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

科学要可靠地支持新发现，其结果必须是可重复的。这已被证明是一个挑战，在许多领域，包括，最值得注意的是，依赖于计算研究作为支持新发现的手段的领域。在这些研究中，复制是特别困难的，因为它们需要开放共享数据和模型，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的硬件和软件上运行，并产生一致的结果。该项目将开发有助于支持计算再现性的软件，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，水文学家和水文科学促进大学联盟之间的合作。（CUAHSI）为水文研究界。 拥有超过3500个用户，拥有超过8000个模型和数据资源，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。 它们也有可能为期刊和出版商目前进行的再现性评估过程提供信息。该项目还将举办研讨会，培训研究人员，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。研究重复性的重要性越来越受到重视，但许多研究仍然无法重复。计算研究中的再现特别困难，因为涉及完整记录数据，模型和程序以及底层硬件和软件依赖性的挑战。该项目开发的再现性工作台软件（ReproBench）将通过建立基于容器的可再现工作流程来解决再现性问题，这将使地球科学家能够轻松有效地验证科学结果。自动化和文件化是改进核查的两个关键方法，一般来说，也是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构建：（I）通过Sciunit项目的自动化容器化方法，以及（II）通过HydroShare的良好记录，社区采用的界面，并将这些投资结合在一起，以建立一个新颖，强大和可复制的工作流程。通过将此工作流程应用于与水相关的科学用例，该项目将展示如何保存，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，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，ReproBench项目团队结合了网络基础设施、领域科学和可再现计算数据科学方面的专业知识。通过利用Sciunit，ReproBench将可重复计算科学的正式方法引入地球科学。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。

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.