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INSPIRE: Automating Reasoning in Interpreting Climate Records of the Past

INSPIRE: Automating Reasoning in Interpreting Climate Records of the Past
INSPIRE:解释过去气候记录的自动推理
批准号:
1245947
负责人:
Elizabeth Bradley
金额:
$57.7万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-08-15 至 2018-07-31

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This INSPIRE award is jointly funded by the Information Integration and Informatics Program in the Information and Intelligent Systems Division of the Computer and Information Sciences Directorate, the Marine Geology and Geophysics Program in the Ocean Sciences Division of the Geosciences Directorate, the Arctic Natural Sciences Program in the Arctic Sciences Division and the Antarctic Glaciology Program in the Antarctic Sciences Division in the Office of Polar Programs, and the Office of Cyberinfrastructure. The critical first step in the analysis of paleoclimate records like ice or sediment cores is the construction of an age model, which relates the depth in a core to the calendar age of the material at that point. The reasoning involved in age-model construction is complex, subtle, and scientifically demanding because the processes that control the rate of material accumulation over time, and that affect the core between formation and sampling, are unknown. Geoscientists approach this problem by treating the core like a crime scene and asking the question: "What physical and chemical processes could have produced this situation, and what does that say about the timeline?" However, the sheer number of possibilities, coupled with the volume and complexity of the climatology data that is currently available and is continually collected, severely limit the scope of these investigations. The goal of this project is to remove this roadblock. This research will lead to an integrated software tool called CScibox, that uses automated reasoning techniques to help scientists analyze ice and sediment cores. It employs a cyberinfrastructure that provides powerful, intuitive tools on a scientist's desktop while taking full advantage of modern data- and computation-intensive computing and networking infrastructure -- including workflow-based computation, parallel execution, distributed systems, cluster machines and the cloud. CScibox will not only improve the ability of individual geoscientists analyze single cores; it has the potential to transform the field of paleoclimatology by facilitating rapid, reproducible analysis and synthesis of the information in the diverse collections of raw data available in data archives to foster understanding and improved scientific decision making. This will have broad impacts for society by allowing scientists to develop deeper insights into the roles of various factors in the complex relationships that give rise to geological records of the earth's climate that are used in today's models of environmental change. This project also has a broad educational impact. Students involved in the development and implementation of CScibox will develop skills in interdisciplinary research and will learn how to apply computational methodology in a challenging scientific context that has not yet significantly benefitted from developments in information technology. CScibox is designed to be easy to install and use; see the project web site (http://www.cs.colorado.edu/~lizb/cscience.html) for source code, documentation, and examples of its use. Future steps include extending the work to other paleoclimate data, working with geoscientists to make the user interface as intuitive as possible, and holding demos and workshops at geosciences conferences to educate that community about what the tool can do and how to use it.
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Computing Innovation Fellows Project 2021
  • 批准号:
    2127309
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $1999.87万
  • 财政年份:
    2021
  • 负责人:
    Elizabeth Bradley
  • 依托单位:
Computing Innovation Fellows 2020 Project
  • 批准号:
    2030859
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $1395.5万
  • 财政年份:
    2020
  • 负责人:
    Elizabeth Bradley
  • 依托单位:
Harnessing the Data Revolution in Space Physics: Topological Data Analysis and Deep Learning for Improved Solar Eruption Prediction
  • 批准号:
    2001670
  • 项目类别:
    Standard Grant
  • 资助金额:
    $79.24万
  • 财政年份:
    2020
  • 负责人:
    Elizabeth Bradley
  • 依托单位:
The Shape of Data: A New Way to Detect Critical Shifts in System Performance
  • 批准号:
    1537460
  • 项目类别:
    Standard Grant
  • 资助金额:
    $43.0万
  • 财政年份:
    2015
  • 负责人:
    Elizabeth Bradley
  • 依托单位:
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