Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13
datasets available to search
ShareScore release 0.9.0
Dataset results
13 results for “Florida panhandle”
Fig. 4 in The good side of the bad guys: predation of lepidopteran pests by Solenopsis invicta Buren (Hymenoptera: Formicidae) in the Florida Panhandle
Fig. 4. Occurrence of Solenopsis invicta in different peanut phenological stages during the 2018 crop season. West Florida Research and Education Center, Jay, Florida, USA. Means with different letters are significantly different P <0.05 (Tukey's mean separation test).
Fig. 2 in The good side of the bad guys: predation of lepidopteran pests by Solenopsis invicta Buren (Hymenoptera: Formicidae) in the Florida Panhandle
Fig. 2. Occurrence of Solenopsis invicta in hotdog-baited flip-top tubes placed on the ground and on the canopy of peanut fields during the 2017 crop season (log scale). West Florida Research and Education Center, Jay, Florida, USA. P- values from Pearson's Chi Square analysis (χ2 = 48.69; df = 5; P = 2.7 × 10−9).
Fig. 3 in The good side of the bad guys: predation of lepidopteran pests by Solenopsis invicta Buren (Hymenoptera: Formicidae) in the Florida Panhandle
Fig. 3. Comparison of mean Solenopsis invicta between cotton canopy zones during different phenological stages, 2018 crop season. West Florida Research and Education Center, Jay, Florida, USA. Means with different letters are significantly different P <0.05 (Tukey's mean separation test).
Fig. 1 in The good side of the bad guys: predation of lepidopteran pests by Solenopsis invicta Buren (Hymenoptera: Formicidae) in the Florida Panhandle
Fig. 1. Occurrence of Solenopsis invicta in hotdog-baited flip-top tubes placed on the ground and on the canopy of cotton fields during the 2017 crop season (log scale). West Florida Research and Education Center, Jay, Florida, USA. P- values from Pearson's Chi Square analysis (χ2 = 20.68; df = 6; P = 0.0021).
Western Florida Panhandle Electric Transmission Grid Substations, Lines, and Towers
<p>The upload consists of 5 different datasets pertaining to the electric transmission grid in the nine counties of the western Florida Panhandle. The area largely coincides with the former operation area of the Gulf Power Company (GPCO) but is not limited to this utility. The five datasets describe the substations, lines, and transmission towers of the grid. The data were created and validated through a variety of datasets from the utility, national data, and state-level information. In total, 195 substations, 1800 miles of transmission lines, and over 18,000 transmission towers were cataloged and described spatially in the data. The spatial files are uploaded as feature classes within a ArcGIS geodatabase. Three metadata files are provided, one each for substations, transmission lines, and transmission towers, which describe the process and sources for creating each data as well as a detailed list of all fields in the files.</p>
