EarthCube Data Capabilities: Collaborative Research: Integration of Reproducibility into Community CyberInfrastructure

EarthCube 数据功能：协作研究：将可重复性集成到社区网络基础设施中

• 批准号: 1928369
• 负责人: David Tarboton
• 金额: $ 30.57万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928369&HistoricalAwards=false
• 关键词: EarthCube; Data; Capabilities; Collaborative; Research

For science to reliably support new discoveries, its results must be reproducible. This has proven to be a challenge in many fields including, most notably, fields that rely on computational studies as a means for supporting new discoveries. Reproducibility in these studies is particularly difficult because they require open sharing of data and models and careful control by the original researcher. This is to ensure that products can be run on later generations of hardware and software and produce consistent results. This project will develop software that helps support computational reproducibility and makes it easier and more efficient for geoscientists to preserve, share, repeat and replicate scientific computations. The Broader Impacts of this project include a collaboration between computer scientists, hydrologists and the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) for the hydrology research community. With over 3500 users, and holding over 8000 model and data resources, this collaboration will bring improved tools and best practices to a broad and diverse community of geoscientists. Beyond hydrology, the methods and tools developed as part of this project have the potential to be extended to the solid Earth and space science geoscience domains. They also have the potential to inform the reproducibility evaluation process as currently undertaken by journals and publishers. The projct will also conduct workshops to train researchers and be used in the classroom at Utah Sate Universtiy, DePaul University and the University of Virginia. Emphasis on the importance of research reproducibility is steadily rising, however many studies still continue to not be reproducible. Reproducibility in computational studies is particularly difficult because of the challenges involved in completely documenting the data, models and procedures used together with the underlying hardware and software dependencies. The reproducibility workbench software (ReproBench) developed in this project will address reproducibility questions by establishing a container-based reproducible workflow that will make it easy and efficient for geoscientists to verify scientific results. Automation and documentation are two key methods for improving verification and, in general, the conduct of reproducible science. This project will build-from past investments: (I) automated containerization methods, through the Sciunit project, and (II) well-documented, community-adopted interfaces, through HydroShare, and bring these investments together to establish a novel, robust, and reproducible workflow. By applying this workflow to water-related science use cases, this project will demonstrate how to preserve, share, repeat, and replicate scientific results. The interfaces can become an exemplar for other community cyberinfrastructure that, akin to Hydrology, aims to share data and models at a large scale. In establishing this workflow, the ReproBench project team combines expertise in cyberinfrastructure, domain science, and reproducible computational data science. By leveraging Sciunit, ReproBench brings formal methods for the conduct of reproducible computational science into the geosciences.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.

为了使科学可靠地支持新发现，其结果必须可再现。事实证明，这在许多领域中都是一个挑战，包括依靠计​​算研究作为支持新发现的手段的领域。在这些研究中的可重复性尤其困难，因为它们需要公开共享数据和模型，并由原始研究人员进行仔细的控制。这是为了确保可以在后来的硬件和软件上运行产品并产生一致的结果。该项目将开发有助于支持计算可重复性的软件，并使地球科学家保存，共享，重复和复制科学计算更容易，更有效。该项目的更广泛的影响包括计算机科学家，水文学家和大学联盟之间的合作，以促进水文科学公司（CUAHSI）为水文学研究界的发展。 有超过3500位用户以及拥有8000多个模型和数据资源的用户，这项合作将为广泛而多样化的地球科学家社区带来改进的工具和最佳实践。 除了水文外，作为该项目的一部分开发的方法和工具有可能扩展到固体地球和太空科学地球科学领域。 他们还有可能告知期刊和发行商当前进行的可重复性评估过程。 ProJCT还将举办研讨会来培训研究人员，并在犹他州塞特大学，Depaul大学和弗吉尼亚大学的教室中使用。强调研究可重复性的重要性是稳步上升的，但是许多研究仍然不可再现。在计算研究中的可重复性尤其困难，因为完全记录了与基础硬件和软件依赖关系一起使用的数据，模型和程序所带来的挑战。该项目中开发的可重复性工作台软件（RepoBench）将通过建立基于容器的可重复工作流程来解决可重复性问题，这将使地球科学家验证科学结果变得容易有效。自动化和文档是改善验证以及一般而言可再现科学的两种关键方法。该项目将从过去的投资中建立：（i）通过Sciunit项目自动化的容器化方法，以及（ii）通过HardoRshare进行了充分记录的，社区辅助的界面，并将这些投资聚集在一起以建立一种新颖，可靠的，可靠的和可重复的工作流程。通过将此工作流程应用于与水有关的科学用例，该项目将演示如何保存，共享，重复和复制科学结果。这些接口可以成为其他社区网络基础结构的典范，类似于水文学，旨在大规模共享数据和模型。在建立此工作流程时，Reprobench项目团队结合了网络基础结构，领域科学和可再现的计算数据科学方面的专业知识。通过利用Sciunit，Repobench将进行可重复的计算科学进行正式方法带入地球科学。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来审查标准的评估值得支持的。

项目成果

Building cyberinfrastructure for the reuse and reproducibility of complex hydrologic modeling studies

构建网络基础设施以实现复杂水文模型研究的重用和可重复性

• DOI: 10.1016/j.envsoft.2023.105689
• 发表时间: 2023
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: Maghami, Iman;Van Beusekom, Ashley;Hay, Lauren;Li, Zhiyu;Bennett, Andrew;Choi, YoungDon;Nijssen, Bart;Wang, Shaowen;Tarboton, David;Goodall, Jonathan L.
• 通讯作者: Goodall, Jonathan L.

A taxonomy for reproducible and replicable research in environmental modelling

环境建模中可重复和可复制研究的分类法

• DOI: 10.1016/j.envsoft.2020.104753
• 发表时间: 2020
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: Essawy, Bakinam T.;Goodall, Jonathan L.;Voce, Daniel;Morsy, Mohamed M.;Sadler, Jeffrey M.;Choi, Young Don;Tarboton, David G.;Malik, Tanu
• 通讯作者: Malik, Tanu

Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces

• DOI: 10.1016/j.envsoft.2020.104888
• 发表时间: 2021-01-01
• 期刊: ENVIRONMENTAL MODELLING & SOFTWARE
• 影响因子: 4.9
• 作者: Choi, Young-Don;Goodall, Jonathan L.;Tarboton, David G.
• 通讯作者: Tarboton, David G.

The Next Frontier: Making Research More Reproducible

下一个前沿：使研究更具可重复性

• DOI: 10.1061/(asce)wr.1943-5452.0001215
• 发表时间: 2020
• 期刊: Journal of Water Resources Planning and Management
• 影响因子: 3.1
• 作者: Rosenberg, David E.;Filion, Yves;Teasley, Rebecca;Sandoval-Solis, Samuel;Hecht, Jory S.;van Zyl, Jakobus E.;McMahon, George F.;Horsburgh, Jeffery S.;Kasprzyk, Joseph R.;Tarboton, David G.
• 通讯作者: Tarboton, David G.

Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating Model‐Agnostic and Model‐Specific Configuration Steps in Applications of Large‐Domain Hydrologic Models

• DOI: 10.1029/2021wr031753
• 发表时间: 2021-12
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: W. Knoben;M. Clark;J. Bales;Andrew R. Bennett;S. Gharari;C. Marsh;Bart Nijssen;A. Pietroniro;R. Spiteri;D. Tarboton;A. Wood