University of South Florida Showcase

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.

This repository includes the data utilized in the paper “High -Resolution Magnetic Survey Using an Unoccupied Aerial Vehicle to Constrain Buried Lava Flow Geometry, Volume, and Eruptive History of Little Cones, Crater Flat, Nevada” by Van Alphen and colleagues. DOI: https://doi.org/10.3133/pp1890R. This paper contributes to a special volume by the USGS. Data were collected from October 17 to October 22, 2019, in the Craters Flat area of Nevada, United States. A Unmanned Aerial Vehicle (UAV) and the Sensys R3 MagDrone instrument were employed for data collection purposes. The procedures pertaining to data collection and processing are delineated comprehensively in the paper. The data collected on October 22 primarily pertain to the ‘fencing’ methodology. The folder, “Raw R3 Magdrone Data,” contains unaltered Sensys MagDrone format data captured during flight. No modifications were made to the preserved files. Users must have Sensys software to access this data, which cannot be shared due to licensing restrictions. The dataset is organized into folders by acquisition date, with each file corresponding to a single flight. Filenames include the data collection date and time, the magnetometer model (R3), and its serial number. The folder, “Raw R3 data in ASCII csv format," contains unaltered raw data from the Sensys R3 MagDrone in ASCII csv format, identical to the files in the instrument. It is organized similarly by acquisition dates, with filenames detailing the start date, time, magnetometer model (R3), and serial number. The data format is in the file headers. Magnetic data were collected at 200Hz, and GPS data at 5Hz. The folder “Filtered, reduced and split clean lines” contains magnetic data cleaned of outliers and divided into straight flight lines. Files are organized by day, with each line extracted based on the anticipated magnetic field direction at Craters Flat, Nevada, as described in the referenced paper. The data was filtered at 1Hz using Sensys Magdrone software, with Easting and Northing coordinates for UTM zone 11N included. Each column is described in the file header. Filenames include the flight start date, time, direction, and coordinates. Only lines in the North-South, South-North, East-West, and West-East orientations are kept; lines under 100 meters and ferry lines are excluded. The “Leveled dataset in a uniform spacing grid” contains the final dataset for the inversion process. Processing procedures are detailed in the associated paper. Data is organized on a 10-meter regular spacing grid using GMT’s surface routine with minimum curvature. Additionally, data is formatted in GMT NetCDF. The "Photogrammetry Photos and ancillary data" folder contains all the photos taken during the photogrammetry survey. The photos were taken every second but only every 5th photo was used in the DEM creation. There is also the resultant DEM from the photogrammetry model and the ground control points used to georeference the model.

Raw and processed physiological data for figures. Supplemental Information on response characteristics of ventricular and subarachnoid recording electrodes.

Raw, processed, and fitted IOP data of manuscript figures.

This dataset is the spatial distribution maps of brine shrimp cyst detected from Landsat images in Great Salt Lake (1984-2024).

This replication package reproduces all tables and figures in "Old But Gold: Historical Pathways and Path Dependence." It contains the analysis-ready datasets (municipality, mining census area, and census wage microdata panels, together with auxiliary historical series on population, GDP, crops, railroads, and roads), the digitized shapefiles for colonial gold roads, least-cost-path instruments, administrative boundaries, and highways, and the R and Julia scripts that build the tables, regression output, and maps from these inputs. Running MakeFiles.R executes the full pipeline and writes results to tables/ and figures/.