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PlanetProfile Python version outputs compared to models of Vance et al. (2018)
<p>Plain text outputs, associated input files, and comparison figures for hydrosphere properties for PlanetProfile models analogous to those studied by Vance et al. (2018), for Europa, Ganymede, Callisto, Enceladus, and Titan. PlanetProfile is an open-source geophysical modeling framework available at https://github.com/NASA-Planetary-Science/PlanetProfile. The inputs and outputs are from and created by the v2.3.17 release, with default settings. Output files list bulk properties in header lines at the top, then physical properties of each spherically symmetric layer along with depth and radius, beginning at the surface and continuing to the center of the body.</p> <p>Vance et al. (2018) is available at: <a href="https://doi.org/10.1002/2017JE005341">https://doi.org/10.1002/2017JE005341</a></p>
PlanetProfile default model outputs
<p>Summary graphics, plain text outputs, and associated input files for all PlanetProfile default models: 16 moons across the outer solar system and Pluto. PlanetProfile is an open-source geophysical modeling framework available at https://github.com/NASA-Planetary-Science/PlanetProfile. The inputs and outputs are from and created by the v2.3.17 release, with all default settings. Output files list bulk properties in header lines at the top, then physical properties of each spherically symmetric layer along with depth and radius, beginning at the surface and continuing to the center of the body. Summary graphics are grouped both by size and by solar system location, such that each model appears twice.</p>
mitgcm_code_input_and_output_for_amundsen_runoff_study
<p>This repository contains all inputs and code to carry out subglacial runoff-forced experiments of the Amundsen Sea Embayment using the modelling framework MITgcm.</p> <p>expt_code/ contains specialised code files for the experiment</p> <p>modified_code/ contains main code trunk files that were modified from checkpoint 67v</p> <p>input/ contains experiment-specific binary and parameter files</p> <p>outputs/ contains .mat files which contain all relevant output for analysis</p>
Data from: The costs of living on the coast: reduction in body size and size-specific reproductive output in coastal populations of a widespread amphibian
<ol> <li>Body size is a critical component of organismal biology. Body size is known to be influenced by a plethora of environmental conditions, among which exposure to large-scale variations of salinity has been comparatively overlooked. Yet, exposure to salinity is known to affect energetic allocation toward growth and reproduction.</li> <li>In this study, we investigated the morphological differences between inland and coastal individuals of spined toads (<em>Bufo</em> <em>spinosus</em>) in Western France. </li> <li>We measured adult morphology both outside and during the reproductive season on 190 individuals, and assessed reproduction in pairs originating from inland (N=20) and coastal (N=30) environments.</li> <li>Overall, we found that adult coastal toads were smaller and lighter than inland individuals. Reproductive correlates of these differences included lower fecundity and smaller egg size (but higher egg density) in coastal females. Interestingly, these differences were not allometric correlates of body size, as coastal females invested proportionally less in all components of reproduction (fecundity, egg size and egg protection).</li> <li>These results suggest altered resource allocation to growth and reproduction in coastal amphibians, which may be related to the marked spatial gradient of salinity (measured in reproductive ponds) and the associated costs of osmoregulation (higher osmolality in coastal individuals), for which local adaptation and higher tolerance to salinity remains to be tested.</li> </ol>
WRF_NOx_output
<p>WRF model outputs</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>
Reproductive output and telomere dynamics following calcium supplementation in wild Tree Swallows (Tachycineta bicolor)
<p>This data was the result of an experiment investigating whether biologically relevant differences in reproductive investment influence fitness in a wild bird species. We employed calcium supplementation to experimentally modify reproductive output in Tree Swallow mothers and offspring and we used telomere dynamics during the breeding season as a proxy for fitness. <span>To confirm that calcium supplementation positively impacts reproductive parameters at our study site, we initiated a 4-year trial of calcium supplementation with our Tree Swallow study population. In the following two breeding seasons, we used calcium supplementation to experimentally alter reproductive output and measured telomere length in mothers immediately before egg-laying and shortly before fledging to determine the direct relationship between reproductive investment and telomere shortening in this species. </span>Our study includes one hundred eighty-eight nests initiated and monitored over six breeding seasons. </p>
