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University of South Florida Showcase

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2025
1970 2025
36 results
  • PATS Detailed Treatment Protocol
    This is a detailed treatment protocol for PATS: Program for Advanced Treatment of Stuttering (copyright: Nathan Maxfield).
  • Monthly Sargassum Wet Biomass Estimates in the Western North Atlantic from MODIS Satellite Observations
    This dataset provides monthly mean Sargassum wet biomass estimates (in million metric tons) from March 2000 to February 2024, derived from MODIS observations using the methodology described in Hu et al. (2023). The data cover several subregions, including the North Sargasso Sea (NSS), South Sargasso Sea (SSS), Gulf Stream Region (GSR), Antilles Current Region (ACR), Great Atlantic Sargassum Belt (GASB), and the Northwestern Gulf of Mexico (NW_GoM). The locations of these subregions are provided in the attached READ ME file. Briefly, each image pixel is classified into one of the three types using a deep-learning computer model: Sargassum containing, Sargassum free, and invalid observation (clouds, cloud shadows, strong sun glint, etc). Then the Sargassum containing pixels are further spectrally unmixed to determine the subpixel percent (%) cover within each pixel. For a pre-gridded Sargassum map, the mean Sargassum percent cover in each 0.5-degree grid is calculated as the arithmetic average of all image pixels (both Sargassum containing and Sargassum free) in that grid collected by the satellite during that calendar month. Such a mean percent cover is converted to Sargassum wet biomass using a calibration constant of 3.34 kg per square meter of Sargassum determined from field measurements (Wang et al., 2018). Integration of the grid-specific biomass across all grids within a subregion leads to the mean Sargassum biomass for that subregion. These steps were applied to MODIS/Terra (2000 – 2024) and MODIS/Aqua (2002 – 2024) separately, with the final maps being the arithmetic average between the two. In the above steps, all MODIS data were downloaded from NASA OB.DAAC (https://oceancolor.gsfc.nasa.gov) and processed using the NASA software SeaDAS (version 8.0). The deep-learning model and spectral unmixing model as well as the method to calculate monthly means were all developed at the Optical Oceanography Lab of the University of South Florida using computer codes developed in house. The daily and weekly Sargassum maps have been made available through their Sargassum Watch System (SaWS) website: https://optics.marine.usf.edu/projects/saws.html. Hu, C., Zhang, S., Barnes, B.B., Xie, Y., Wang, M., Cannizzaro, J.P., & English, D.C. (2023). Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi‐band medium‐resolution satellite data and deep learning. Remote Sensing of Environment, 289, 113515. https://doi.org/10.1016/j.rse.2023.113515. Wang, M., Hu, C., Cannizzaro, J., English, D., Han, X., Naar, D., Lapointe, B., Brewton, R. and Hernandez, F. (2018). Remote sensing of Sargassum biomass, nutrients, and pigments. Geophysical Research Letters, 45(22), pp.12-359. https://doi.org/10.1029/2018GL078858.
  • In Situ Observations of Pelagic Sargassum Total and Morphotype-specific Density and Distribution Across the Western North Atlantic from 1993 to 2023.
    Between 1993 and 2023, 5,587 neuston net tows were conducted onboard Sea Education Association’s (www.sea.edu) SSVs Westward and Corwith Cramer during oceanographic research cruises across the North Atlantic Ocean, Caribbean Sea, and Gulf of Mexico. Cruise tracks varied seasonally and annually. The neuston net (1.0m × 0.5m frame, 33​3​ um mesh) was deployed from a boom extended 5 m off the ship’s port side while sailing on a port tack. Any wind​r​ows of Sargassum were thus crossed perpendicularly and any bow wake effect was minimized. Tows were conducted at two knots for 30 minutes for a typical distance of ~1.0 nm; tow distance (m) was calculated as the difference between start/end GPS locations (1993–2003) and from minute-by-minute GPS locations (2004–2023). Standard net processing included identifying pelagic Sargassum specimens to morphotype, patting dry, and weighing using a spring scale to yield mass (g) for each morphotype. Three common holopelagic morphotypes are reported: Sargassum natans var. natans (Sn_n), S. natans var. wingei (Sn_w), and S. fluitans (Sf). Weighted arithmetic mean Sargassum density (g/km2) was calculated from the sum of Sargassum mass divided by the sum of tow area for all tows in a given year (season, time frame) for a given region. Morphotypes reported as percentage of total collected biomass in a given year (season, time frame) for a given region. The date and GPS position were captured with each sample. All net tows were assigned to one of five regions defined on the basis of physical oceanographic boundaries. Analysis of data was organized by ecological year (March–February, corresponding with the start of the annual spring bloom period in the Sargasso Sea) and season (spring/summer March-August, fall/winter September-February). In most years, neuston tows occurred during both the spring/summer and fall/winter seasons in a region; sporadic changes in cruise track may have caused occasional missed seasons. Examined time frames (pre-MODIS 1993-1999, pre-GASB 2000-2010, early GASB 2011-2014, and recent GASB 2015-2023) were selected for comparison with satellite data and Great Atlantic Sargassum Belt (GASB) dynamics. Quality control confirmed accurate, consistent data entry and GPS positions, and filtered out incomplete records. Blank cell = no data.
  • Water Temperature in Global Lakes and Estuaries
    This spreadsheet contains summer SST trends (℃/decade) for 1,505 and winter SST trends for 740 lakes and estuaries worldwide, derived from MODIS 1-km nighttime SST data during 2003–2023. For lakes, the FID corresponds to the ID used in the HydroATLAS database. Summer is defined as July–September in the Northern Hemisphere and January–March in the Southern Hemisphere, while winter is defined as January–March in the Northern Hemisphere and July–September in the Southern Hemisphere.
