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1. NSF Belmont Forum Collaborative Research: Advancing Resilience in Low-Income Housing Using Climate-Change Science and Big Data Analytics

   https://doi.org/10.17603/ds2-1kpb-dv70  

   Obonyo, Esther; Mutunga, James; Mirhosseini, Homeira (January 2025, Designsafe-CI)

   Project Overview: This NSF-funded project (Award #2019754) is part of the Belmont Forum’s Disaster Risk, Reduction, and Resilience (DR3) initiative, a global effort to assess and mitigate disaster risks through transdisciplinary collaboration. The study investigates strategies to enhance the resilience of low-income communities living in flood-prone and climate-vulnerable regions, with a geographic focus on Brazil, East Africa, and the southeastern United States. The U.S. component centers on coastal and urban communities in Florida, particularly those at risk from flooding and extreme weather events. Research Objectives: Through a transdisciplinary approach, the project integrates machine learning, geospatial analytics, and socio-economic data to: - Assess community-level vulnerabilities to flooding and extreme heat, -Identify barriers to adopting disaster-resilient housing, - Co-design affordable, climate-resilient housing prototypes using sustainable, locally sourced materials. The research aims to support community-informed design strategies and policy recommendations that are adaptable across different socio-economic and geographic contexts. Dataset Description: The dataset contains responses from approximately 500 residents aged 18+ living in low-income, flood-prone neighborhoods in Florida. The survey captures detailed information on: - Housing conditions and infrastructure, - Disaster preparedness and flood risk perception, - Access to services during and after disasters, - Health and economic impacts of extreme weather events, - Community cohesion and recovery strategies. This dataset serves as a resource for researchers, urban planners, emergency response agencies, and policymakers seeking data-driven insights to inform resilient housing design, climate adaptation, and disaster recovery planning. Data Collection and Anonymity: Survey distribution and data collection were conducted in partnership with Centiment, a third-party research company that recruits demographically targeted panels for academic and applied research. For this study, Centiment distributed the survey to residents of low-income, flood-prone communities in Florida, based on geographic and socio-economic criteria specified by the research team. All personally identifiable information (PII), such as IP addresses, email addresses, and precise geolocation data, was removed prior to uploading the dataset to DesignSafe. The dataset has been reviewed to ensure participant anonymity in accordance with DesignSafe data protection policies and applicable ethical standards. 

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2. Answering big questions with big data: Using analytics across the research ecosystem

   https://doi.org/10.13023/9FZ6-7J52  

   Beynon, Ann (April 2024, University of Kentucky Libraries)

   The research landscape continues to shift and evolve. With challenges around research integrity and security growing, research leaders must rely on the highest quality data to make confident decisions. Universities and funders need to understand the societal impact of research investments in addition to the scholarly impact. Has research helped to advance the UN Sustainable Development Goals? What are the emerging research areas that will lead to commercial innovations and sustainability advancements? We will discuss how quantitative data from Web of Science and InCites can help research stakeholders understand these big questions and inform their strategic research decisions. Presented at the 2024 Research Analytics Summit in Albuquerque, NM 

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3. Automation, Big Data and Politics: A Research Review

   Shorey, S; Howard, Philip N. (October 2016, International journal of communication)

   We review the great variety of critical scholarship on algorithms, automation, and big data in areas of contemporary life both to document where there has been robust scholarship and to contribute to existing scholarship by identifying gaps in our research agenda. We identify five domains with opportunities for further scholarship: (a) China, (b) international interference in democratic politics, (c) civic engagement in Latin American, (d) public services, and (e) national security and foreign affairs. We argue that the time is right to match dedication to critical theory of algorithmic communication with a dedication to empirical research through audit studies, network ethnography, and investigation of the political economy of algorithmic production. 

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4. Big Spatiotemporal Data Analytics: a research and innovation frontier

   https://doi.org/10.1080/13658816.2019.1698743  

   Yang, Chaowei; Clarke, Keith; Shekhar, Shashi; Tao, C. Vincent (January 2019, International Journal of Geographical Information Science)
5. Big data, data privacy, and plant and animal disease research using GEMS

   https://doi.org/10.1002/agj2.20933  

   Senay, Senait_D; Shurson, Gerald_C; Cardona, Carol; Silverstein, Kevin_AT (November 2021, Agronomy Journal)

   Abstract One of the major challenges in ensuring global food security is the ever‐changing biotic risk affecting the productivity and efficiency of the global food supply system. Biotic risks that threaten food security include pests and diseases that affect pre‐ and postharvest terrestrial agriculture and aquaculture. Strategies to minimize this risk depend heavily on plant and animal disease research. As data collected at high spatial and temporal resolutions become increasingly available, epidemiological models used to assess and predict biotic risks have become more accurate and, thus, more useful. However, with the advent of Big Data opportunities, a number of challenges have arisen that limit researchers’ access to complex, multi‐sourced, multi‐scaled data collected on pathogens, and their associated environments and hosts. Among these challenges, one of the most limiting factors is data privacy concerns from data owners and collectors. While solutions, such as the use of de‐identifying and anonymizing tools that protect sensitive information are recognized as effective practices for use by plant and animal disease researchers, there are comparatively few platforms that include data privacy by design that are accessible to researchers. We describe how the general thinking and design used for data sharing and analysis platforms can intrinsically address a number of these data privacy‐related challenges that are a barrier to researchers wanting to access data. We also describe how some of the data privacy concerns confronting plant and animal disease researchers are addressed by way of the GEMS informatics platform. 

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