EAGER: A Comprehensive Approach for Generating, Sharing, Searching, and Using High-Resolution Terrain Parameters
EAGER: A Comprehensive Approach for Generating, Sharing, Searching, and Using High-Resolution Terrain Parameters
批准号:
2334945
负责人:
Michela Taufer
金额:
$22.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-10-01 至 2025-09-30
中文摘要
地形参数定量描述了景观的表面特性(例如,坡度或地形湿度)。地形参数对于推进与气候有关的科学和工程工作具有重大潜力。由于地形参数可以在不同的空间分辨率下生成,它们是从事土壤湿度预测、火灾传播、土壤碳含量估计、土壤呼吸和水文学工作的科学家的宝贵资源。这些应用对于了解陆地-大气相互作用和减轻气候变化对生态系统和景观的影响至关重要。然而,从数字高程模型推导地形参数的过程是准确的,但在计算资源和时间方面昂贵。为了更有效地生成地形参数,该项目实现了一个灵活的工作流程,以生成不同分辨率的地形参数数据集,用于不同的感兴趣区域。该项目的所有产品(数据,元数据和软件)都存储在开放访问的公共空间中,以确保它们是可查找,可扩展,可互操作和可重用的(FAIR)。研究人员团队通过在Systers(田纳西诺克斯维尔大学的电子工程和计算机科学女性组织)指导学生以及与斯坦福大学的数据科学女性(WiDS)合作,促进代表性不足的学生,特别是女性的参与。地形参数来自数字高程模型。高分辨率地形参数使气候相关科学和工程领域的准确空间分析和决策成为可能,但生成高分辨率数据的计算成本很高,阻碍了地形参数在多种应用中的可用性。该项目以三种方式应对这一挑战,为气候研究提供地形参数。首先,该项目实施了一个工作流程,以任何分辨率(从30公里到3米)生成15个地形参数,同时保持性能和准确性。性能通过测量墙时间和内存使用云平台进行评估。通过将数据与导出的地形参数进行比较,验证了精度。其次,该项目使用工作流并利用数据并行性来生成大型高分辨率数据集(即,低至3米)用于北美(即,加拿大、美国和墨西哥)。该项目可交付成果包括丰富的元数据注释参数值和用于数据搜索和访问、可重现数据生成、准确性验证和性能测量的自动化笔记本。第三,通过汇集一个由具有数据科学和土壤水分动态经验的顶尖科学家组成的跨学科研究团队,该项目促进了联邦机构之间的合作(包括NSF,NASA和USDA等)和机构进行跨学科研究,分享见解,并为气候问题提供创新解决方案-该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Terrain parameters quantitatively describe a landscape's surface properties (for example, slope or topographic wetness). Terrain parameters hold significant potential for advancing climate-related science and engineering efforts. As terrain parameters can be generated at different spatial resolutions, they are valuable resources for scientists working on soil moisture prediction, fire propagation, estimation of soil carbon content, soil respiration, and hydrology. These applications are critical for understanding land-atmosphere interactions and mitigating the impacts of climate change across ecosystems and landscapes. However, the process of deriving terrain parameters from digital elevation models is accurate, but expensive in terms of computational resources and time. For more efficient generation of terrain parameters, this project implements a flexible workflow to generate terrain parameter datasets at different resolutions for different regions of interest. All the products of this project (data, metadata, and software) are stored in an open-access commons to ensure they are Findable, Accessible, Interoperable, and Reusable (FAIR). The team of researchers promotes increased participation of underrepresented students, particularly women, through mentoring students in Systers (the organization for women in Electrical Engineering and Computer Science at the University of Tennessee Knoxville) and the collaboration with the Women in Data Science (WiDS) at Stanford.Terrain parameters are derived from Digital Elevation Models. High-resolution terrain parameters enable accurate spatial analyses and decision-making in climate-related science and engineering domains, but generating high-resolution data is computationally expensive, hindering the usability of terrain parameters for multiple applications. The project addresses this challenge to make terrain parameters available for climate study in three ways. First, the project implements a workflow to generate 15 terrain parameters at any resolution (from 30 km to 3 m) while preserving performance and accuracy. Performance is evaluated by measuring wall times and memory usage cloud platforms. The accuracy is validated by comparing the data with the derived terrain parameters. Second, the project uses the workflow and exploits data parallelism to generate large high-resolution datasets (i.e., down to 3 m) for North America (i.e., Canada, the United States, and Mexico). The project deliverable comprises rich metadata annotating the parameter values and Jupyter Notebooks for data search and access, reproducible data generation, accuracy validation, and performance measurement. Third, by bringing together an interdisciplinary research team of leading scientists with experience in data science and soil moisture dynamics, the project facilitates collaboration among federal agencies (including NSF, NASA, and USDA, among others) and institutions to pursue interdisciplinary research, share insights, and deliver innovative solutions for climate-related issues.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.
