Collaborative Research: Elements: Advancing Data Science and Analytics for Water (DSAW)

合作研究:要素:推进水数据科学和分析 (DSAW)

基本信息

项目摘要

Scientific challenges in hydrology and water resources such as understanding impacts of variable climate, sustainability of water supply with population growth and land use change, and impacts of hydrologic change on ecosystems and humans are increasingly data intensive. The volume of data produced by environmental scientists to study hydrologic systems requires advanced software tools for effective data visualization, analysis, and modeling. Scientists spend much of their time accessing, organizing, and preparing datasets for analyses, which can be a barrier to efficient analyses and hinders scientific inquiries and advances. This project will develop new software that will enhance scientists' ability to apply advanced data visualization and analysis methods (collectively referred to as "data science" methods) in the hydrology and water resources domain. The project will promote standardized software tools and data formats to help scientists enhance the consistency, share-ability, and reproducibility of the analyses they perform - all of which are important in building trust in scientific results. The software developed in the project will make data loading and organization for analysis easier, reducing the time spent by scientists in choosing appropriate data structures and writing computer code to read and parse data. It will enable users to automatically retrieve data from the HydroShare system, which is a hydrology domain data repository, as well as from important national water data sources like the United States Geological Survey's National Water Information System. The software will automatically load data from these sources into standardized and high performance data structures targeted to specific scientific data types and that integrate with visualization, analysis, and other data science capabilities commonly used by scientists in the hydrology and water resources domains. The project will also reduce the technical burden for water scientists associated with creating a computational environment within which to execute their analyses by installing and maintaining the Python packages developed within the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare-linked JupyterHub environment. Finally, the project will demonstrate the functionality and use of the software by producing a set of educational modules based on real water-data science applications that provide a specific mechanism for delivering the software to the community and promoting its use in classroom and research environments.Scientific and related management challenges in the water domain are inherently multi-disciplinary, requiring synthesis of data of multiple types from multiple domains. Many data manipulation, visualization, and analysis tasks performed by water scientists are difficult because (1) datasets are becoming larger and more complex; (2) standard data formats for common data types are not always agreed upon, and, when they are, they are not always mapped to an efficient structure for visualization and/or analysis within an analytical environment; and (3) water scientists generally lack training in data intensive scientific methods that would enable them to use new and existing tools to efficiently tackle large and complex datasets. This project will advance Data Science and Analytics for Water (DSAW) by developing: (1) an advanced object data model that maps common water-related data types to high performance data structures within the object-oriented Python language and analytical environment based upon standard file, data, and content types established by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare system; (2) two new Python packages that enable users to write Python code for automating retrieval of desired water data, loading it into high performance memory objects specified by the object data model designed in the project, and performing analysis in a reproducible way that can be shared, collaborated around, and formally published for reuse. The project will use domain-specific data science applications to demonstrate how the new Python packages can be paired with the powerful data science capabilities of existing Python packages like Pandas, numpy, and scikit-learn to develop advanced analytical workflows within cloud and desktop environments. The project aims to extend the data access, collaboration, and archival capabilities of the HydroShare data and model repository and promote its use as a platform for reproducible water-data science. The project also aims to overcome barriers associated with accessing, organizing, and preparing datasets for data science intensive analyses. Overcoming these barriers will be an enabler for transforming scientific inquiries and advancing application of data science methods in the hydrology and water resources domains.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.
水文学和水资源领域的科学挑战,如了解气候变化的影响、随着人口增长和土地使用变化的供水可持续性,以及水文变化对生态系统和人类的影响,都越来越需要大量的数据。环境科学家为研究水文系统而产生的大量数据需要先进的软件工具来进行有效的数据可视化、分析和建模。科学家花费大量时间访问、组织和准备数据集以进行分析,这可能会阻碍高效分析并阻碍科学探究和进步。该项目将开发新的软件,提高科学家在水文和水资源领域应用先进的数据可视化和分析方法(统称为“数据科学”方法)的能力。该项目将促进标准化的软件工具和数据格式,以帮助科学家提高他们所进行分析的一致性、共享性和可重复性,所有这些对于建立对科学结果的信任都很重要。该项目开发的软件将使数据加载和组织分析更加容易,减少科学家在选择适当的数据结构和编写计算机代码以读取和解析数据方面所花费的时间。它将使用户能够从水文领域数据储存库HydroShare系统以及美国地质调查局国家水信息系统等重要的国家水数据来源自动检索数据。该软件将自动将来自这些来源的数据加载到针对特定科学数据类型的标准化和高性能数据结构中,并与水文和水资源领域科学家常用的可视化,分析和其他数据科学功能相结合。该项目还将通过安装和维护在促进水文科学大学联盟内开发的Python软件包,减轻水科学家在创建计算环境以执行分析方面的技术负担。(CUAHSI)HydroShare链接的HydroyterHub环境。最后,该项目将展示该软件的功能和用途,制作一套基于真实的水数据科学应用程序的教育模块,为向社区提供该软件并促进其在课堂和研究环境中的使用提供一种具体机制。需要综合来自多个域的多种类型的数据。由水科学家执行的许多数据操纵、可视化和分析任务是困难的,因为(1)数据集变得更大和更复杂;(2)用于常见数据类型的标准数据格式并不总是一致的,并且当它们一致时,它们并不总是映射到用于分析环境内的可视化和/或分析的有效结构;(3)水科学家普遍缺乏数据密集型科学方法方面的培训,这些方法将使他们能够使用新的和现有的工具来有效地处理大型和复杂的数据集。该项目将通过开发以下内容推进水数据科学和分析(DSAW):(1)一个高级对象数据模型,该模型将常见的与水相关的数据类型映射到基于标准文件的面向对象Python语言和分析环境中的高性能数据结构,数据,以及由大学促进水文科学联盟建立的内容类型。(CUAHSI)HydroShare系统;(2)两个新的Python包,使用户能够编写Python代码,自动检索所需的水数据,将其加载到项目中设计的对象数据模型指定的高性能内存对象中,并以可共享,协作和正式发布的可重复方式进行分析。该项目将使用特定领域的数据科学应用程序来演示新的Python包如何与现有Python包(如Pandas,numpy和scikit-learn)的强大数据科学功能配对,以在云和桌面环境中开发高级分析工作流程。该项目旨在扩展HydroShare数据和模型存储库的数据访问、协作和存档能力,并促进其作为可复制水数据科学平台的使用。该项目还旨在克服与访问、组织和准备数据集以进行数据科学密集型分析相关的障碍。该奖项反映了NSF的法定使命,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。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Anthony Castronova其他文献