Output raster datasets from an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the output results for a spatial water yield model applied to an 11 county area in the state of Florida panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in this datasets associated publication. All data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p> <p>The output raster datasets contained here are water yield estimate informed with 2018 pine basal area, binary depth to water table, and average aridity index input rasters. These rasters have 10m spatial resolution, the raster extent covers 11 counties in the panhandle of Florida, the units are in centimeters of water yield per year. The water yield outputs consist of ten rasters representing the current water yield using the mean aridity raster, the water yield expected from the three pine tree thinning scenarios: 7m/hectare ba, 11 m/hectare, and 18 m/hectare, taken from the mean aridity index. Then rasters representing the water yield expected from the three thinning scenarios under maximum, and minimum aridity indexes.</p> <p> </p> <p>These ten outputs are listed here:</p> <p>"wy_current_mean" Based on 2018 BA conditions; Mean ARID</p> <p>"wy_18_mean" BA reduced to 18m2ha-1; Mean ARID</p> <p>"wy_11_mean" BA reduced to 11m2ha-1; Mean ARID</p> <p>"wy_7_mean" BA reduced to 7m2ha-1; Mean ARID</p> <p>"wy_ 18_max" BA reduced to 18m2ha-1; Maximum ARID</p> <p>"wy_ 11_max" BA reduced to 11m2ha-1; Maximum ARID</p> <p>"wy_7_max" BA reduced to 7m2ha-1; Maximum ARID</p> <p>"wy_ 18_min" BA reduced to 18m2ha-1; Minimum ARID</p> <p>"wy_ 11_min" BA reduced to 11m2ha-1; Minimum ARID</p> <p>"wy_ 7_min" BA reduced to 7m2ha-1; Minimum ARID</p> <p> </p> <p>Water yield was estimated for 2018 using the following datasets to inform the model in the Current Water Yield Calculation tool:</p> <ul> <li>Leaf area index modeled from a 2018 pine species basal area raster,</li> <li>Depth to water table data provided by Florida Geological Survey and reclassified as a binary raster,</li> <li>Average aridity index raster generated with precipitation data from PRISM Climate Group and MODIS PET data.</li> </ul> <p>For detailed information on how the above inputs were developed, please see the associated publication:</p> <p>Vernon, J., St. Peter, J., Crandall, C., Awowale, O.E., Medley, P., Drake, J., & Ibeanusi, V. (2023). Spatial application of southern pine water yield for prioritizing forest management activities. ISPRS International Journal of Geo-Information, 12(2), 34. <a href="https://doi.org/10.3390/ijgi12020034">https://doi.org/10.3390/ijgi12020034</a> </p>
Input raster datasets for an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p>
Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018
<p>Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Leon. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table: </p> <table> <tbody> <tr> <td> <p>RDCC Band</p> </td> <td> <p>lidR short name</p> </td> <td> <p>Metric Description</p> </td> </tr> <tr> <td> <p>Band 1</p> </td> <td> <p>Num_Returns</p> </td> <td> <p>Total number of returns in cell</p> </td> </tr> <tr> <td> <p>Band 2 </p> </td> <td> <p>Num_GrndRet</p> </td> <td> <p>Number of ground returns in cell</p> </td> </tr> <tr> <td> <p>Band 3</p> </td> <td> <p>Num_1stRet</p> </td> <td> <p>Number of first returns in cell</p> </td> </tr> <tr> <td> <p>Band 4</p> </td> <td> <p>Grnd_Elev</p> </td> <td> <p>Ground Elevations (above geoid, etc)</p> </td> </tr> <tr> <td> <p>Band 5</p> </td> <td> <p>Mn_RH</p> </td> <td> <p>Mean of all Relative Heights</p> </td> </tr> <tr> <td> <p>Band 6</p> </td> <td> <p>SD_RH</p> </td> <td> <p>Std. Dev of all Relative heights</p> </td> </tr> <tr> <td> <p>Band 7</p> </td> <td> <p>RHt_95th</p> </td> <td> <p>Relative Height 95%</p> </td> </tr> <tr> <td> <p>Band 8</p> </td> <td> <p>RHt_90th</p> </td> <td> <p>Relative Height 90%</p> </td> </tr> <tr> <td> <p>Band 