CDI Type I: Collaborative Research: Cyber-Enabled Chemical Imaging: From Terascale Data to Chemical Imaging

CDI I 型：协作研究：网络支持的化学成像：从万亿级数据到化学成像

• 批准号: 1027781
• 负责人: Amy Walker
• 金额: $ 56.64万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027781&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; Research; Cyber

Chemical imaging is widely used in many areas of science and engineering, including chemistry, materials science, forensic science, medicine, art conservation, and archaeometry. A chemical image is a picture in which the colors indicate the position and concentration of different atoms and molecules. These images therefore provide the chemical composition of the surface of a sample, which is crucial information for understanding its history, behavior, and properties. Chemical images can be very complex. Biological tissue samples, forensic evidence, and other materials may be composed of hundreds or even thousands of different chemical compounds, in amounts varying over lengths as small as a few nanometers. Tools that can correctly identify and resolve these components and changes are highly desirable. In this project, new analysis tools and state-of-the-art computing resources will be used to greatly improve the resolution and quality of these chemical images. This improvement is possible because the great bulk of information obtained in chemical imaging techniques is normally not used. The "raw" data produced by high-resolution experiments (imaging mass spectrometry, infrared and Raman microscopy, scanning Auger microscopy, and x-ray photoelectron spectroscopy imaging) can exceed thousands of gigabytes per square millimeter of sample imaged, which up to now has been far too much for individual researchers to even store, let alone completely analyze. The team will provide software that can take full advantage of the power of modern supercomputers, such as those available through NSF's Teragrid, to extract statistically optimal chemical images from these enormous data sets. These tools will also be able to combine many small-area images into large-area chemical images of unprecedented resolution, enabling detailed chemical imaging of much larger samples than previously possible.This research's goal is to dramatically improve the power, applicability, and ease-of-use of a wide range of chemical imaging techniques, significantly advancing many different fields of science, engineering and medicine. This can only be accomplished through "computational thinking", because the data sets themselves are simply too large and too complex to be comprehensively analyzed by even expert human operators. The software, along with documentation and tutorials, will be freely distributed, and installed for general use at national supercomputing centers.

化学成像广泛应用于科学和工程的许多领域，包括化学、材料科学、法医学、医学、艺术保护和考古学。 化学图像是用颜色表示不同原子和分子的位置和浓度的图片。因此，这些图像提供了样品表面的化学成分，这是了解其历史、行为和特性的关键信息。化学图像可能非常复杂。生物组织样本、法医证据和其他材料可能由数百甚至数千种不同的化合物组成，其数量在小至几纳米的长度上变化。 非常需要能够正确识别和解决这些组件和更改的工具。在该项目中，将使用新的分析工具和最先进的计算资源来大大提高这些化学图像的分辨率和质量。这种改进是可能的，因为通常不使用化学成像技术中获得的大量信息。 高分辨率实验（成像质谱、红外和拉曼显微镜、扫描俄歇显微镜和 X 射线光电子能谱成像）产生的“原始”数据可以超过每平方毫米成像样品数千千兆字节，迄今为止，对于个别研究人员来说，这些数据甚至无法存储，更不用说完全分析了。 该团队将提供能够充分利用现代超级计算机（例如通过 NSF 的 Teragrid 提供的超级计算机）功能的软件，从这些庞大的数据集中提取统计上最佳的化学图像。 这些工具还能够将许多小面积图像组合成前所未有的分辨率的大面积化学图像，从而能够对比以前大得多的样品进行详细的化学成像。这项研究的目标是显着提高功能、适用性和易用性。广泛使用化学成像技术，显着推进了科学、工程和医学的许多不同领域。 这只能通过“计算思维”来完成，因为数据集本身太大、太复杂，即使是专家操作员也无法进行全面分析。 该软件以及文档和教程将免费分发，并安装在国家超级计算中心供一般使用。