EarthCube Data Capabilities: Collaborative Research: Integration of Reproducible Methods into Community Cyberinfrastructure

要让科学可靠地支持新发现，其结果必须是可重现的。事实证明，这在许多领域都是一项挑战，尤其是那些依赖计算研究作为支持新发现的手段的领域。这些研究的重复性尤其困难，因为它们需要公开共享数据和模型，并由原始研究人员仔细控制。这是为了确保产品可以在以后几代硬件和软件上运行，并产生一致的结果。该项目将开发有助于支持计算重现性的软件，并使地球科学家更容易和更有效地保存、共享、重复和复制科学计算。该项目的更广泛影响包括计算机科学家、水文学家和大学促进水文科学公司联合会(CUAHSI)为水文学研究界开展的合作。这项合作拥有超过3500名用户，拥有超过8000个模型和数据资源，将为广泛和多样化的地球科学家社区带来改进的工具和最佳实践。除水文学外，作为该项目一部分开发的方法和工具有可能扩展到固体地球和空间科学地球科学领域。它们还有可能为期刊和出版商目前正在进行的可再生性评估过程提供信息。该项目还将举办培训研究人员的研讨会，并在犹他州州立大学、德保罗大学和弗吉尼亚大学的课堂上使用。对研究可重复性的重要性的强调正在稳步上升，然而许多研究仍然是不可重现的。计算研究的可重复性尤其困难，因为在完整记录与基础硬件和软件相关性一起使用的数据、模型和程序方面存在挑战。在该项目中开发的可再生性工作台软件(ReproBch)将通过建立一个基于容器的可再生性工作流程来解决可再生性问题，这将使地球科学家能够轻松高效地核实科学结果。自动化和文档化是改进核查和进行可复制科学的两个关键方法。该项目将从过去的投资中建立：(I)通过本单位项目的自动化集装箱化方法，以及(Ii)通过Water Share提供的记录良好的社区采用的接口，并将这些投资结合在一起，建立一个新的、强大的和可重复的工作流程。通过将此工作流程应用于与水相关的科学使用案例，该项目将演示如何保存、共享、重复和复制科学成果。这些接口可以成为其他社区网络基础设施的典范，这些基础设施类似于水文学，旨在大规模共享数据和模型。在建立这一工作流程时，ReproBch项目团队结合了网络基础设施、领域科学和可复制计算数据科学方面的专业知识。该奖项反映了NSF的法定使命，通过使用基金会的学术价值和更广泛的影响审查标准进行评估，被认为是值得支持的。

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.