A General Approach for Cloud-based Hydrologic Modeling using Jupyter Notebooks
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| Owners: | Anthony M. Castronova |
| Type: | Resource |
| Storage: | The size of this resource is 51.3 MB |
| Created: | Jun 27, 2018 at 3:46 p.m. |
| Last updated: | Jun 27, 2018 at 3:50 p.m. |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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Abstract
Continued investment and development of cyberinfrastructure (CI) for water science research is transforming the way future scientists approach large collaborative studies. Among the many challenges, that we as a community need to address, are integrating existing CI to support reproducible science, enabling open collaboration across traditional domain and institutional boundaries, and extending the lifecycle of data beyond the scope of a single project. One emerging solution for addressing these challenges is HydroShare JupyterHub which is an open-source, cloud-based, platform that combines the data archival and discovery features of HydroShare with the expressive, metadata-rich, and self-descriptive nature of Jupyter notebooks. This approach offers researchers a mechanism for designing, executing, and disseminating toolchains with supporting data and documentation. The goals of this work are to establish a free and open source platform for domain scientists to (1) conduct data intensive and computationally intensive collaborative research, (2) utilize high performance libraries, models, and routines within a pre-configured cloud environment, and (3) enable dissemination of research products. This presentation will discuss our approach for hydrologic model simulation, sensitivity analysis, and optimization applications in this platform by establishing a generic CI pattern that can be adopted to support research, classroom, and workshop activities
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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