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Publication Year: 2020 Author Affiliation: Texas A and M University

41 to 50 of 95 Results

NEON-CLBJ Distributed Vegetation Plot Data May 13, 2020 - Lyndon B. Johnson National Grasslands Evapotranspiration Partitioning Study Adkison, Christopher, 2020, "NEON-CLBJ Distributed Vegetation Plot Data", https://doi.org/10.18738/T8/EIZX7Y, Texas Data Repository, V1 This dataset contains spreadsheets housing data for individual vegetation plots at Lyndon B. Johnson National Grasslands that were collected by NEON staff in November of 2018. It also contains descriptive data on the eleven oak trees with sap flow sensors, and the process for cal...
Monthly Vapor Pressure Deficit Calculations May 13, 2020 - Lyndon B. Johnson National Grasslands Evapotranspiration Partitioning Study Adkison, Christopher, 2020, "Monthly Vapor Pressure Deficit Calculations", https://doi.org/10.18738/T8/HLFMSY, Texas Data Repository, V1 These spreadsheets contain data for 30-min vapor pressure deficit calculations at Lyndon B. Johnson National Grasslands in Decatur, TX, part of the National Ecological Observatory Network from May 2019-February 2020.
Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m Jun 9, 2020 - Roughness Scenarios FLUD Research Group, 2020, "Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m", https://doi.org/10.18738/T8/CES19C, Texas Data Repository, V1 Shapefiles of Computational Meshes for HEC-RAS Models for that have an average point spacing of 7.5m, 10.0m, 12.5m, and 15.0m (original). Created with the export feature of RAS-Mapper for HEC-RAS v 5.0.7 (Brunner, 2016) Brunner, G.W. (2016). HEC-RAS: River Analysis System, 2D Mod...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0", https://doi.org/10.18738/T8/ILRKFQ, Texas Data Repository, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/F2U3QV, Texas Data Repository, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GMU4XY, Texas Data Repository, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0 Jun 8, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/0PQLIB, Texas Data Repository, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes all environmental variables (veget...
Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0", https://doi.org/10.18738/T8/GQYIX2, Texas Data Repository, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes all environmental variables (veget...
Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 1.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/8UXWKH, Texas Data Repository, V1 This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes all environmental variables (veget...
Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0 Jun 9, 2020 - Environmental Analyses FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0", https://doi.org/10.18738/T8/WHS3BR, Texas Data Repository, V1 This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...

NEON-CLBJ Distributed Vegetation Plot Data

May 13, 2020 - Lyndon B. Johnson National Grasslands Evapotranspiration Partitioning Study

Adkison, Christopher, 2020, "NEON-CLBJ Distributed Vegetation Plot Data", https://doi.org/10.18738/T8/EIZX7Y, Texas Data Repository, V1

This dataset contains spreadsheets housing data for individual vegetation plots at Lyndon B. Johnson National Grasslands that were collected by NEON staff in November of 2018. It also contains descriptive data on the eleven oak trees with sap flow sensors, and the process for cal...

Monthly Vapor Pressure Deficit Calculations

May 13, 2020 - Lyndon B. Johnson National Grasslands Evapotranspiration Partitioning Study

Adkison, Christopher, 2020, "Monthly Vapor Pressure Deficit Calculations", https://doi.org/10.18738/T8/HLFMSY, Texas Data Repository, V1

These spreadsheets contain data for 30-min vapor pressure deficit calculations at Lyndon B. Johnson National Grasslands in Decatur, TX, part of the National Ecological Observatory Network from May 2019-February 2020.

Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m

Jun 9, 2020 - Roughness Scenarios

FLUD Research Group, 2020, "Mesh Resolution of HEC-RAS Models: Average Point Spacing of 7.5m-15.0m", https://doi.org/10.18738/T8/CES19C, Texas Data Repository, V1

Shapefiles of Computational Meshes for HEC-RAS Models for that have an average point spacing of 7.5m, 10.0m, 12.5m, and 15.0m (original). Created with the export feature of RAS-Mapper for HEC-RAS v 5.0.7 (Brunner, 2016) Brunner, G.W. (2016). HEC-RAS: River Analysis System, 2D Mod...

Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0", https://doi.org/10.18738/T8/ILRKFQ, Texas Data Repository, V1

This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables...

Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/F2U3QV, Texas Data Repository, V1

Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GMU4XY, Texas Data Repository, V1

Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0

Jun 8, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/0PQLIB, Texas Data Repository, V1

Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0", https://doi.org/10.18738/T8/GQYIX2, Texas Data Repository, V1

Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 1.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/8UXWKH, Texas Data Repository, V1

Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0

Jun 9, 2020 - Environmental Analyses

FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0", https://doi.org/10.18738/T8/WHS3BR, Texas Data Repository, V1

This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...

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