PyWGCNA's output example
<p>5xFAD pickle file is an output of running the <a href="https://github.com/mortazavilab/PyWGCNA">PyWGCNA package</a> on 5xFAD mice with matching wildtype in four different time points (4,8,12 and 18 months) on the cortex and hippocampus.</p> <p>3xTgAD pickle file is an output of running the <a href="https://github.com/mortazavilab/PyWGCNA">PyWGCNA package</a> on 3xTgAD mice with matching wildtype in three different time points (4, 12 and 18 months) on the hippocampus.</p>
Model Output of GPT-3.5 and GPT-4 for ECHR-AM
<p> <strong>"gpt3.5-gpt4-input-output-echram.zip"</strong> :</p> <blockquote> <p>Input and output to GPT-3.5 and GPT-4 based on <a href="http://www.di.uevora.pt/~pq/echr/">ECHR dataset</a> published in JSON format in this <a href="https://aclanthology.org/2020.argmining-1.8.pdf">paper</a> for argument component classification only i.e. clauses that are argumentative (conclusion/premise), extracted from the JSON file</p> </blockquote> <p>Note: Output of the model is under <a href="https://openai.com/policies">OpenAI Terms & policies</a>.</p> <p> </p> <p>Please cite our paper also if you use this dataset: <a href="https://www.frontiersin.org/articles/10.3389/frai.2023.1278796">Performance analysis of large language models in the domain of legal argument mining</a></p> <p><a href="https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1278796/bibtex">You can click here for BibTex</a> or copy the text below. </p> <blockquote> <p>@ARTICLE{10.3389/frai.2023.1278796,</p> <p>AUTHOR={Al Zubaer, Abdullah and Granitzer, Michael and Mitrović, Jelena },</p> <p>TITLE={Performance analysis of large language models in the domain of legal argument mining},</p> <p>JOURNAL={Frontiers in Artificial Intelligence},</p> <p>VOLUME={6},</p> <p>YEAR={2023},</p> <p>URL={https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1278796},</p> <p>DOI={10.3389/frai.2023.1278796},</p> <p>ISSN={2624-8212},</p> <p>ABSTRACT={<p>Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.</p>}}</p> </blockquote> <p> </p> <p> </p> <p> </p> <p> </p>
CMAQ-CUDA simulation outputs
<p>Output for conventional CMAQ and CMAQ-CUDAv2.0.</p>
CSPG GridPath Model Input and Output Data
<p>This data repository holds input and output data for the paper Jin XY., Chowdhury, A.K., Cheng CT., and Galelli, S. “The unintended consequences of decarbonizing the China Southern Power Grid”. See Readme for more details. </p>
enpkg_graph_builder_output
<p>Output of https://github.com/enpkg/enpkg_graph_builder for https://zenodo.org/record/8252033</p>
Standard Error Estimates for ARRI Ensemble LM model outputs
<p>Summary</p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p>Description</p> <p>The LM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble linear regression models (LM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble LM model was created using a custom R script that was based off the work detailed in Hogland, 2021. The ensemble LM script was modified to use the lm() function in place of the GAM modelling functions. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble LM models for each cell were averaged (mean) to produce the LM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p>The 246 Restore field plots used to train the LM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p>References:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. <a href="https://doi.org/10.5281/zenodo.8222114">https://doi.org/10.5281/zenodo.8222114</a></p> <p>Credits</p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p>Use Limitations</p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
Scripts, inputs and outputs of Module 4 ANOVA test regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 4_Calculation of ANOVA test (the input, formulation, macros, and scripts of ANOVA test per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
Scripts, inputs and outputs of Module 5 USI travellers, TSPs and Effectiveness regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 5_Results and outputs of USI Travellers, USI TSPs, Effectiveness (Travellers & TSPs) and the average of Effectiveness of all functionalities individually across all 6 demo sites (inputs, calculation, formulation, scripts of USI travellers, TSPs and Effectiveness per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
Scripts, inputs and outputs of Module 1 AHP analysis regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 1_AHP and Pairwise comparison matrix calculation_IP4MaaS II (formulations and calculations of pairwise comparison matrix per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
Scripts, inputs and outputs of Module 2 Regression analysis regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 2_Regression analysis (inputs, scripts, and formulations of regression analysis per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
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.