# data[].attributes.title
Addressing COVID-19 Testing Disparities in Vulnerable Populations Using a Community JITAI (Just in Time Adaptive Intervention) Approach: RADxUP Phase III

# data[].attributes.grants[].title
Addressing COVID-19 Testing Disparities in Vulnerable Populations Using a Community JITAI (Just in Time Adaptive Intervention) Approach: RADxUP Phase III

# data[].attributes.grants[].abstract
Vulnerable populations including those with medical comorbidities, people living in rural settings and minorities experience significant COVID-19 disparities. Additionally, Hispanics and Blacks are significantly more likely to be infected and hospitalized when compared to White, Non-Hispanics. The proposed study builds on RADx-UP Phases I & Phase II work reaching these populations in three racially diverse regions: Houston/Harris County, South Texas, and Northeast Texas to increase SARS-CoV-2 testing, vaccination, and risk mitigation behaviors to reduce COVID-19 morbidity, mortality, and inequities among underserved populations in Texas. The proposed study leverages the partnerships and resources of the Center for Clinical and Translational Science (CCTS) including long-standing community partnerships. Phase III will include mixed methods and community-engaged approaches to inform adaptation of our existing (and the development of new) multilevel intervention messages, materials and strategies with a focus on increasing rapid SARS-CoV-2 testing. It will also include a broader focus on addressing the social determinants of health (SDOH) and an emphasis on combating misinformation. Innovative elements of the proposed study include testing a novel approach to optimize community engagement that uses real-time data to inform intervention adaptation and implementation, using advances in social computing and machine learning to better understand patterns of misinformation in social media, and using multilevel social network analysis techniques to increase intervention agility, intensity, and reach. This project has three aims: Aim 1) Expand existing sources of population-based COVID-19 surveillance data to quantify infection, testing and vaccination trends in three Texas regions, and use innovative methods to inform and evaluate the proposed interventions; Aim 2) Adapt, implement and evaluate the multilevel community just-in-time adaptive intervention (MC-JITAI) developed in Phase II to increase SARS-CoV-2 testing, mitigation behaviors, and COVID-19 vaccination, among underserved and vulnerable populations in three regions of Texas; and Aim 3) Determine the feasibility and effectiveness of leveraging multilevel social networks to improve SARS-CoV-2 testing and COVID-19 vaccination, in three regions of Texas.

# data[].attributes.grants[].keywords
['2019-nCoV', 'Address', 'Adopted', 'Attitude', 'Behavior', 'Black Populations', 'COVID-19', 'COVID-19 disparity', 'COVID-19 morbidity', 'COVID-19 severity', 'COVID-19 surveillance', 'COVID-19 susceptibility', 'COVID-19 testing', 'COVID-19 vaccination', 'Censuses', 'Clinical Sciences', 'Collaborations', 'Communities', 'Community Health Aides', 'Comparison arm', 'Country', 'County', 'Data', 'Development', 'Disease', 'Economics', 'Education and Outreach', 'Effectiveness', 'Elements', 'Foundations', 'Hispanic Populations', 'Hospitalization', 'Individual', 'Infection', 'Influentials', 'Information Networks', 'Infrastructure', 'Intervention', 'Light', 'Machine Learning', 'Medical', 'Methods', 'Minority Groups', 'Misinformation', 'Monitor', 'Not Hispanic or Latino', 'Pathway Analysis', 'Pattern', 'Persons', 'Phase', 'Population', 'RADx Underserved Populations', 'Resources', 'Rural Minority', 'Science', 'Services', 'Severe Acute Respiratory Syndrome', 'Social Network', 'Source', 'South Texas', 'Techniques', 'Testing', 'Texas', 'Time', 'Translational Research', 'Trust', 'Underserved Population', 'Use Effectiveness', 'Vaccination', 'Vaccines', 'Variant', 'Vulnerable Populations', 'Waxes', 'Work', 'adaptive intervention', 'analytical method', 'antigen test', 'behavior test', 'clinical center', 'community engagement', 'community partnership', 'comorbidity', 'comparison group', 'contextual factors', 'design', 'disparities in morbidity', 'experience', 'health disparity', 'improved', 'innovation', 'interest', 'mortality', 'mortality disparity', 'novel', 'novel strategies', 'pandemic disease', 'phase 3 study', 'population based', 'racial diversity', 'randomized trial', 'rapid testing', 'risk mitigation', 'rural setting', 'social', 'social computing', 'social health determinants', 'social interventions', 'social media', 'surveillance data', 'testing access', 'therapy design', 'trend', 'wastewater surveillance']

