HydroShare 2017-2021 Proposal: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis
|Authors:||David Tarboton · Dan Ames · Martyn Clark · Alva Lind Couch · Jeffery S. Horsburgh · Ray Idaszak · Michael J. Stealey · Hong Yi · Shaowen Wang|
|Resource type:||Composite Resource|
|Created:||Dec 21, 2017 at 3:54 p.m.|
|Last updated:||Dec 21, 2017 at 4:21 p.m. by David Tarboton|
This resource holds the narrative text of the proposal funded by NSF to support development of HydroShare from 2017-2021. A second document lists the scope of work adjustments due to NSF not being able to provide the full funding requested.
This work is being pursued through three collaborative NSF Awards
Researchers across the country and around the world expend tremendous resources to gather and analyze vast stores of hydrologic data and populate a myriad of models to better understand hydrologic phenomena and find solutions to vexing water problems. Each of those researchers has limited money, time, computational capacity, data storage, and ability to put that data to productive use. What if they could combine their efforts to make collaboration easier? What if those collected data sets and processed model outputs could be used collaboratively to help advance hydrologic understanding beyond their original purpose? HydroShare is a system to advance hydrologic science by enabling the scientific community to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare supports the sharing and publication of hydrologic data and models. This capability is necessary for community model development, execution, and evaluation and to improve reproducibility and community trust in scientific findings through transparency. As a platform for collaboration and running models on advanced computational infrastructure, HydroShare enhances the capability for data intensive research in hydrology and other aligned sciences. HydroShare is designed to help researchers easily meet the sharing requirements of data management plans while at the same time providing value added functionality that makes metadata capture more effective and helps researchers improve their work productivity. This project will extend the capabilities of the HydroShare cyberinfrastructure to: (1) enhance support for scientific methods enabling systematic data and model analysis and hypothesis testing; (2) advance the social capabilities of HydroShare to enable improved collaborative research; (3) integrate with 3rd party consumer data storage systems to provide more flexible and sustainable data storage; and (4) establish an application testing environment to empower researchers to develop their own computer programs to act on and work with data in HydroShare.
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This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/
|David Tarboton||Utah State University||4357973172|
|Dan Ames||Brigham Young University|
|Alva Lind Couch||Tufts University/CUAHSI||MA, USA||617 627 3674|
|Jeffery S. Horsburgh||Utah State University||Utah, US||(435) 797-2946|
|Ray Idaszak||RENCI, University of North Carolina at Chapel Hill||NC, US|
|Michael J. Stealey||RENCI||(919) 445-9653|
|Hong Yi||RENCI at University of North Carolina at Chapel Hill||NC, US|
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This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis||OAC-1664061|
|National Science Foundation||Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis||OAC-1664018|
|National Science Foundation||Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis||OAC-1664119|
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