Integrating scientific cyberinfrastructures to improve reproducibility in computational hydrology: Example for HydroShare and GeoTrust

Essawy, Bakinam T.; Goodall, Jonathan L.; Zell, W.; Voce, Daniel; Morsy, Mohamed M.; Sadler, Jeffrey M.; Yuan, Zhihao; Malik, Tanu

doi:10.1016/j.envsoft.2018.03.025

Cited by 29 publications

(9 citation statements)

References 16 publications

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“…Institutional libraries are transitioning to offer online repositories to house digital research artifacts 29–32 . Within our fields of hydrology and water resources, recent tools provide environments to store data publicly and allow software to operate on the data as well as create virtual environments that package code, data, and a working operating system to reduce problems of incompatibility 33,34 . Authors can assign digital object identifiers (DOIs) to research packages to create persistent links and use umbrella research licenses to describe the manner in which these digital artifacts and their associated paper may be legally used by others 22 .…”

Section: Introductionmentioning

confidence: 99%

Assessing data availability and research reproducibility in hydrology and water resources

et al. 2019

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There is broad interest to improve the reproducibility of published research. We developed a survey tool to assess the availability of digital research artifacts published alongside peer-reviewed journal articles (e.g. data, models, code, directions for use) and reproducibility of article results. We used the tool to assess 360 of the 1,989 articles published by six hydrology and water resources journals in 2017. Like studies from other fields, we reproduced results for only a small fraction of articles (1.6% of tested articles) using their available artifacts. We estimated, with 95% confidence, that results might be reproduced for only 0.6% to 6.8% of all 1,989 articles. Unlike prior studies, the survey tool identified key bottlenecks to making work more reproducible. Bottlenecks include: only some digital artifacts available (44% of articles), no directions (89%), or all artifacts available but results not reproducible (5%). The tool (or extensions) can help authors, journals, funders, and institutions to self-assess manuscripts, provide feedback to improve reproducibility, and recognize and reward reproducible articles as examples for others.

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Section: Introductionmentioning

confidence: 99%

Assessing data availability and research reproducibility in hydrology and water resources

et al. 2019

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“…This may require repositories to develop additional functionality (e.g., the HydroShare repository has created a linked JupyterHub environment for online execution of Jupyter Notebooks contained within HydroShare resources), services (e.g., curation), and a commitment from data producers to invest the time and effort to create and share these assets along with the data. The work of Nüst et al (2017) in developing the concept of an "executable research compendium" as a self-contained collection of data, code, and execution environment is relevant here as is the concept of a "Sciunit" developed by That, Fils, Yuan, and Malik (2017) as a reusable research object that uses application virtualization to create a container of an executable application that could be integrated with a repository like HydroShare as demonstrated by Essawy et al (2018) to enhance reproducibility.…”

Section: Enhance Support For Reproducible Sciencementioning

confidence: 99%

Assessing the state of research data publication in hydrology: A perspective from the Consortium of Universities for the Advancement of Hydrologic Science, Incorporated

et al. 2020

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Many have argued that datasets resulting from scientific research should be part of the scholarly record as first class research products. Data sharing mandates from funding agencies and scientific journal publishers along with calls from the scientific community to better support transparency and reproducibility of scientific research have increased demand for tools and support for publishing datasets. Hydrology domain-specific data publication services have been developed alongside more general purpose and even commercial data repositories. Prominent among these are the Hydrologic Information System

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“…These initiatives include the Hy-droShare web-based system for sharing hydrologic data and models, which allows hydrologists to visualize, analyse, and work with data and models on the HydroShare website (e.g. creating and publishing a web app resource; Essawy et al, 2018). Additionally, the Sharing Water-related Information to Tackle Changes in the Hydrosphere for Operational Needs (SWITCH-ON) virtual laboratory aims to explore the potential of open data for water security and management (Ceola et al, 2015).…”

Section: Enhancing Reproducible Hydrological Research and Open Sciencementioning

confidence: 99%

Using R in hydrology: a review of recent developments and future directions

Slater

Thirel²,

Harrigan

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation, and vignettes (long-form guides that illustrate how to use packages); the role of integrated development environments (IDEs); and the challenges of big data and parallel computing in hydrology. Lastly, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, application programming interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events.

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Integrating scientific cyberinfrastructures to improve reproducibility in computational hydrology: Example for HydroShare and GeoTrust

Cited by 29 publications

References 16 publications

Assessing data availability and research reproducibility in hydrology and water resources

Assessing data availability and research reproducibility in hydrology and water resources

Assessing the state of research data publication in hydrology: A perspective from the Consortium of Universities for the Advancement of Hydrologic Science, Incorporated

Using R in hydrology: a review of recent developments and future directions

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