# data[].attributes.grants[].title
CIC-E: COVID Information Commons Extension for Pandemic Recovery

# data[].attributes.grants[].abstract
The COVID Information Commons, CIC (http://covidinfocommons.datascience.columbia.edu/), was established in May 2020 via an NSF COVID Rapid Response Research (RAPID) award to facilitate information sharing and collaboration across NSF-funded COVID research efforts. The  initial focus was on compiling publicly available information from all COVID-related projects funded by the various Directorates across NSF in order to create a easily searchable corpus. In addition to the publicly available information, the CIC also collected self-report information from the projects via a voluntary survey. A webinar series was created, featuring talks by researchers from the NSF-funded COVID RAPID research projects. The CIC Extension will extend this initial CIC effort to also include all projects funded by NSF related to the pandemic recovery phase. In addition, it will seek to include publicly available information on COVID-related efforts beyond those funded by the NSF.The initial CIC effort clearly demonstrated the benefits of bringing together information about a diverse set of COVID-related projects into a single place, thereby enabling interested users to efficiently search for information and discover linkages among diverse efforts. This helped foster the creation of a community and helped catalyze collaborations. The CIC Extension will carry forward this idea to include projects in the pandemic recovery phase, and will additionally incorporate contemporary ways of interacting with the information such as via search and discovery of linked information using semantic search methods, and the use of domain ontologies and knowledge graph mechanisms.Broader impact is central to the idea of the COVID Information Commons, which pulls together publicly available information along with voluntary self-report information on NSF-funded COVID-related research projects in order to enable search and discovery of information and collaborations among individual efforts. The CIC has demonstrated early successes in creating such collaborations among researchers from diverse scientific disciplines and from different parts of the country drawn together by their common interest in studying the COVID pandemic. By extending the CIC effort to the pandemic recovery phase, the CIC Extension will reach an even larger and  more diverse community of COVID researchers and facilitate networking among researchers engaged in COVID-related research. The CIC Extension will also build upon and expand the successful research webinar series and undergraduate engagement programs initiated in the initial phase of this effort.This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).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.

# data[].attributes.grants[].abstract
This project will create a COVID Information Commons (CIC) website to facilitate knowledge sharing and collaboration across various COVID research efforts, especially focusing on all the NSF-funded COVID Rapid Response Research (RAPID) projects. The CIC will serve as a resource for researchers as well as decision-makers from government, academia, not-for-profit and industry to leverage each other's findings, and invest in and accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic. It will also serve as a model for integrated knowledge sharing and collaboration on other public health challenges, in benefit to society. Projects will be able to enter and publish information about their efforts in ways that are most relevant and user-friendly for a variety of potential stakeholders from academia, industry, government, and non-profit sectors. Information will be organized in multiple ways, for example, by research topics areas and by geography. In addition to information from NSF COVID-19 RAPID projects, the COVID Information Commons will incorporate coronavirus-related information from NSF Open Knowledge Network projects, as well as from other NSF research projects in general. The COVID Information Commons will utilize information science methods to bring together information about the collection of COVID-19 RAPID projects funded by the National Science Foundation. A wide array of research efforts are underway to study the impacts of the pandemic in fields as far ranging as biophysics, social justice/inequity, behavioral science, public health, supply chains, and risk management. The CIC will semantically link information across projects to provide a more holistic view across distinct efforts, including efforts such as the COVID projects in the NSF Open Knowledge Network. The resulting, concise, curated, integrated resource will provide insight into NSF-funded COVID RAPID projects and facilitate collaborations among such efforts. These objectives will be achieved using information science approaches to 1) compile a comprehensive list of NSF COVID RAPID awards, along with relevant details for each project, 2) link to any publicly available data sets and data feeds, 3) organize the information and data feeds, for example, by categories of research areas and/or geography, using a meta-data schema developed for the resource and existing taxonomy and semantic frameworks; 4) design and develop a web portal to allow project teams to publish their data, or links to the data, and present project information in ways that are most relevant and user friendly for researchers in academia, industry, and government; 5) integrate the schema.org COVID-19 annotated data to enable more effective identification, retrieval, and integration of relevant data. A Minimum Viable Product for the website will be developed first, working with stakeholders in the community to prioritize features and add new functionality. In addition to the Information Commons, the project will also assess the effort and feasibility of implementing a data and model commons—to share datasets as well as data-driven models, such as machine learning models related to COVID-19.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities with funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.

