SI2-SSE: Wavelet Enabled Progressive Data Access and Storage Protocol (WASP)

数字成像方法的进步促进了大量数据的获取，从而使复杂的多维图像的可视化和分析发生了革命性的变化。与此同时，现代计算技术使得能够对广泛的科学现象进行数值建模，从而产生大量的数值数据，而这些数据只是现代计算和可视化方法实现的科学探索的起点。在生物学和地球科学这两个看似截然不同的学科中，情况尤其如此。这些功能是有代价的：不断增加的数据大小和复杂性需要更复杂的数据分析和可视化方法。该项目将进行研究，以形成一个共同的软件框架，以支持基于小波展开的数据表示的多尺度渐进数据精化方法，并使生物和地球科学界能够互动探索大数据集。基于小波的数据表示通用工具包的开发将产生广泛的影响，允许对从广泛领域和多种平台收集的数据进行多尺度分析、存储和可视化，从高端计算设施到学生、野外生物学家和其他人使用的笔记本电脑。大数据集的分析和可视化在科学发现中发挥着重要作用。高效和广泛可用的工具来完成这些任务对于广泛的科学和教育领域是至关重要的。然而，随着数据的大小和复杂性的增加，有效的分析和可视化是一个不小的问题。这项研究通过一种通用的渐进式访问、多尺度数据表示来解决这一挑战，以有效地处理一系列科学领域的结构化数据集。这一开发是基于NCAR为地球科学应用开发的支持小波的数据表示法。这些工具将利用非常灵活和开放源码的标准NetCDF格式，这些方法将被记录为一套惯例和开发的工具包，其中纳入和整合这些组成部分以供传播。除开放源码工具包外，这些工具还将集成到VALUE(NCAR)和STK(CSCI)平台中，从而分别为地球科学界和生物科学界扩大这些平台的能力和效率。*该项目产生的进步将通过开放源码工具包公开传播给用户社区。

Advances in digital imaging methods are revolutionizing a wide range of scientific disciplines by facilitating the acquisition of huge amounts of data that allow the visualization and analysis of complex, multidimensional images. Concurrently, modern computing technologies enable numerical modeling of a broad gamut of scientific phenomena, resulting in vast quantities of numerical data, which are just the starting point for the scientific exploration that modern computational and visualization methods enable. This is particularly true in the biological and geosciences, two seemingly very different disciplines. These capabilities come with a cost: increasing data size and complexity require more sophisticated methods for data analysis and visualization. This project will conduct research that will lead to a common software framework for supporting a multi scale progressive data refinement method based upon the representation of the data as a wavelet expansion, and enabling interactive exploration of large data sets for the bio and geoscience communities. The development of a general toolkit for wavelet based representations of data will have broad impact, allowing the multi scale analysis, storage, and visualization for data collected in a wide range of fields and on a multitude of platforms, from high end computing facilities to laptop computers used by students, field biologists, and others.Analysis and visualization of large data sets play an important role in scientific discovery. Efficient, and broadly available tools to accomplish these tasks are crucial for a wide range of scientific and educational fields.  However, efficient analysis and visualization is a non trivial problem as the size and complexity of data increases. This research addresses this challenge through a general progressive access, multi scale data representation for efficient handling of structured data sets across a range of science domains. The development is based upon a wavelet enabled data representation developed by NCAR for geoscience applications.  The tools will utilize the very flexible and open source standard NetCDF format, and the methods will be documented as a set of conventions and a toolkit developed that incorporates and integrates these components for dissemination. In addition to an open source toolkit, these tools will be integrated into the VAPOR (NCAR) and STK (CSCI) platforms, thus expanding the capabilities and efficiencies of these platforms for the geo and bio sciences communities, respectively.   Advancements generated by this project will be openly disseminated to the user community through an open source toolkit.