University of South Florida Showcase

De-identified qualitative dataset of Black and Latino young men in Tampa Bay, Florida. Interviews were conducted across three phases of data collection between 2020 and 2023. While semi-structured interview guides were used, questions were iteratively refined to follow emerging themes. The analytic dataset includes participant-level experiential theme tables documenting coded excerpts and representative quotations linked to transcript locations.

Coastal communities like Tampa Bay, Florida, are exposed to multiple natural hazards, including extreme high temperatures and hurricane-related impacts such as wind, storm surge, and precipitation. The seasonality of hurricanes and high temperatures increase the chances of compound flooding-heat events, which can produce greater impacts than either hazard alone. In the future, rising global temperatures and sea levels will further increase the likelihood of these flooding-heat compound events. Nature-based solutions can help reduce the impact of these two hazards, as green spaces lower temperatures, increase infiltration rates, and enhance surface friction, effectively reducing flood propagation and depth. Here, we investigate how nature-based solutions can improve the resilience of coastal communities along Tampa Bay. To do so, we simulate storm surge flooding under different what-if scenarios that include the implementation of nature-based solutions. Nature-based solutions are simulated through different friction coefficients in a flooding model. Flooding maps are overlaid with extreme heat wave data from satellite data to characterize compound hazards at the local scale. Results identify hotspots for flooding and heat risk across Tampa Bay, quantifying the increase in flood depth under various sea rise scenarios, and pinpoint optimal locations for nature-based interventions that reduce compound flood and heat risk. The resulting maps are then used to evaluate how nature-based solutions may reduce risk in vulnerable communities, identified using Socio-Vulnerability Index (SoVI) data. Findings indicate Ana Maria Island is an optimal location to implement a nature-based solution, where flood depths could be reduced by 2.9 km².

Raw and processed data from manuscript figures describing effects of beta and alpha-adrenergic agonists and antagonists on IOP

LAMMPS input files and output analysis files for molecular dynamics simulations of vitrimers studied in the work "Reaction-Diffusion Competition Drives Anomalous Relaxation of Vitrimers".

Data Overview The dataset comprises both primary and secondary data. The primary dataset consists of trace metal concentrations measured in Spanish moss (Tillandsia usneoides) samples collected throughout the city of Tampa, Florida, USA. Spanish moss has been widely recognized as an effective biomonitor of atmospheric trace metal pollution because it absorbs atmospheric contaminants directly from the air. The secondary dataset consists of 2018 demographic and socioeconomic variables obtained from the American Community Survey (ACS) at the census block group level. The objective of this study was to investigate environmental inequality by examining the relationships between atmospheric trace metal pollution and the socioeconomic and demographic characteristics of the population. The data package includes a document called "README" which described the details of the data files, while the document "Description" provides detailed methods.

LANLoad is a geospatial screening tool designed to facilitate water quality management decisions. It provides an estimate of the relative likelihood that nutrient inputs applied at specific locations on land will impact water quality. LANLoad is based solely on physical characteristics and may be used independently or with other relevant datasets. LANLoad NEEPP was developed by the USF Ecohydrology Research Group in collaboration with the FDEP (OEAT). A publication is in review (Guerron-Orejuela et al.) and additional process documentation is available in the dataset metadata. LANLoad NEEPP is available as a single comprehensive file "LANLoad_NEEPP_Overall" and as subsets corresponding to intersections between NEEPP and 15 FL counties. The datasets consist of cells (9.6 m x 9.6 m) ranked to reflect the likelihood that nutrients applied to a given location will reach a downgradient surface waterbody. Possible ranks range from 1 to 9 with values increasing as the likelihood of nutrient transport to downgradient surface waterbodies increases. Ranks are based on 6 physical landscape parameters selected by Subject Matter Experts (SMEs) who also assigned relative weights to each parameter using the Analytical Hierarchy Process (AHP). During this exercise, the location considered by SMEs was the pilot study area, St Lucie County, FL, and the focal nutrient source was Onsite Sewage and Treatment Disposal Systems. However, LANLoad NEEPP can be used to gauge the likelihood of nutrient transport to surface waterbodies from other, similar, nutrient sources. The resulting AHP model had high internal consistency (Consistency Ratio: 0.01) and returned the following parameter weights: • Distance to Waterbody, 30.0% • Depth to Water, 21.6% • Hydraulic Conductivity, 20.7% • Potential for Flooding, 10.9% • Slope, 9.8% • Surficial Karstic Deposits, 7.0% Geospatial datasets representative of these parameters were acquired (2025 & 2026) and combined using a weighted overlay to produce LANLoad NEEPP. Performance was evaluated at multiple locations (selected via a random stratified process) within NEEPP by classifying LANLoad ranks less than or equal to 4 as “lower” and those more than or equal to 6 as “higher”. Then, 2 assessment methods were applied, both conducted blind: 1) SMEs evaluated 30 locations using best professional judgment while viewing only the underlying datasets. There was a 92% consistency rate with LANLoad NEEPP classifications. 2) The groundwater numerical model ArcNLET-Py was used to simulate uniform nutrient loading in 10 subregions containing at a total of 500 model points. This yielded a 100% consistency rate with the LANLoad NEEPP classifications where subregions identified by LANLoad NEEPP as "higher likelihood" corresponded with the highest modeled cumulative nutrient loads, while "lower likelihood" locations matched the lowest modeled cumulative nutrient loads. Contact: Kai Rains, PhD, PWS USF Ecohydrology Research Group

Bathymetry data for the Florida Middle Grounds Habitat Area of Particular Concern (HAPC), provided as a raster grid with a spatial resolution of 0.0001 decimal degrees in the WGS 84 geographic coordinate system (EPSG:4326). The dataset was derived from multibeam echosounder surveys conducted between August 2000 and August 2006 using a Kongsberg Simrad EM3000 operating at 300 kHz. Surveys were conducted by the University of South Florida under the direction of Principal Investigator Dr. David F. Naar aboard the R/V Suncoaster. Multibeam bathymetry data were processed using CARIS HIPS/SIPS software and compiled into a continuous bathymetric surface representing the Florida Middle Grounds HAPC. A related bathymetric dataset is available through NOAA's National Centers for Environmental Information (NCEI). The version archived here is provided as a regular geographic-coordinate raster grid and contains fewer missing cells than the corresponding NOAA-distributed raster product, resulting in more continuous spatial coverage across portions of the study area.

All sample data provided by NEON along with measured FKBP5 from blood.

Raw and processed data from manuscript figures describing contributions of transient, sustained, and diurnal fluctuations to IOP variability

Raw, processed, and fitted data from manuscript figures on effects of tetrodotoxin and superior cervical ganglionectomy on IOP.