SGER: 2- and 3-D Visualization of Ecological Phenomena - Transition to the Petabyte Age

在PB时代，科学家面临的信息学问题有三个部分：1)查找、访问和加载海量数据；2)找出适当的数据简化或抽象以理解合并的数据集；以及3)对这些数据进行操作处理。在这个SGER中，焦点是这些子问题中的第二个？科学家如何认知地处理他们自己和其他人的PB大小的数据集？出处。当统计处理/机器学习应用于海量数据集时，自然语言处理应用程序得到了数量级的改进。一些语言学家观察到，这些改进在某种程度上是平稳的，只有在领域知识(在这种情况下，语言学理论)也应用于处理之后，才会出现后续的改进。科学界也有类似的情况。尽管拍字节时代的一些应用领域可能只需要非特定领域的机器学习来预测现象(克里斯·安德森称之为不可知论统计学？)，但其他应用领域将需要对大多数科学家寻求的现象有更深入的理解。换句话说，对于一些领域，在大多数科学中，答案是不够的？什么？？人们还需要回答？如何回答？这项工作风险很高，因为很少有研究表明，自然现象的可视化可以在多大程度上跨越不同的空间和时间尺度“认知地”协调一致；此外，由于科学和信息可视化社区之间的差异，尚不清楚如何将分析与生态现象的可视化联系起来。最后，提议的可视化所需的数据集成需要管理更大的数据量和更多不同类型的数据。拟议工作的智力价值在于科学可视化的新概念数据结构和数据表示，这些结构和数据表示可用于生成特定领域的可视化模板，从中可以绘制一个领域的一系列特定可视化。拟议工作的更广泛的影响有三个方面：1)超越环境科学和气候变化领域的工作可能应用到自然资源管理和政策领域，以及其他科学领域；2)长青学院课程开发方面对计算思维的教育影响，将通过NSF CPATH项目西北分布式计算机科学系(NWDCSD)传播；3)向科学界免费开放源码分发软件工具。

The informatics problem facing scientists in the Petabyte Age has three parts: 1) finding, accessing, and loading massive amounts of data, 2) figuring out the appropriate data reduction or abstraction to make sense of the combined data set, and 3) operationally processing those data. n this SGER, the focus is on the second of these sub-problems ? how scientists deal cognitively with petabyte size data sets of their own and others? provenance. Natural language processing applications saw orders of magnitude improvement when statistical processing/machine learning was applied to massive data sets. Some linguists observe that these improvements level off at some point, and that subsequent improvements come only after domain knowledge (in that case, linguistic theory) is also applied to the processing. There is a similar situation in science. While some application areas in a Petabyte Age might require only non domain-specific machine learning to predict phenomena (what Chris Anderson calls ?agnostic statistics?), others will require the deeper understanding of phenomena that most scientists seek. Inother words, for some domains, and in most sciences, it is not enough to answer ?what? ? one also needs to answer ?how?. This work is high risk because there is little research on the extent to which visualization of natural phenomena can be made "cognitively" consonant across disparate spatial and temporal scales, Further, because of disparity between the scientific- and information-visualization communities, it is unclear how to connect analytics with visualization of ecological phenomena. Finally, the data integration necessary for the proposed visualization requires managing much larger volumes of data, and many more different kinds of data. The intellectual merit of the proposed work lays in new conceptual data structures and data representations for scientific visualization that can be used to generate domain specific visualization templates from which a range of specific visualizations for a domain could bedrawn. The broader impacts of the proposed work are three-fold: 1) potential application of the work beyond the realm of environmental science and climate change to that of natural resource management and policy, and to other sciences, 2) educational impact to computational thinking in terms of curricular development at Evergreen College, which will be disseminated via the NSF CPATH project Northwest Distributed Computer Science Department (NWDCSD), and 3) free open-source distribution of the software tool to the scientific community.