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Hydroshare, JupyterHub, and strategies for collaborative and cloud based data sharing, modeling and analysis


Authors: David Tarboton · Anthony Michael Castronova · Jonathan Goodall · Dandong Yin · Shaowen Wang · Martyn Clark · Christina Bandaragoda · Tanu Malik
Owners: David Tarboton
DOI:10.4211/hs.cf97755bf8ba43b8b67cf5b376eca7dc How to Cite
Resource type:Composite Resource
Created:Jun 03, 2018 at 4:25 a.m.
Last updated: Aug 28, 2018 at midnight by David Tarboton

Abstract

Advances in many domains of earth science increasingly require integration of information from multiple sources, reuse and repurposing of data, and collaboration. HydroShare is a web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI). HydroShare includes a repository for users to share and publish data and models in a variety of formats, and to make this information available in a citable, shareable, and discoverable manner. HydroShare also includes tools (web apps) that can act on content in HydroShare, providing users with a gateway to high performance computing and computing in the cloud. Jupyter notebooks, and associated code and data are an effective way to document and make a research analysis or modeling procedure reproducible. This presentation will describe how a Jupyter notebook in a HydroShare resource can be opened from a JupyterHub app using the HydroShare web app resource and API capabilities that enable linking a web app to HydroShare, reading of data from HydroShare and writing of results back to the HydroShare repository in a way that results can be shared among HydroShare users and groups to support research collaboration. This interoperability between HydroShare and other cyberinfrastructure elements serves as an example for how EarthCube cyberinfrastructure may integrate. Base functionality within JupyterHub supports data organization, simple scripting and visualization, while Docker containers are used to encapsulate models that have specific dependency requirements. This presentation will describe the strategy for, and challenges of using models in Docker containers, as well as using Geotrust software to package computational experiments as 'geounits', which are reproducible research objects that describe and package computational experiments.

Presentation at EarthCube all hands meeting, June 6-8, 2018, Washington, DC https://www.earthcube.org/ECAHM2018

Subject Keywords

Jupyter,Earthcube,ECAHM18

How to cite

Tarboton, D., A. M. Castronova, J. Goodall, D. Yin, S. Wang, M. Clark, C. Bandaragoda, T. Malik (2018). Hydroshare, JupyterHub, and strategies for collaborative and cloud based data sharing, modeling and analysis, HydroShare, https://doi.org/10.4211/hs.cf97755bf8ba43b8b67cf5b376eca7dc

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
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Content

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Authors

The people or organizations that created the intellectual content of the resource.

Name Organization Address Phone Author Identifiers
David Tarboton Utah State University 4357973172 ORCID
Anthony Michael Castronova CUAHSI MA, US 3399334127
Jonathan Goodall University of Virginia Virginia, US
Dandong Yin
Shaowen Wang
Martyn Clark NCAR
Christina Bandaragoda University of Washington Washington, US
Tanu Malik
Relations
cites: Tarboton, D., A. M. Castronova (2018). Hydrologic Terrain Analysis Jupyter Notebook, HydroShare, http://www.hydroshare.org/resource/b6807d9df60a48babbdb18f1f9094830 (The Jupyter Notebook that was used for the live demo as part of this presentation)

Credits

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation EarthCube Building Blocks: Collaborative Proposal: GeoTrust: Improving Sharing and Reproducibility of Geoscience Applications ICER 1639655
National Science Foundation Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis OAC 1664061, 1664018, 1664119

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