  • Reflectance of floating matters compiled from literature
    This spreadsheet contains digital data and graphics of reflectance of various types of floating matters found in the natural aquatic environments. The data have been compiled from published literature, where the worksheets contain more explanations and references of the data origins.
  • Comparative Efficiency of Swab Types for Recovery of Escherichia coli and HF183 from Household Surfaces
    This dataset contains measurements of fecal indicator bacteria (Escherichia coli) and microbial source tracking (MST) gene markers (HF183 and EC23S857), as well as detections of culturable E. coli from tile surfaces experimentally inoculated with sewage influent. Swabbing was conducted using three swab types (i.e., polyester, foam, and nylon-flocked) across three surface treatments: wet (immediate recapture), dry (recapture 20–25 minutes post-inoculation), and 24-hours dry. E. coli culture results are reported in colony forming units (CFU) per milliliter (mL), while qPCR-derived gene marker concentrations are expressed as gene copies (GC) per 100 cm². Experiments were conducted in the Harwood laboratory at the University of South Florida to assess swab recapture and efficiency of recovery under varying moisture and time-delay conditions. Data may be used to evaluate microbial persistence on surfaces and inform sampling protocol development for environmental monitoring and public health surveillance.
  • Advanced Microbial Source Tracking and Fecal Source Apportionment
    This dataset contains measurements of concentrations of fecal indicator bacteria (Escherichia coli and enterococci) and microbial source tracking (MST) markers (HF183, Rum2Bac, GFD, DG37, CowM3, and GenBac), and detections of culturable E. coli that carry the sewage-associated H8 gene. This data also includes environmental parameters recorded during each sampling event, including estimations of tide and precipitation, measurements of water temperature, dissolved oxygen, pH, salinity, and turbidity. Surface water was sampled in five different water bodies (Bullfrog Creek, Frenchmans Creek, Hamilton Creek, Northshore Park, and Salt Creek) that discharge directly or indirectly to Tampa Bay in Hillsborough and Pinellas, Florida counties. Samples were collected at several sites in each water body on a monthly basis from July 2022 to June 2024 to identify major sources of fecal pollution. QPCR MST data were also used to estimate human health risk ascribed to primary contact recreational exposure by QMRA.
  • Cooperation in Nature: A nature-based intervention improves collaboration and creativity
    Data for manuscript titled "Cooperation in Nature: Nature-based intervention improves mood, creativity, collaboration, and interpersonal affiliation" Abstract: As urbanization accelerates, the cognitive effects of exposure to manmade environments and decreased time in nature become increasingly important to understand. The current study investigates the effects of real-world outdoor exposure versus simulated office-based activities on verbal creativity, mood, and collaboration. Office workers engaged in workplace collaboration either outdoors in nature or indoors in an office setting. Measures of individual affect and verbal creativity were also administered before and after a period of exposure to the assessed environments. The outdoor group showed lower self-reported negative emotions and demonstrated increased performance in one of our two verbal creativity measures. Moreover, groups exposed to nature demonstrated greater satisfaction with their collective problem-solving solutions and with the degree of influence they felt they had during the group discussions, as compared to their indoor counterparts. The current research design and results address multiple components of the Tetrahedal Model, specifically the vertices of materials, participants, contexts, and outcomes. Within the vertex of materials, this study contributes to nature-based research focused on real-world, immersive environments. This study utilizes working adult participants within the context of work-related collaboration. Finally, our findings extend Attention Restoration Theory to include other prefrontal cortex (PFC) processes, such as sensitivity to emotions, verbal creativity, and social cognition.
  • Microbial Source Tracking for Selected Waterbodies within Hillsborough County Compiled Data
    This dataset contains all microbial data collected (February 2023-January 2025) for the Microbial Source Tracking for Selected Waterbodies within Hillsborough County project. This includes measurements of concentrations of fecal indicator bacteria (Escherichia coli and enterococci) and microbial source tracking markers (HF183, GenBac, GFD, and Rum2Bac), and detections of culturable E. coli that carry the sewage-associated H8 gene. Surface water was sampled in three different water bodies (the Alafia River, Buckhorn Creek, and Rice Creek) on a monthly basis. Environmental data including precipitation is included in the datasheet.
  • Karenia brevis monthly bloom frequency maps on the West Florida Shelf
    This dataset provides monthly frequency maps of Karenia brevis blooms (red tides) on the West Florida Shelf, 1978-1986, 2003-2024. It integrates data from two satellite sensors: - CZCS-derived maps: Covering 1978-1986, based on CZCS Nimbus-7 data - MODIS-derived maps: Covering 2003-2019, based on MODIS Aqua data - VIIRS-derived maps: Covering 2012-2014, based on VIIRS SNPP data Two spatial resolutions are provided: - 1 km resolution, geographic extent: 88.0°W to 81.0°W, 23.0°N to 31.0°N - 0.1° resolution, geographic extent: 87.5°W to 81.0°W, 25.0°N to 30.5°N File Formats: - TIFF (.tif): Each file contains monthly bloom frequency values ranging from 0 to 1, indicating the proportion of days within the month when a bloom was detected at each pixel. Pixels with no valid observations are marked as NaN. - PNG (.png): Visualization-ready images using a 'jet' colormap, where values from 0 to 1 are mapped to a color gradient (blue to red) for rapid visual inspection. Each file name follows the format: YYYY_MM_monthly_frequency_SENSOR_RESOLUTION.EXT Where: YYYY_MM = year and month of observation SENSOR = MODIS or VIIRS RESOLUTION = 1km or 0.1deg EXT = file extension (.tif or .png)
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