9</p> </td> <td> <p>RHt_75th</p> </td> <td> <p>Relative Height 75%</p> </td> </tr> <tr> <td> <p>Band 10</p> </td> <td> <p>RHt_50th</p> </td> <td> <p>Relative Height 50%</p> </td> </tr> <tr> <td> <p>Band 11</p> </td> <td> <p>RHt_25th</p> </td> <td> <p>Relative Height 25%</p> </td> </tr> <tr> <td> <p>Band 12</p> </td> <td> <p>RHt_10th</p> </td> <td> <p>Relative Height 10%</p> </td> </tr> <tr> <td> <p>Band 13</p> </td> <td> <p>RHt_05th</p> </td> <td> <p>Relative Height 5%</p> </td> </tr> <tr> <td> <p>Band 14</p> </td> <td> <p>RD_2to10ft</p> </td> <td> <p>Relative Density 2 to 10 ft (Shrubs)</p> </td> </tr> <tr> <td> <p>Band 15</p> </td> <td> <p>RD_10to20ft</p> </td> <td> <p>Relative Density 10 to 20 ft (High shrub/low midstory)</p> </td> </tr> <tr> <td> <p>Band 16</p> </td> <td> <p>RD_20to49ft</p> </td> <td> <p>Relative Density 20 to 49 ft (High midstory)</p> </td> </tr> <tr> <td> <p>Band 17</p> </td> <td> <p>RD_gt2ft</p> </td> <td> <p>Relative Density all returns gt 2 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 18</p> </td> <td> <p>RD_gt10ft</p> </td> <td> <p>Relative Density all returns gt 10 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 19</p> </td> <td> <p>RD_gt20ft</p> </td> <td> <p>Relative Density all returns gt 20 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 20</p> </td> <td> <p>RD_gt49ft</p> </td> <td> <p>Relative Density greater than 49 ft (Canopy)</p> </td> </tr> <tr> <td> <p>Band 21</p> </td> <td> <p>CC_gt2ft</p> </td> <td> <p>Canopy Cover gt 2 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 22</p> </td> <td> <p>CC_gt10ft</p> </td> <td> <p>Canopy Cover gt 10 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 23</p> </td> <td> <p>CC_gt20ft</p> </td> <td> <p>Canopy Cover gt 20 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 24</p> </td> <td> <p>CC_gt49ft</p> </td> <td> <p>Canopy Cover gt 49 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 25</p> </td> <td> <p>MnRHgt2ft</p> </td> <td> <p>Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 26</p> </td> <td> <p>MnRHgt10ft</p> </td> <td> <p>Mean of all relative heights gt 10 ft (includes all midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 27</p> </td> <td> <p>MnRHgt20ft</p> </td> <td> <p>Mean of all relative heights gt 20 ft (includes high midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 28</p> </td> <td> <p>MnRHgt49ft</p> </td> <td> <p>Mean of all relative heights gt 49 ft (includes upper canopy)</p> </td> </tr> </tbody> </table> <p>Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763 </p>
Relative Density Canopy Cover Outputs for Choctawhatchee Lidar data in the Florida Panhandle 2018
<p>Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Choctawhatchee. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table: </p> <table> <tbody> <tr> <td> <p>RDCC Band</p> </td> <td> <p>lidR short name</p> </td> <td> <p>Metric Description</p> </td> </tr> <tr> <td> <p>Band 1</p> </td> <td> <p>Num_Returns</p> </td> <td> <p>Total number of returns in cell</p> </td> </tr> <tr> <td> <p>Band 2 </p> </td> <td> <p>Num_GrndRet</p> </td> <td> <p>Number of ground returns in cell</p> </td> </tr> <tr> <td> <p>Band 3</p> </td> <td> <p>Num_1stRet</p> </td> <td> <p>Number of first returns in cell</p> </td> </tr> <tr> <td> <p>Band 4</p> </td> <td> <p>Grnd_Elev</p> </td> <td> <p>Ground Elevations (above geoid, etc)</p> </td> </tr> <tr> <td> <p>Band 5</p> </td> <td> <p>Mn_RH</p> </td> <td> <p>Mean of all