About this dataset

Context

The dataset tabulates the population of Grass Range by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Grass Range across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.63% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Grass Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Grass Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Gender. You can refer the same here

BackgroundAlthough increasingly sophisticated environmental measures are being applied to species distributions models, the focus remains on using climatic data to provide estimates of habitat suitability. Climatic tolerance estimates based on expert knowledge are available for a wide range of plants via the USDA PLANTS database. We aim to test how climatic tolerance inferred from plant distribution records relates to tolerance estimated by experts. Further, we use this information to identify circumstances when species distributions are more likely to approximate climatic tolerance.MethodsWe compiled expert knowledge estimates of minimum and maximum precipitation and minimum temperature tolerance for over 1800 conservation plant species from the ‘plant characteristics’ information in the USDA PLANTS database. We derived climatic tolerance from distribution data downloaded from the Global Biodiversity and Information Facility (GBIF) and corresponding climate from WorldClim. We compared expert-derived climatic tolerance to empirical estimates to find the difference between their inferred climate niches (ΔCN), and tested whether ΔCN was influenced by growth form or range size.ResultsClimate niches calculated from distribution data were significantly broader than expert-based tolerance estimates (Mann-Whitney p values < 0.001). The average plant could tolerate 24 mm lower minimum precipitation, 14 mm higher maximum precipitation, and 7° C lower minimum temperatures based on distribution data relative to expert-based tolerance estimates. Species with larger ranges had greater ΔCN for minimum precipitation and minimum temperature. For maximum precipitation and minimum temperature, forbs and grasses tended to have larger ΔCN while grasses and trees had larger ΔCN for minimum precipitation.

This raster portrays the distribution of sagebrush within the geographic extent of the sagebrush biome in the United States. It was created for the Western Association of Fish and Wildlife Agency’s (WAFWA) Sagebrush Conservation Strategy publication as a visual for the schematic figures and to calculate summary statistics. This distribution incorporates the most recently available sagebrush cover mapping (Xian et al. 2015, Rigge et al. 2019) and classified LANDFIRE EVT (Department of Ecosystem Science, University of Wyoming 2016). Both datasets were rigorously evaluated and extensive ground measurements taken to evaluate accuracy by the respective authors. We created a combined binary sagebrush distribution by classifying the Rigge et al. (2019) product to a binary form where sagebrush cover was greater than 5%, which is equal to the root mean squared error of the analysis (RMSE = 5.09). The Rigge et al. (2019) raster is not complete across the sagebrush biome, so we filled in the areas of NoData with the 'Sagebrush-dominated Ecological Systems' pixels from binary sagebrush raster (Department of Ecosystem Science, University of Wyoming 2016) to create a continuous raster across the sagebrush biome. The input layers are informative to conditions circa the beginning of 2015.

The Mechanical MNIST – Distribution Shift dataset contains the results of finite element simulation of heterogeneous material subject to large deformation due to equibiaxial extension at a fixed boundary displacement of d = 7.0. The result provided in this dataset is the change in strain energy after this equibiaxial extension. The Mechanical MNIST dataset is generated by converting the MNIST bitmap images (28x28 pixels) with range 0 - 255 to 2D heterogeneous blocks of material (28x28 unit square) with varying modulus in range 1- s. The original bitmap images are sourced from the MNIST Digits dataset, (http://www.pymvpa.org/datadb/mnist.html) which corresponds to Mechanical MNIST – MNIST, and the EMNIST Letters dataset (https://www.nist.gov/itl/products-and-services/emnist-dataset) which correspond to Mechanical MNIST – EMNIST Letters. The Mechanical MNIST – Distribution Shift dataset is specifically designed to demonstrate three types of data distribution shift: (1) covariate shift, (2) mechanism shift, and (3) sampling bias, for all of which the training and testing environments are drawn from different distributions. For each type of data distribution shift, we have one dataset generated from the Mechanical MNIST bitmaps and one from the Mechanical MNIST – EMNIST Letters bitmaps. For the covariate shift dataset, the training dataset is collected from two environments (2500 samples from s = 100, and 2500 samples from s = 90), and the test data is collected from two additional environments (2000 samples from s = 75, and 2000 samples from s = 50). For the mechanism shift dataset, the training data is identical to the training data in the covariate shift dataset (i.e., 2500 samples from s = 100, and 2500 samples from s = 90), and the test datasets are from two additional environments (2000 samples from s = 25, and 2000 samples from s = 10). For the sampling bias dataset, datasets are collected such that each datapoint is selected from the broader MNIST and EMNIST inputs bitmap selection by a probability which is controlled by a parameter r. The training data is collected from two environments (9800 from r = 15, and 200 from r = -2), and the test data is collected from three different environments (2000 from r = -5, 2000 from r = -10, and 2000 from r = 1). Thus, in the end we have 6 benchmark datasets with multiple training and testing environments in each. The enclosed document “folder_description.pdf'” shows the organization of each zipped folder provided on this page. The code to reproduce these simulations is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST/blob/master/generate_dataset/Equibiaxial_Extension_FEA_test_FEniCS.py).