# data[].attributes.grants[].keywords
['COVID-19', 'coronavirus', 'researcher collaboration', 'SARS-CoV-2', 'Future pandemic prevention', 'long COVID research', 'COVID research and recovery']

# data[].attributes.title
A multi-modal sensor dataset for continuous stress detection of nurses in a hospital

# data[].attributes.grants[].title
I/UCRC Phase II Renewal: Center for Visual and Decision Informatics (CVDI)

# data[].attributes.grants[].funder_divisions
['Computer and Information Science and Engineering (CISE)', 'IUCRC-Indust-Univ Coop Res Ctr']

# data[].attributes.grants[].abstract
This award will help the University of Louisiana at Lafayette transition its IUCRC Center for Visual and Decision Informatics (CVDI) into Phase 2 operation for the next 5 years. At the start of Phase 2, the CVDI IUCRC is expanding from three sites to 5 sites, with the addition of two new sites, and University of Louisiana at Lafayette will serve as the managing lead site of the Center for this phase. The Center seeks to continue its record of sustained accomplishments in terms of strengthening and growing industry partnerships, delivering innovation through intellectual property and publications, expanding research competencies through increased faculty participation, and broadening educational experience of students through participation in industry funded research.

The research program of the Center focuses on developing next-generation visual and decision support tools and techniques that enable decision makers to fundamentally improve the way an organization's information is interpreted and exploited. CVDI's area of research is industry-relevant, attracts membership and has the potential to improve US competitiveness in this area. The center intends to broaden participation through center engagement in university programs. Through participation in the Louis Stokes Louisiana Alliance for Minority Participation Program, of which the PI is the co-coordinator, this site will introduce undergraduate minority students to industry-driven research. CVDI will recruit students from neighboring Historically Black Colleges and Universities to join our graduate programs. The site will also participate in university outreach programs for K-12 students, such as the annual Engineering and Technology Expo Week and the Science Day.

# data[].attributes.title
Perspectives on sexual and reproductive health self-care among women, healthcare providers, and other key informants: a mixed-methods study in South Africa and Zambia

# data[].attributes.title
Data for: What Extraordinary Times Tell Us about Ordinary Ones: A Multiple Case Study of Precariously Employed Food Retail and Service Workers in Two U.S. State Contexts during the COVID-19 Pandemic

# data[].attributes.publications[].title
What Extraordinary Times Tell Us About Ordinary Ones: A Multiple Case Study of Precariously Employed Food Retail and Service Workers in Two U.S. State Contexts during the COVID-19 Pandemic

# data[].attributes.title
This Is Not Online Education. It’s Disaster Education”: A Qualitative Study of Remote and Hybrid School Climates during COVID-19

# data[].attributes.publications[].title
School Mental Health Professionals’ Perceptions of Principal Leadership and Working Conditions During Remote and Hybrid School Operations During the COVID-19 Pandemic

# data[].attributes.title
COVID Future panel survey: A unique public dataset documenting how U.S. residents’ travel-related choices changed during the COVID-19 pandemic

# data[].attributes.title
Pharmacogenetics: Using science to reduce racial bias in pain management?

# data[].attributes.title
Improving pediatric COVID-19 vaccine uptake using an mHealth tool: a randomized, controlled trial

# data[].attributes.title
Exploring Well-Being Disparities: A Comparative Analysis of Urban and Rural Clinicians Using the NIOSH Worker Well-Being Questionnaire.