Relative Heights</p> </td> </tr> <tr> <td> <p>Band 6</p> </td> <td> <p>SD_RH</p> </td> <td> <p>Std. Dev of all Relative heights</p> </td> </tr> <tr> <td> <p>Band 7</p> </td> <td> <p>RHt_95th</p> </td> <td> <p>Relative Height 95%</p> </td> </tr> <tr> <td> <p>Band 8</p> </td> <td> <p>RHt_90th</p> </td> <td> <p>Relative Height 90%</p> </td> </tr> <tr> <td> <p>Band 9</p> </td> <td> <p>RHt_75th</p> </td> <td> <p>Relative Height 75%</p> </td> </tr> <tr> <td> <p>Band 10</p> </td> <td> <p>RHt_50th</p> </td> <td> <p>Relative Height 50%</p> </td> </tr> <tr> <td> <p>Band 11</p> </td> <td> <p>RHt_25th</p> </td> <td> <p>Relative Height 25%</p> </td> </tr> <tr> <td> <p>Band 12</p> </td> <td> <p>RHt_10th</p> </td> <td> <p>Relative Height 10%</p> </td> </tr> <tr> <td> <p>Band 13</p> </td> <td> <p>RHt_05th</p> </td> <td> <p>Relative Height 5%</p> </td> </tr> <tr> <td> <p>Band 14</p> </td> <td> <p>RD_2to10ft</p> </td> <td> <p>Relative Density 2 to 10 ft (Shrubs)</p> </td> </tr> <tr> <td> <p>Band 15</p> </td> <td> <p>RD_10to20ft</p> </td> <td> <p>Relative Density 10 to 20 ft (High shrub/low midstory)</p> </td> </tr> <tr> <td> <p>Band 16</p> </td> <td> <p>RD_20to49ft</p> </td> <td> <p>Relative Density 20 to 49 ft (High midstory)</p> </td> </tr> <tr> <td> <p>Band 17</p> </td> <td> <p>RD_gt2ft</p> </td> <td> <p>Relative Density all returns gt 2 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 18</p> </td> <td> <p>RD_gt10ft</p> </td> <td> <p>Relative Density all returns gt 10 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 19</p> </td> <td> <p>RD_gt20ft</p> </td> <td> <p>Relative Density all returns gt 20 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 20</p> </td> <td> <p>RD_gt49ft</p> </td> <td> <p>Relative Density greater than 49 ft (Canopy)</p> </td> </tr> <tr> <td> <p>Band 21</p> </td> <td> <p>CC_gt2ft</p> </td> <td> <p>Canopy Cover gt 2 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 22</p> </td> <td> <p>CC_gt10ft</p> </td> <td> <p>Canopy Cover gt 10 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 23</p> </td> <td> <p>CC_gt20ft</p> </td> <td> <p>Canopy Cover gt 20 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 24</p> </td> <td> <p>CC_gt49ft</p> </td> <td> <p>Canopy Cover gt 49 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 25</p> </td> <td> <p>MnRHgt2ft</p> </td> <td> <p>Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 26</p> </td> <td> <p>MnRHgt10ft</p> </td> <td> <p>Mean of all relative heights gt 10 ft (includes all midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 27</p> </td> <td> <p>MnRHgt20ft</p> </td> <td> <p>Mean of all relative heights gt 20 ft (includes high midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 28</p> </td> <td> <p>MnRHgt49ft</p> </td> <td> <p>Mean of all relative heights gt 49 ft (includes upper canopy)</p> </td> </tr> </tbody> </table> <p>Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763 </p>
Relative Density Canopy Cover Outputs for Block3 Lidar data in the Florida Panhandle 2018
