NSF Org:	OAC Office of Advanced Cyberinfrastructure (OAC)
Recipient:	UNIVERSITY OF WASHINGTON
Initial Amendment Date:	July 9, 2018
Latest Amendment Date:	April 7, 2023
Award Number:	1829585
Award Instrument:	Standard Grant
Program Manager:	Ashok Srinivasan OAC  Office of Advanced Cyberinfrastructure (OAC) CSE  Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2018
End Date:	August 31, 2023 (Estimated)
Total Intended Award Amount:	$446,392.00
Total Awarded Amount to Date:	$446,392.00
Funds Obligated to Date:	FY 2018 = $446,392.00
History of Investigator:	Anthony Arendt (Principal Investigator) arendta@uw.edu Bart Nijssen (Co-Principal Investigator) Erkan Istanbulluoglu (Co-Principal Investigator) Bart Nijssen (Former Principal Investigator) Christina Norton (Former Principal Investigator) Bart Nijssen (Former Co-Principal Investigator) Anthony Arendt (Former Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Washington 4333 BROOKLYN AVE NE SEATTLE WA  US  98195-1016 (206)543-4043
Sponsor Congressional District:	07
Primary Place of Performance:	University of Washington 4333 Brooklyn Ave NE Seattle WA  US  98195-2700
Primary Place of Performance Congressional District:	07
Unique Entity Identifier (UEI):	HD1WMN6945W6
Parent UEI:
NSF Program(s):	CyberTraining - Training-based, Special Initiatives
Primary Program Source:	01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	026Z, 062Z, 7361, 9102, 9179
Program Element Code(s):	044Y00, 164200
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Studies of water and environmental systems are becoming increasingly complex and require integration of knowledge across multiple domains. At the same time, technological advances have enabled the collection of massive quantities of data for studying earth system changes. Fully leveraging these datasets and software tools requires fundamentally new approaches in the way researchers store, access and process data. The project serves the national interest by motivating a culture shift within the hydrologic and more broadly earth science communities toward open and reproducible software practices that will enhance interdisciplinary collaboration and increase capacity for addressing complex science challenges around the availability, risks and use of water. Project's CyberTraining approach provides virtual learning experiences throughout an academic year, with online learning modules oriented around a one-week in-person workshop (WaterHackWeek) that will focus on hands-on real-world research projects. These research projects are designed to serve the national interest by preparing for natural hazards such as floods, hurricanes and climate change, and to advance the nation's health by making tools and data accessible to health researchers, local governments, and citizens.

New cyberinfrastructure that emphasizes data sharing and open, reproducible software practices is currently in development, but requires a mode of knowledge transfer, or CyberTraining, that extends beyond currently available university curriculum. Project's aim is to ensure successful use of community cyberinfrastructure to 1) publish large datasets, 2) run numerical models, 3) organize collaborative research projects, and 4) meet journal requirements to follow open data standards. The activities take advantage of HydroShare, a National Science Foundation funded cyberinfrastructure platform, operated by the Consortium of Universities Allied for Hydrologic Sciences (CUAHSI), for sharing hydrologic data and models. The short-term goals are to develop new CyberTraining modules; the long-term goals are to have an annually recurring WaterHackWeek, to distribute curriculum CUAHSI to more than 130 member universities, and advance cyberinfrastructure education for the broader geoscience community. The use of the hackweek educational model extends the use of cyberinfrastructure to promote the progress of science by including a specific emphasis on graduate student training as instructors, training coordinators, and building research networks with data providers who are stakeholders outside of academia. For example, case studies include data and resource management by Native American tribal governments, Hurricane Maria data archive for research in Puerto Rico, improving flood forecasting, and tool-building using complex numerical models such as the National Water Model. This project allows to test the educational model in the water research community, in addition to connecting team's research and curriculum to annually recurring hackweeks in neuro, astro, ocean, and geo sciences. The team of researchers is actively engaged in experimenting with this new model, and in testing its efficacy through robust evaluation metrics. The proposed activities encourage collaboration and support for use of cyberinfrastructure at all stages of the educational pipeline and provides participants with opportunities for networking, career development, community building and design of open-source software tools.

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.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

 

Studies of water and environmental systems are becoming increasingly complex. There has been a rapid expansion of satellite, airborne and ground observations, together with increasingly sophisticated models, creating ever-increasing volumes of data. This project provided training to people conducting hydrological research who need to broaden their skill with the tools of data science so they can work with these datasets. We engaged with members of the hydrological community to better understand their needs, and then helped to design tutorials, workshops and collaborative activities that addressed these needs. Tutorial content focused on facilitating data access, creating cloud computing workflows, algorithm development and visualization of model output. Tutorials were shared publically so that everyone could access them and contribute to their development and improvement. Projects were designed around existing hydrological use cases and addressed themes related to snow distribution, water resource mapping and hydrological simulations. Both tutorials and projects were featured in a series of interactive, collaborative events called "hackweeks". Hackweeks are open to all members of the hydrological community and were offered in person at the University of Washington campus and virtually online. Before each hackweek we trained members of a small organizing team in our approaches for creating interactive learning spaces and inclusive team dynamics for project work. Participants helped co-shape project outcomes, and several projects spawned collaborative work that continued after the events. All of our training and project work was conducted in the framework of open and reproducible science. This means that we prioritized code sharing using version control software, data sharing on centralized cloud platforms, and algorithm development on open, distributed computing hubs. As the project matured we shifted our focus toward mentoring others in the hydrological community to apply the principles of our collaborative and training models to the needs of their own community.

 

Last Modified: 11/17/2023
Modified by: Anthony A Arendt

Please report errors in award information by writing to: awardsearch@nsf.gov.