AndreaBozzo/ceres-open-data-index huggingface dataset:
Coast Horned Lizard Habitat Model for NSNF Connectivity - CDFW [ds1035]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1035_20141006_wm, cn coast horned lizard, connectivity modeling, habitat modeling, reptile, sn Phrynosoma coronatum
Organization: California Department of Fish and Wildlife
California Ground Squirrel Habitat Model for NSNF Connectivity - CDFW [ds1034]

This grid represents habitat suitability based on the California Wildlife Habitat Relationships (CWHR) expert opinion habitat suitability rankings by habitat type. Habitat suitability rankings (ranging 0-100) for each combination of habitat type, size class (dbh) and cover class (density) were applied to the 30 m vegetation grid using CWHRs Bioview. These values were then averaged across grid cells to create a 270 m grid consistent with that used in the Maxent models for the project. The grid was then symbolized to represent low (0-50), medium (50-75) and high (75-100) habitat suitability. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1034_20141006_wm, cn California ground squirrel, connectivity modeling, habitat modeling, mammal, sn Otospermophilus beesheyi
Organization: California Department of Fish and Wildlife
California Thrasher Habitat Model for NSNF Connectivity - CDFW [ds1033]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1033_20141006_wm, bird, cn California thrasher, connectivity modeling, habitat modeling, sn Toxostoma redivivum
Organization: California Department of Fish and Wildlife
California Quail Habitat Model for NSNF Connectivity - CDFW [ds1032]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1032_20141006_wm, bird, cn California quail, connectivity modeling, habitat modeling, sn Callipepla californica
Organization: California Department of Fish and Wildlife
Gray Fox Habitat Model for NSNF Connectivity - CDFW [ds1041]

This grid represents habitat suitability based on the California Wildlife Habitat Relationships (CWHR) expert opinion habitat suitability rankings by habitat type. Habitat suitability rankings (ranging 0-100) for each combination of habitat type, size class (dbh) and cover class (density) were applied to the 30 m vegetation grid using CWHRs Bioview. These values were then averaged across grid cells to create a 270 m grid consistent with that used in the Maxent models for the project. The grid was then symbolized to represent low (0-50), medium (50-75) and high (75-100) habitat suitability. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1041_20141006_wm, cn Gray Fox, connectivity modeling, habitat modeling, mammal, sn Urocyon cinereoargenteus
Organization: California Department of Fish and Wildlife
Black-tailed Jackrabbit Habitat Model for NSNF Connectivity - CDFW [ds1031]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1031_20141006_wm, cn black-tailed jackrabbit, connectivity modeling, habitat modeling, mammal, sn Lepus californicus
Organization: California Department of Fish and Wildlife
Bobcat Habitat Suitability Model for NSNF Connectivity - CDFW [ds1027]

This grid represents habitat suitability based on the California Wildlife Habitat Relationships (CWHR) expert opinion habitat suitability rankings by habitat type. Habitat suitability rankings (ranging 0-100) for each combination of habitat type, size class (dbh) and cover class (density) were applied to the 30 m vegetation grid using CWHRs Bioview. These values were then averaged across grid cells to create a 270 m grid consistent with that used in the Maxent models for the project. The grid was then symbolized to represent low (0-50), medium (50-75) and high (75-100) habitat suitability. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1027_20140903_wm, cn bobcat, connectivity modeling, habitat modeling, northern Sierra Nevada foohills, sn Lynx rufus
Organization: California Department of Fish and Wildlife
Arboreal Salamander Habitat Model for NSNF Connectivity - CDFW [ds1024]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1024_20140821_wm, cn arboreal salmander, connectivity modeling, habitat modeling, northern Sierra Nevada foohills, sn Aneides lugubris
Organization: California Department of Fish and Wildlife
Acorn Woodpecker Habitat Model for NSNF Connectivity - CDFW [ds1023]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1023_20140820_wm, cn acorn woodpecker, connectivity modeling, habitat modeling, northern Sierra Nevada foohills, sn Melanerpes formicivorus
Organization: California Department of Fish and Wildlife
Gopher Snake Habitat Model for NSNF Connectivity - CDFW [ds1040]

This grid represents habitat suitability based on the California Wildlife Habitat Relationships (CWHR) expert opinion habitat suitability rankings by habitat type. Habitat suitability rankings (ranging 0-100) for each combination of habitat type, size class (dbh) and cover class (density) were applied to the 30 m vegetation grid using CWHRs Bioview. These values were then averaged across grid cells to create a 270 m grid consistent with that used in the Maxent models for the project. The grid was then symbolized to represent low (0-50), medium (50-75) and high (75-100) habitat suitability. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1040_20141006_wm, cn Gopher Snake, connectivity modeling, habitat modeling, reptile, sn Pituophis catenifer
Organization: California Department of Fish and Wildlife
Black Bear Habitat Model for NSNF Connectivity - CDFW [ds1008]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman &amp; Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Tags: Auth_CDFW, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS1008_20140617_wm, cn black bear, connectivity modeling, habitat modeling, northern Sierra Nevada foohills, sn Ursus americanus
Organization: California Department of Fish and Wildlife
Utility Natural Gas Generation by Type, Heat Rate, and County: 2021

<span style='font-family:&quot;Avenir Next&quot;, &quot;Avenir Next&quot;; font-size:16px;'>Data originates from the California Energy Commission. Map and data are for 2021 and are current as of October7, 2022. Projection: NAD 1983 (2011) California (Teale) Albers (Meters). Contact Rebecca Vail at (916) 477-0738,or John Hingtgen at (916) 510-9747 for more information.</span>

Tags: CAOpenData, California Energy Commission, California Natural Resources Agency, EAD
Organization: California Energy Commission
Utility Natural Gas Generation by Type, Heat Rate, and County: 2020

<span style='font-family:&quot;Avenir Next&quot;, &quot;Avenir Next&quot;; font-size:16px;'>This map shows the percentage of energy produced by three types of natural gas power plants in each county - combined cycle, peaker, and aging plants. Data is for 2020. The table shows the raw data for the amount of energy produced and the fuel used by each plant type, as well as the number of each type of plant per county. The heat rates are also given.</span>

Tags: CAOpenData, California Energy Commission, California Natural Resources Agency, EAD
Organization: California Energy Commission