<p>Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Block 3. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table: </p> <table> <tbody> <tr> <td> <p>RDCC Band</p> </td> <td> <p>lidR short name</p> </td> <td> <p>Metric Description</p> </td> </tr> <tr> <td> <p>Band 1</p> </td> <td> <p>Num_Returns</p> </td> <td> <p>Total number of returns in cell</p> </td> </tr> <tr> <td> <p>Band 2 </p> </td> <td> <p>Num_GrndRet</p> </td> <td> <p>Number of ground returns in cell</p> </td> </tr> <tr> <td> <p>Band 3</p> </td> <td> <p>Num_1stRet</p> </td> <td> <p>Number of first returns in cell</p> </td> </tr> <tr> <td> <p>Band 4</p> </td> <td> <p>Grnd_Elev</p> </td> <td> <p>Ground Elevations (above geoid, etc)</p> </td> </tr> <tr> <td> <p>Band 5</p> </td> <td> <p>Mn_RH</p> </td> <td> <p>Mean of all Relative Heights</p> </td> </tr> <tr> <td> <p>Band 6</p> </td> <td> <p>SD_RH</p> </td> <td> <p>Std. Dev of all Relative heights</p> </td> </tr> <tr> <td> <p>Band 7</p> </td> <td> <p>RHt_95th</p> </td> <td> <p>Relative Height 95%</p> </td> </tr> <tr> <td> <p>Band 8</p> </td> <td> <p>RHt_90th</p> </td> <td> <p>Relative Height 90%</p> </td> </tr> <tr> <td> <p>Band 9</p> </td> <td> <p>RHt_75th</p> </td> <td> <p>Relative Height 75%</p> </td> </tr> <tr> <td> <p>Band 10</p> </td> <td> <p>RHt_50th</p> </td> <td> <p>Relative Height 50%</p> </td> </tr> <tr> <td> <p>Band 11</p> </td> <td> <p>RHt_25th</p> </td> <td> <p>Relative Height 25%</p> </td> </tr> <tr> <td> <p>Band 12</p> </td> <td> <p>RHt_10th</p> </td> <td> <p>Relative Height 10%</p> </td> </tr> <tr> <td> <p>Band 13</p> </td> <td> <p>RHt_05th</p> </td> <td> <p>Relative Height 5%</p> </td> </tr> <tr> <td> <p>Band 14</p> </td> <td> <p>RD_2to10ft</p> </td> <td> <p>Relative Density 2 to 10 ft (Shrubs)</p> </td> </tr> <tr> <td> <p>Band 15</p> </td> <td> <p>RD_10to20ft</p> </td> <td> <p>Relative Density 10 to 20 ft (High shrub/low midstory)</p> </td> </tr> <tr> <td> <p>Band 16</p> </td> <td> <p>RD_20to49ft</p> </td> <td> <p>Relative Density 20 to 49 ft (High midstory)</p> </td> </tr> <tr> <td> <p>Band 17</p> </td> <td> <p>RD_gt2ft</p> </td> <td> <p>Relative Density all returns gt 2 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 18</p> </td> <td> <p>RD_gt10ft</p> </td> <td> <p>Relative Density all returns gt 10 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 19</p> </td> <td> <p>RD_gt20ft</p> </td> <td> <p>Relative Density all returns gt 20 ft (for comp w/ CC)</p> </td> </tr> <tr> <td> <p>Band 20</p> </td> <td> <p>RD_gt49ft</p> </td> <td> <p>Relative Density greater than 49 ft (Canopy)</p> </td> </tr> <tr> <td> <p>Band 21</p> </td> <td> <p>CC_gt2ft</p> </td> <td> <p>Canopy Cover gt 2 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 22</p> </td> <td> <p>CC_gt10ft</p> </td> <td> <p>Canopy Cover gt 10 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 23</p> </td> <td> <p>CC_gt20ft</p> </td> <td> <p>Canopy Cover gt 20 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 24</p> </td> <td> <p>CC_gt49ft</p> </td> <td> <p>Canopy Cover gt 49 ft (based only on first returns)</p> </td> </tr> <tr> <td> <p>Band 25</p> </td> <td> <p>MnRHgt2ft</p> </td> <td> <p>Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 26</p> </td> <td> <p>MnRHgt10ft</p> </td> <td> <p>Mean of all relative heights gt 10 ft (includes all midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 27</p> </td> <td> <p>MnRHgt20ft</p> </td> <td> <p>Mean of all relative heights gt 20 ft (includes high midstory and upper canopy)</p> </td> </tr> <tr> <td> <p>Band 28</p> </td> <td> <p>MnRHgt49ft</p> </td> <td> <p>Mean of all relative heights gt 49 ft (includes upper canopy)</p> </td> </tr> </tbody> </table> <p>Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763 </p>
Relative Density Canopy Cover Outputs for Block2 Lidar data in the Florida Panhandle 2018
