
NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
Recipient: |
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Initial Amendment Date: | August 27, 2018 |
Latest Amendment Date: | August 27, 2018 |
Award Number: | 1761969 |
Award Instrument: | Standard Grant |
Program Manager: |
Wendy Nilsen
wnilsen@nsf.gov (703)292-2568 OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 1, 2018 |
End Date: | August 31, 2022 (Estimated) |
Total Intended Award Amount: | $651,000.00 |
Total Awarded Amount to Date: | $651,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1960 KENNY RD COLUMBUS OH US 43210-1016 (614)688-8735 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1960 Kenny Road Columbus OH US 43210-1016 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | BD Spokes -Big Data Regional I |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
The opioid crisis ravaging Ohio and the Midwest disproportionally affects small and rural communities. Harnessing and deploying data holds promise for developing a response to this crisis by policymakers, healthcare providers, and citizens of the communities. Currently, there are many barriers to getting data into the hands of individuals on the frontlines. Crucial data are siloed across law enforcement, public health departments, hospitals and clinics, and county administrations; data often are inaccurate or collected in non-standard ways across different agencies and departments; the stigma of drug abuse limits accurate reporting of drug-related deaths; and information is not shared with the community and other stakeholders because of the lack of a privacy and security framework. Such barriers, for example, prevent individuals with addictions or their families and friends from locating available treatment centers or obtaining other important information in a timely way. Similarly, it is difficult for first responders and healthcare providers to obtain critical up-to-date information. In predominantly rural counties, these challenges are especially daunting because there is often poor connectivity and communication infrastructure. This Big Data Spoke project involves developing scalable, flexible, and connectivity-rich data-driven approaches to address the opioid epidemic. The cyberinfrastructure framework, OpenOD, will be initially designed and deployed in small and rural communities in Appalachia Ohio and the Midwest, where the need for data and connection are greatest. Based upon significant community input, OpenOD will also create end-user applications or enterprise solutions to support stakeholders and communities to mount a response they feel will be most efficient and beneficial at the local level. As a Spoke to NSF?s Midwest Big Data Hub, our efforts can be efficiently scaled, disseminated, and applied to the opioid and other societal problems such as infant mortality, crime, and natural disasters. This project fits within NSF's mission to promote the progress of science (contribute to the science and engineering of large socially relevant cyberinfrastructures) to advance the health and welfare of US citizens (by linking data sources in new and useful ways to empower communities to address societal problems; establishing sustainable partnerships between academia, industry, government and communities; increasing data literacy and community engagement with data science; and enhancing research and education via development/adaptation of training modules and courses in data analytics).
The main goal of this project is to help small and rural communities in the Midwest address the opioid epidemic via BIGDATA (BD) technology. While no communities have been spared, small and rural communities face unique challenges in confronting the opioid epidemic: knowledge and data exist in siloes across multiple organizations with varying jurisdictional boundaries; efforts to collect, link, and analyze data are hampered by a lack of infrastructure and tools; rural areas are plagued by "dead zones" in cellular connectivity; communities lack capacity for data collection, and analytics; needs and resources across effected communities are not uniform and require BD approaches that are flexible, open, leverage significant community input, and can be dutifully validated. Our proposed solution is OpenOD, a framework that provides uniform, relevant and timely access to data. Working integrally with the Midwest Big Data Hub (MBDH) and our partners, our three main objectives are to: (1) Work with local communities to understand strengths and gaps in cyberinfrastructure, data availability, and need for data analytics workforce skills. (2) Assemble flexible cyberinfrastructure that includes a data commons, stakeholder-usable and cloud-amenable data analytics and visualization tools, and internet connectivity with both mobile and non-mobile capabilities. (3) Validate, evaluate, and disseminate cyberinfrastructure and data analytics tools to stakeholder groups throughout the region while fostering new partnerships. OpenOD will create approaches that will allow governing units to deploy openly available tools rather than rely on proprietary tools. In this way, existing disparities in data access and ensuing responses are effectively addressed. The potential contributions of the project are to: (1) Increase BD and STEM literacy and community engagement in underrepresented groups given the operating milieu of OpenOD in rural areas where the population is indigent and lacks adequate skills to join the modern workforce. (2) Improve well-being of individuals in society by linking data sources in new and useful ways to empower communities to address the opioid crisis; improved connectivity and timely delivery of critical information will accelerate community responsiveness and improve preventive strategies. (3) Provide infrastructure for research and education will be improved given that project activities will deliver linked, curated data sets to community stakeholders, researchers and educators. Training modules and courses adapted and developed and shared with local/regional educators and will remain with the communities after the funding period has ended. In addition, new and established partnerships will allow sustainability of the project in the communities for the long-term.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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.