Advancing open and reproducible water data science by integrating data analytics with an online data repository
通过将数据分析与在线数据存储库相整合来推进开放且可重现的水数据科学
  • DOI:
    10.1016/j.envsoft.2025.106422
  • 发表时间:
    2025-04-01
  • 期刊:
  • 影响因子:
    4.600
  • 作者:
    Jeffery S. Horsburgh;Scott Black;Anthony Castronova;Pabitra K. Dash
  • 通讯作者:
    Pabitra K. Dash

Anthony Castronova的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Anthony Castronova', 18)}}的其他基金

Framework: Software: Collaborative Research: CyberWater-An open and sustainable framework for diverse data and model integration with provenance and access to HPC
框架:软件:协作研究:Cyber​​Water - 一个开放且可持续的框架,用于将各种数据和模型集成到 HPC 的来源和访问权限
  • 批准号:
    1835592
  • 财政年份:
    2019
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Framework: Software: NSCI : Computational and data innovation implementing a national community hydrologic modeling framework for scientific discovery
合作研究:框架:软件:NSCI:计算和数据创新实施国家社区水文建模框架以促进科学发现
  • 批准号:
    1835818
  • 财政年份:
    2018
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: CYBER Training: CIU: Data Streams, Model Workflows, and Educational Pipelines for Hydrologic Sciences
合作研究:网络培训:CIU:水文科学的数据流、模型工作流程和教育管道
  • 批准号:
    1829744
  • 财政年份:
    2018
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant

相似国自然基金

Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 批准年份:
    2024
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
Cell Research
  • 批准号:
    31224802
  • 批准年份:
    2012
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research
  • 批准号:
    31024804
  • 批准年份:
    2010
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research (细胞研究)
  • 批准号:
    30824808
  • 批准年份:
    2008
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: Elements: VLCC-States: Versioned Lineage-Driven Checkpointing of Composable States
协作研究:元素:VLCC-States:可组合状态的版本化谱系驱动检查点
  • 批准号:
    2411387
  • 财政年份:
    2024
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: Linking geochemical proxy records to crustal stratigraphic context via community-interactive cyberinfrastructure
合作研究:要素:通过社区交互式网络基础设施将地球化学代理记录与地壳地层背景联系起来
  • 批准号:
    2311092
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: Lattice QCD software for nuclear physics on heterogeneous architectures
合作研究:Elements:用于异构架构核物理的 Lattice QCD 软件
  • 批准号:
    2311430
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: ProDM: Developing A Unified Progressive Data Management Library for Exascale Computational Science
协作研究:要素:ProDM:为百亿亿次计算科学开发统一的渐进式数据管理库
  • 批准号:
    2311757
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: FuSe: Monolithic 3D Integration (M3D) of 2D Materials-Based CFET Logic Elements towards Advanced Microelectronics
合作研究:FuSe:面向先进微电子学的基于 2D 材料的 CFET 逻辑元件的单片 3D 集成 (M3D)
  • 批准号:
    2329189
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Experimental and computational constraints on the isotope fractionation of Mossbauer-inactive elements in mantle minerals
合作研究:地幔矿物中穆斯堡尔非活性元素同位素分馏的实验和计算约束
  • 批准号:
    2246686
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: Linking geochemical proxy records to crustal stratigraphic context via community-interactive cyberinfrastructure
合作研究:要素:通过社区交互式网络基础设施将地球化学代理记录与地壳地层背景联系起来
  • 批准号:
    2311091
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: Phonon Database Generation, Analysis, and Visualization for Data Driven Materials Discovery
协作研究:要素:数据驱动材料发现的声子数据库生成、分析和可视化
  • 批准号:
    2311202
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: Enabling Particle and Nuclear Physics Discoveries with Neural Deconvolution
合作研究:元素:通过神经反卷积实现粒子和核物理发现
  • 批准号:
    2311667
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Experimental and computational constraints on the isotope fractionation of Mossbauer-inactive elements in mantle minerals
合作研究:地幔矿物中穆斯堡尔非活性元素同位素分馏的实验和计算约束
  • 批准号:
    2246687
  • 财政年份:
    2023
  • 资助金额:
    $ 2.94万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了