合并为一个。因此，例如，两个基于地球的地图数据集可以通过使用坐标(例如，纬度和经度)来连接并联合可视化，以将地图粘合在一起，从而当用户选择(例如，使用套索工具)一个数据集中的区域时，对应的所选数据子集将同时在所有相关可视化中突出显示。这些链接的视图“在各种不同的情节类型中尤其强大。例如，如果对空中交通管制感兴趣的用户将具有关于所有飞机的3D位置的信息的数据集粘合到提供天气信息的第二数据集，则该用户可以进行选择组合，以(在地图上、3D视图中或任何其他显示中)高亮显示可能在特定时间段内发生雷暴的特定高度的飞机。特别是，Glue使用户可以轻松地创建他们自己的可视化类型，这一点很重要，因为不同的学科通常需要非常专门的方式来查看数据。该软件已经被广泛应用于几个学科，特别是天文学和医学，这两个学科已经进行了专门的优化。该项目将增加新的功能，使GLUE在更多的科学领域(如生物信息学、流行病学)中更有用，这些领域需要链接视图可视化，并使其更容易作为一种教育工具使用。此外，该项目将培训新的用户和开发人员，他们将把Glue扩展为更可持续的社区工作。GLUE是一个开源包，它允许科学家探索相关数据集内部和之间的关系，使他们能够轻松地进行数据集的多维链接可视化，以交互方式或编程方式在1、2或3维中选择数据子集，并查看这些选择在所有开放的数据可视化(如图形、地图、诊断图表)中实时传播。GLUE的一个独特功能是，使用用户定义的数据组件集之间的数学关系，可以将来自不同来源的数据集相互链接，这使得可以跨数据集执行选择。GLUE是用Python语言编写的，是为多学科工作从头开始设计的，目前它正在帮助研究人员在地球科学、基因组学、天文学和医学方面取得发现。它还提供了对来自学术界以外的数据的见解，包括政府和城市提供的公开数据。为了长期可持续，胶水开发需要成为社区驱动的努力。通过教程和开发人员研讨会、编码冲刺以及与几个学科的研究人员和经验丰富的开源开发人员的战略合作，GLUE团队将通过开发在特定研究领域有用的新功能来帮助用户社区扩展GLUE。该团队将帮助用户将最需要的功能贡献给GLUE，并将招募积极的贡献者参与核心GLUE的开发。随着社区的发展，GLUE开发将被引导专注于对广泛的研究社区有用的几个主要功能，包括：支持超大型数据集，支持在浏览器中完全运行GLUE(在Jupyter笔记本和Jupyter实验室内)，以及改进与第三方工具的互操作性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持

Glue is a free and open-source application that allows scientists and data scientists to explore relationships within and across related datasets. Glue makes it easy create a wide variety of visualizations (such as scatter plots, bar charts, images) of data, including three dimensional views. What makes Glue unique is its ability to connect datasets together, without merging them into one. Thus, for example, two Earth-based mapping data sets may be connected and jointly visualized by using the coordinates (e.g. latitude and longitude) to glue the maps together, so that when a user selects (e.g. with a lasso tool) regions in one data set, the corresponding selected subset of data will highlight in all related visualizations simultaneously. These ?linked views" are especially powerful across wide varieties of plot types. For example, if a user interested in air traffic control glues a data set with information about the 3D locations of all airplanes to a second data set giving weather information, that user could make a combination of selections that would highlight (on maps, in 3D views, or any other display) planes at particular altitudes where thunderstorms might be likely to occur within a specific period of time. In particular, Glue makes it easy for users to create their own kinds of visualizations, which is important because different disciplines often need very specialized ways of looking at data. The software is already being used widely across several disciplines, in particular, astronomy and medicine, for which has been specially optimized. This project will add new features to make Glue more useful in more fields of science (e.g. bioinformatics, epidemiology) where there is demand for linked-view visualization, as well as making it more accessible as an educational tool. In addition, this project will train new users and developers, who will expand Glue into a much more sustainable community effort. Glue is an open-source package that allows scientists to explore relationships within and across related datasets, by making it easy for them to make multi-dimensional linked visualizations of datasets, select subsets of data interactively or programmatically in 1, 2, or 3 dimensions, and see those selections propagate live across all open visualizations of the data (e.g. graphs, maps, diagnostics charts). A unique feature of glue is that datasets from different sources can be linked to each other, using user-defined mathematical relationships between sets of data components, which makes it possible to carry out selections across datasets. Glue, written in Python, is designed from the ground-up for multidisciplinary work, and it is currently helping researchers make discoveries in geoscience, genomics, astronomy, and medicine. It is also giving insights into data from outside academia, including open data provided by governments and cities. To become sustainable in the long term, glue development needs to become a community-driven effort. Through tutorial and developer workshops, coding sprints, and strategic collaborations with researchers in several disciplines and experienced open source developers, the glue team will help user communities extend glue by developing new functionality useful within particular fields of research. The team will help users contribute the most widely-needed functionality back to glue, and will recruit active contributors to participate in core glue development. As the community grows, glue development will be guided to focus on several major features useful to the broad research community, including: support for very large datasets, support for running glue fully in the browser (inside Jupyter notebooks and Jupyter Lab), and improved interoperability with third-party tools.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