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会议论文
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批准号:2331152
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项目类别:Standard Grant
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资助金额:$42.4万
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财政年份:2023
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负责人:Michela Taufer
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依托单位:
SHF: Small: Methods, Workflows, and Data Commons for Reducing Training Costs in Neural Architecture Search on High-Performance Computing Platforms
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资助金额:$62.4万
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财政年份:2022
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负责人:Michela Taufer
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依托单位:
Collaborative Research: Elements: SENSORY: Software Ecosystem for kNowledge diScOveRY - a data-driven framework for soil moisture applications
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批准号:2103845
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项目类别:Standard Grant
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资助金额:$35.0万
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Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications
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批准号:2028923
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项目类别:Standard Grant
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资助金额:$9.0万
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财政年份:2020
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负责人:Michela Taufer
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依托单位:
Collaborative Research: EAGER: Advancing Reproducibility in Multi-Messenger Astrophysics
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批准号:2041977
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项目类别:Standard Grant
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资助金额:$10.0万
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财政年份:2020
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负责人:Michela Taufer
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依托单位:
SHF: Medium: Collaborative Research: ANACIN-X: Analysis and modeling of Nondeterminism and Associated Costs in eXtreme scale applications
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批准号:1900888
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资助金额:$91.57万
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财政年份:2019
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负责人:Michela Taufer
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依托单位:
Collaborative: EAGER: Exploring and Advancing the State of the Art in Robust Science in Gravitational Wave Physics
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批准号:1841399
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项目类别:Standard Grant
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资助金额:$7.5万
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财政年份:2018
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负责人:Michela Taufer
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依托单位:
Collaborative: EAGER: Exploring and Advancing the State of the Art in Robust Science in Gravitational Wave Physics
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批准号:1823372
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项目类别:Standard Grant
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资助金额:$7.5万
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财政年份:2018
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负责人:Michela Taufer
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依托单位:
SHF:Medium:Collaborative Research:A comprehensive methodology to pursue reproducible accuracy in ensemble scientific simulations on multi- and many-core platforms
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批准号:1841552
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项目类别:Standard Grant
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资助金额:$15.73万
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财政年份:2018
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负责人:Michela Taufer
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依托单位:
BIGDATA: IA: Collaborative Research: In Situ Data Analytics for Next Generation Molecular Dynamics Workflows
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批准号:1841758
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项目类别:Standard Grant
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资助金额:$98.0万
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财政年份:2018
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负责人:Michela Taufer
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CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st Century
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批准号:1854312
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资助金额:$43.81万
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财政年份:2018
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负责人:Michela Taufer
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依托单位:
BIGDATA: IA: Collaborative Research: In Situ Data Analytics for Next Generation Molecular Dynamics Workflows
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批准号:1741057
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项目类别:Standard Grant
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资助金额:$98.0万
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依托单位:
CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st Century
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负责人:Michela Taufer
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资助金额:$2.0万
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负责人:Michela Taufer
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依托单位:
Student Support: IEEE Cluster 2015-2016 Conferences
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批准号:1550348
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项目类别:Standard Grant
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资助金额:$2.0万
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财政年份:2015
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负责人:Michela Taufer
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依托单位:
SHF:Medium:Collaborative Research:A comprehensive methodology to pursue reproducible accuracy in ensemble scientific simulations on multi- and many-core platforms
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批准号:1513025
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财政年份:2015
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负责人:Michela Taufer
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依托单位:
Student Support: IEEE Cluster 2014 Conference; Madrid Spain; September 22-26, 2014
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项目类别:Standard Grant
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资助金额:$2.0万
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负责人:Michela Taufer
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依托单位:
EAGER: Assessment of the Numerical Reproducibility in Large-Scale Scientific Simulations on Multicore Architectures
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项目类别:Standard Grant
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资助金额:$9.0万
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负责人:Michela Taufer
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依托单位:
SHF: Small: Collaborative Research: Modeling and Analyzing Big Data on Peta- and Exascale Distributed Systems supported by MapReduce Methodologies
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批准号:1318445
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项目类别:Standard Grant
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资助金额:$42.7万
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财政年份:2013
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负责人:Michela Taufer
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依托单位:
CSR: Small: Collaborative: Pursuing High Performance on Clouds and Other Dynamically Heterogeneous Computing Platforms
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项目类别:Standard Grant
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资助金额:$18.45万
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负责人:Michela Taufer
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依托单位:
海外基金