AndreaBozzo/ceres-open-data-index huggingface dataset:
CAL FIRE Working Forest Management Plans TA83

<div>Working Forest Management Plans (WFMPs) and Working Forest Harvest Notices (WFNs) approved by the California Department of Forestry and Fire Protection for landowners with less than 10,000 acres of land, not primarily engaged in the manufacture of forest products. Objectives include maintaining, restoring, or creating uneven aged managed timber stand conditions, achieving sustained yield, and promoting forestland stewardship that protects watersheds, fisheries and wildlife habitats, and other important values on non-federal lands in California. WFMPs are living documents that do not expire. For more information, see Subchapter 7, Article 6.95, Working Forest Management Plan, of the <a href='https://bof.fire.ca.gov/regulations/bills-statutes-rules-and-annual-california-forest-practice-rules' rel='nofollow ugc' target='_blank'>California Forest Practice Rules</a>.  Actual timber operations are conducted under Working Forest Harvest Notices (WFNs). Data from 2020 to present. </div><div><br /></div><div>This data set is in the California Teale Albers NAD83 meters projection (TA83).<br /></div><div><br /></div><div>Link to the California Forest Practice Rules: </div><div>https://bof.fire.ca.gov/regulations/bills-statutes-rules-and-annual-california-forest-practice-rules/</div><div><br /></div><div><span style='font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 16px;'>For additional information on specific plans see the California Timber Regulation and Environmental Evaluation System (CalTREES): https://caltreesplans.resources.ca.gov/caltrees/Default.aspx</span><br /></div>
Racer Habitat Model for NSNF Connectivity - CDFW [ds1050]

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].
Pallid Bat Habitat Model for NSNF Connectivity -  CDFW [ds1049]

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].
Southern Alligator Lizard Habitat Model for NSNF Connectivity - CDFW [ds1051]

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].
Spotted Towhee Habitat Model for NSNF Connectivity - CDFW [ds1052]

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].
Western Gray Squirrel Habitat Model for NSNF Connectivity - CDFW [ds1053]

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].
CAL FIRE Working Forest Harvest Notices TA83

<div>Working Forest Management Plans (WFMPs) and Working Forest Harvest Notices (WFNs) approved by the California Department of Forestry and Fire Protection for landowners with less than 10,000 acres of land, not primarily engaged in the manufacture of forest products. Objectives include maintaining, restoring, or creating uneven aged managed timber stand conditions, achieving sustained yield, and promoting forestland stewardship that protects watersheds, fisheries and wildlife habitats, and other important values on non-federal lands in California. WFMPs are living documents that do not expire. For more information, see Subchapter 7, Article 6.95, Working Forest Management Plan, of the <a href='https://bof.fire.ca.gov/regulations/bills-statutes-rules-and-annual-california-forest-practice-rules' rel='nofollow ugc' target='_blank'>California Forest Practice Rules</a>.  Actual timber operations are conducted under Working Forest Harvest Notices (WFNs). Data from 2020 to present. </div><div><br /></div><div>This data set is in the California Teale Albers NAD83 meters projection (TA83).<br /></div><div><br /></div><div>Link to the California Forest Practice Rules: </div><div>https://bof.fire.ca.gov/regulations/bills-statutes-rules-and-annual-california-forest-practice-rules/</div><div><br /></div><div><span style='font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 16px;'>For additional information on specific plans see the California Timber Regulation and Environmental Evaluation System (CalTREES): https://caltreesplans.resources.ca.gov/caltrees/Default.aspx</span><br /></div>
Wood Duck Habitat Model for NSNF Connectivity - CDFW [ds1054]

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].
Western Pond Turtle Habitat Model for NSNF Connectivity - CDFW [ds1055]

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].
Yellow-billed Magpie Habitat Model for NSNF Connectivity - CDFW [ds1056]

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].
Broad-Footed Mole Range - CWHR M018 [ds1814]

<div style='text-align:Left;'><div><div><p style='margin:0 0 14 0;'><span><span>Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for California's wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (</span><a href='https://www.wildlife.ca.gov/Data/CWHR' style='text-decoration:underline;' rel='nofollow ugc'><span>https://www.wildlife.ca.gov/Data/CWHR</span></a><span>). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.</span></span></p></div></div></div>
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>
NLCD 2019 Land Cover California Subset

<div style='text-align:Left;'><div><div><p><span>The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.</span></p></div></div></div>
NLCD 2013 Land Cover California Subset

<div style='text-align:Left;'><div><div><p><span>The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.</span></p></div></div></div>