课题基金 / 基金详情

EAGER: Preserve/Destroy Decisions for Simulation Data in Computational Physics and Beyond

EAGER: Preserve/Destroy Decisions for Simulation Data in Computational Physics and Beyond
EAGER:计算物理及其他领域模拟数据的保存/销毁决策
批准号:
1839010
负责人:
Victoria Stodden
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-08-15 至 2021-08-31

项目摘要

项目成果

Victoria Stodden的其他基金

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中文摘要
翻译
科学研究界越来越多地开发共享和再利用研究数据的方法,从而使更多的发现能够从以前的研究投资中获得。对共享和可重用性的关注主要集中在实验和观测数据上。该项目解决了同样令人烦恼的挑战,即如何最好地利用和再利用计算模拟中产生的大量数据。指导这个项目的重要研究问题包括:在何种程度上模拟结果可以被复制;存储模拟数据本身的优点,供他人重用相比,提供计算软件,使他人可以重新运行的模拟;和了解哪些软件测试的做法可以促进复制/重用的模拟数据和模拟软件,产生这些数据。主要研究人员将通过对他们通过先前研究收集的一组计算物理模拟数据集和软件代码进行广泛的复制和软件代码测试来解决这些问题。该项目将产生公开可用的,完全可复制的计算物理工作,作为以数据和代码可有效重用的方式发布结果的例子。主要研究人员旨在提高对与基于模拟的研究相关的代码和数据的理解,并提高其可重用性。 该项目旨在更好地告知仿真环境中的数据销毁/保存决策,以提高仿真数据和代码的可重用性和互操作性。该项目还将考虑一些重要的问题,如软件工程测试实践如何与计算物理实践相关,以及计算环境的变化如何影响代码执行和模拟数据的再生。最终,这项工作的结果旨在指导研究界如何最好地制作和传播研究代码。预计计算物理的结果可以扩展到为其他社区制定模拟数据和代码共享的一般准则,进行适当的代码测试,以及开发相关网络基础设施和工具的最佳实践。该项目由美国国家科学基金会的公共访问计划支持,该计划由美国国家科学基金会高级网络基础设施办公室代表基金会管理。该奖项反映了美国国家科学基金会的法定使命,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
The scientific research community has been increasingly developing ways to share and re-use research data, thereby allowing more discoveries to be made from previous research investments. Much of the focus on sharing and reusability has been on experimental and observational data. This project addresses the equally vexing challenge of how to make best use and re-use of the massive data produced in computational simulations. Important research questions guiding this project include: the degree to which simulation results can be replicated; the advantages of storing the simulation data itself for others to reuse as compared to providing the computational software so that others can re-run the simulations; and understanding which software testing practices can facilitate the replication/reuse of simulation data and the simulation software that produces those data. The principal investigators will address these questions by performing extensive replication and software code testing on a set of computational physics simulation datasets and software code that they had gathered through a previous study. The project will produce publicly available, fully reproducible computational physics works as examples for publishing results in a way that the data and code are effectively reusable.The principal investigators aim to improve understanding of, and increase, the reusability of the code and data associated with simulation-based research. This project specifically aims to better inform data destroy/preservation decisions in the simulation context, toward improving the reusability and interoperability of simulation data and code. The project will also consider important questions such as how software engineering testing practices relate to computational physics practices, and how changes in computational environments affect code execution and the regeneration of simulation data. Ultimately, the results of this work are intended to guide the research community on how to best produce and disseminate research code. It is anticipated that the results for computational physics can be extended to develop general guidelines for simulation data and code sharing for other communities, the appropriate code testing to do so, and best practices for development of associated cyberinfrastructure and tools. This project is supported by the National Science Foundation's Public Access Initiative which is managed by the NSF Office of Advanced Cyberinfrastructure on behalf of the Foundation.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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1109/icse43902.2021.00018
发表时间: 2021-05
期刊: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
影响因子: --
作者: [Peilun Zhang;Yanjie Jiang;Anjiang Wei;V. Stodden;D. Marinov;A. Shi]
通讯作者: Peilun Zhang;Yanjie Jiang;Anjiang Wei;V. Stodden;D. Marinov;A. Shi
Understanding and Improving Regression Test Selection in Continuous Integration
理解和改进持续集成中的回归测试选择
DOI: 10.1109/issre.2019.00031
发表时间: 2019
期刊: 30th IEEE International Symposium on Software Reliability Engineering (ISSRE 2019
影响因子: --
作者: [Shi, August, Zhao, Peiyuan, Marinov, Darko]
通讯作者: Marinov, Darko
Dependent-test-aware regression testing techniques
依赖测试感知回归测试技术
DOI: 10.1145/3395363.3397364
发表时间: 2020
期刊: ACM International Symposium on Software Testing and Analysis (ISSTA 2020
影响因子: --
作者: [Lam, Wing, Shi, August, Oei, Reed, Zhang, Sai, Ernst, Michael D., Xie, Tao]
通讯作者: Xie, Tao
DOI: 10.1145/3395363.3397383
发表时间: 2020-07
期刊: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis
影响因子: --
作者: [Qianyang Peng;A. Shi;Lingming Zhang]
通讯作者: Qianyang Peng;A. Shi;Lingming Zhang
共 6 条
    Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications
    • 批准号:
      2138770
    • 项目类别:
      Standard Grant
    • 资助金额:
      $3.0万
    • 财政年份:
      2021
    • 负责人:
      Victoria Stodden
    • 依托单位:
    EAGER: Preserve/Destroy Decisions for Simulation Data in Computational Physics and Beyond
    • 批准号:
      2138773
    • 项目类别:
      Standard Grant
    • 资助金额:
      $30.0万
    • 财政年份:
      2021
    • 负责人:
      Victoria Stodden
    • 依托单位:
    EAGER: Reproducibility and Cyberinfrastructure for Computational and Data-Enabled Science
    • 批准号:
      2138776
    • 项目类别:
      Standard Grant
    • 资助金额:
      $30.0万
    • 财政年份:
      2021
    • 负责人:
      Victoria Stodden
    • 依托单位:
    Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications
    海外基金