The overarching goal of this project was to create an open-source framework, OpenOD, that provided uniform, relevant, and timely access to data to help resource-limited rural agencies address pandemics like the opoiod crisis that ravaaged the rural Midwest. Working integrally with the Midwest Big Data Hub (MBDH) and all partners, we achieved the following three goals: (i) work with local communities to understand strengths and gaps in cyberinfrastructure, data availability, and the need for data analytics workforce skills; (ii) assemble a flexible cyberinfrastructure that includes a data commons, stakeholder-usable, and cloud-amenable data analytics and visualization tools, and internet connectivity with both mobile and non-mobile capabilities; (iii) calidate, evaluate, and disseminate cyberinfrastructure and data analytics tools to regional stakeholder groups while fostering new partnerships.
The three aims were accomplished at the three partnering institutions namely Nationwide Children's Hospital (NCH), The Ohio State University (OSU), and University of South Carolina (UofSC).
The intellectual merit was enhanced by following achievements:
- The project team developed OpenOD and COVID-Commons(www.od.commmons.osu.edu) for use by the OSU research community. CommonsOD is based on the open-source Dataverse platform available from dataverse.org and enables rich metadata, permissions control of data sets, and an application programming interface (API), among other features. The data commons has already proved useful beyond this project.
- The problem of middle-mile wireless network optimization was addressed in this grant, which seeks to connect the edge-access terminals to core network service providers with minimal infrastructure cost and throughput guarantee for each edge-access terminal. More generally, the diverse aspects of this complex optimization problem---selecting network type (WiFi or TV White Spaces, for example), antenna type (point-to-point, point-to-multipoint, broadcast), access point, and tower selection, among others---can warrant solutions that involve a hybrid network of both WiFi and TVWS technologies. Our research has developed the first polynomial-time approximation solution for a generalized version of the middle-mile network optimization problem.
- The use of social media to track and monitor opioid use was demonstrated in this projec. The project initially focussed on Ohio counties. Later, the project pivoted this work to the national scope with a focus on individual states in the context of COVID-19 given that the volume of tweets from rural counties in Ohio was too small to do meaningful analysis, and further with the pandemic, experts and news identified that addiction and mental health are two of the most significant issues affecting society. A novel aggregate and empirical measure, the Social Quality Index (SQI), was used to track psychological and addictive conditions of individuals.
- The project developed and evaluated a clinical-text-based method for identifying Nenonatal Addiction Syndrome (NAS) cases. The results suggest that many babies with intrauterine drug exposure did not have the NAS diagnosis codes. These results suggest that there will be other significant public health implications if only diagnosis codes are used to evaluate healthcare needs and the impact of neonatal exposure to opioids.
The broader impacts are also many,In addition to training staff and students in interdisciplinary settings, this project was leveraged by several other large and significant projects. With the onset of the COVID-19 pandemic the progress ion OpenOD was styimed. Project personnel pivoted to addressing the needs of various communities to combat the COVID-19 pandemic. Standing up COVID-Commons led to additional benefits towards another NSF-funded RCN project, Smart Foodshed. COVID-Commons is still currently being used by OSU researchers to study misinformation related to COVID-19, and the infrastructure and workflows that were established for setting up COVID-Commons is now being used to assist local and state health departments and school districts in their COVID-19 response efforts. A subset of the project team are also prominent members of the NSF AI Institute ICICLE dedicated to the construction of AI cyberinfrastructure. Many of the ideas discussed in this project were realized to build data and model commons for the ICICLE AI cyberinfrastructure compatible with various use-inspired domains. Finally, another offshoot of this project is establishing a 5G AND BROADBAND CONNECTIVITY CENTER, a State of Ohio-funded project. The goal of this project is to create workforce to address the 5G networking needs in rural Ohio.
Last Modified: 03/26/2023
Modified by: Raghu Machiraju
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