<p>Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Block 2. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table: </p> <table> <tbody> <tr> <td>RDCC Band</td> <td>lidR short name</td> <td>Metric Description</td> </tr> <tr> <td>Band 1</td> <td>Num_Returns</td> <td>Total number of returns in cell</td> </tr> <tr> <td>Band 2 </td> <td>Num_GrndRet</td> <td>Number of ground returns in cell</td> </tr> <tr> <td>Band 3</td> <td>Num_1stRet</td> <td>Number of first returns in cell</td> </tr> <tr> <td>Band 4</td> <td>Grnd_Elev</td> <td>Ground Elevations (above geoid, etc)</td> </tr> <tr> <td>Band 5</td> <td>Mn_RH</td> <td>Mean of all Relative Heights</td> </tr> <tr> <td>Band 6</td> <td>SD_RH</td> <td>Std. Dev of all Relative heights</td> </tr> <tr> <td>Band 7</td> <td>RHt_95th</td> <td>Relative Height 95%</td> </tr> <tr> <td>Band 8</td> <td>RHt_90th</td> <td>Relative Height 90%</td> </tr> <tr> <td>Band 9</td> <td>RHt_75th</td> <td>Relative Height 75%</td> </tr> <tr> <td>Band 10</td> <td>RHt_50th</td> <td>Relative Height 50%</td> </tr> <tr> <td>Band 11</td> <td>RHt_25th</td> <td>Relative Height 25%</td> </tr> <tr> <td>Band 12</td> <td>RHt_10th</td> <td>Relative Height 10%</td> </tr> <tr> <td>Band 13</td> <td>RHt_05th</td> <td>Relative Height 5%</td> </tr> <tr> <td>Band 14</td> <td>RD_2to10ft</td> <td>Relative Density 2 to 10 ft (Shrubs)</td> </tr> <tr> <td>Band 15</td> <td>RD_10to20ft</td> <td>Relative Density 10 to 20 ft (High shrub/low midstory)</td> </tr> <tr> <td>Band 16</td> <td>RD_20to49ft</td> <td>Relative Density 20 to 49 ft (High midstory)</td> </tr> <tr> <td>Band 17</td> <td>RD_gt2ft</td> <td>Relative Density all returns gt 2 ft (for comp w/ CC)</td> </tr> <tr> <td>Band 18</td> <td>RD_gt10ft</td> <td>Relative Density all returns gt 10 ft (for comp w/ CC)</td> </tr> <tr> <td>Band 19</td> <td>RD_gt20ft</td> <td>Relative Density all returns gt 20 ft (for comp w/ CC)</td> </tr> <tr> <td>Band 20</td> <td>RD_gt49ft</td> <td>Relative Density greater than 49 ft (Canopy)</td> </tr> <tr> <td>Band 21</td> <td>CC_gt2ft</td> <td>Canopy Cover gt 2 ft (based only on first returns)</td> </tr> <tr> <td>Band 22</td> <td>CC_gt10ft</td> <td>Canopy Cover gt 10 ft (based only on first returns)</td> </tr> <tr> <td>Band 23</td> <td>CC_gt20ft</td> <td>Canopy Cover gt 20 ft (based only on first returns)</td> </tr> <tr> <td>Band 24</td> <td>CC_gt49ft</td> <td>Canopy Cover gt 49 ft (based only on first returns)</td> </tr> <tr> <td>Band 25</td> <td>MnRHgt2ft</td> <td>Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy)</td> </tr> <tr> <td>Band 26</td> <td>MnRHgt10ft</td> <td>Mean of all relative heights gt 10 ft (includes all midstory and upper canopy)</td> </tr> <tr> <td>Band 27</td> <td>MnRHgt20ft</td> <td>Mean of all relative heights gt 20 ft (includes high midstory and upper canopy)</td> </tr> <tr> <td>Band 28</td> <td>MnRHgt49ft</td> <td>Mean of all relative heights gt 49 ft (includes upper canopy)</td> </tr> </tbody> </table> <p>Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763 </p>
The Cancer Connected Access and Remote Expertise Beyond Walls Program to Provide In-Home Cancer Treatment and Improve Treatment Satisfaction in Cancer Patients Living in the Florida Panhandle and Surr
ClinicalTrials.gov study NCT07285044. IPD Sharing: NO. Countries: 1. Publications: 0.
Optical Water quality measurements made in the Florida Panhandle estuaries
Measurements made in the Florida Panhandle estuaries in partnership with USF and FWC-FWRI.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.