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in South Range, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 9,872, while the mean income for the highest quintile (20% of households with the highest income) is 175,498. This indicates that the top earners earn 18 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 352,222, which is 200.70% higher compared to the highest quintile, and 3567.89% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income. You can refer the same here

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias 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 (https://www.wildlife.ca.gov/Data/CWHR). 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.

Distribution (range) polygons were assembled by regional SARA biologists using the best available information, including COSEWIC status reports, recovery potential assessments, academic literature, and expert opinion. These spatial data support the protection, recovery and conservation of species listed as Extirpated, Endangered, Threatened or Special Concern under SARA. Species distributions are also described and displayed in Recovery Strategies, Action Plans and/or Management Plans. Discrepancies may exist between the distribution data shown in a species SARA recovery document and the current spatial data. Please contact DFO for more information on any data discrepancies. Please refer to the metadata included with the data for full entity attribute information

This dataset provides up-to-date, high-precision species distribution maps for 379 terrestrial vertebrates in Taiwan. We used species distribution modeling as the base and then aggregated multiple open datasets describing species occurrence and environmental factors as data sources. Thereafter, we estimated the primary broad-scale and high spatial resolution species range maps using the MaxEnt modeling algorithm, and then consulted experts on each taxa to refine these maps.There are three files in this dataset:model_metadata.csv - metadata of models and information of species, including species taxonomic information, and model arguments.range_maps.shp - species range maps in the shapefile format, each species has its own polygon.

This dataset contains GIS layers that depict the known spatial distributions (i.e., ranges) of the five subspecies of ringed seals (Phoca hispida). It was produced as part of a U.S. Endangered Species Act status review, which included delineating the species in question and assessing its risk of extinction within the foreseeable future throughout all or a significant portion of its range. Its boundaries are based on previously published range maps and/or descriptions of the species' distribution in published or unpublished accounts. All boundaries should be considered approximate.

Accurate information on the distribution of existing species is crucial to assess regional biodiversity. However, data inventories are insufficient in many areas. We examine the ability of Multivariate Adaptive Regression Splines (MARS) multi-response species distribution model to overcome species’ data limitations and portray plant species distribution patterns for 199 South Korean plant species. The study models species with two or more observations, examines their contribution to national patterns of species richness, provides a sensitivity analysis of different range threshold cutoff approaches for modeling species’ ranges, and presents considerations for species modeling at fine spatial resolution. We ran MARS models for each species and tested four threshold methods to transform occurrence probabilities into presence or absence range maps. Modeled occurrence probabilities were extracted at each species’ presence points, and the mean, median, and one standard deviation (SD) calculated to define data-driven thresholds. A maximum sum of sensitivity and specificity threshold was also calculated, and the range maps from the four cutoffs were tested using independent plant survey data. The single SD values were the best threshold tested for minimizing omission errors and limiting species ranges to areas where the associated occurrence data were correctly classed. Eight individual species range maps for rare plant species were identified that are potentially affected by resampling predictor variables to fine spatial scales. We portray spatial patterns of high species richness by assessing the combined range maps from three classes of species: all species, endangered and endemic species, and range-size rarity of all species, which could be used in conservation planning for South Korea. The MARS model is promising for addressing the common problem of few species occurrence records. However, projected species ranges are highly dependent on the threshold and scale criteria, which should be assessed on a per-project basis.

This dataset contains home range size, habitat availability and selection ratio data, calculated from GPS data fixes collected from individual European nightjars, in four concurrent years (2015-2018). Home ranges are 95% areas of use, presented in hectares. Habitat availability data are presented as the percentage (%) of each habitat category (n = 6, pooled from 14 original habitat types) available to each individual within their 95% home range. Selection ratios are Manly Selection Ratios for 14 habitat types and express the extent to which each habitat type is used by each individual bird, compared to how much of it is available. Selection Ratios >1 express positive selection – i.e. used more than expected, given availability. Selection Ratios <1 express avoidance – i.e. used less than expected, given availability. Full details about this dataset can be found at https://doi.org/10.5285/d5cc1b92-6862-4475-8aa1-5936786d12ab

Conservation requires both a needs assessment and prioritization scheme for planning and implementation. Range maps are critical for understanding and conserving biodiversity, but current range maps often omit content, negating important metrics of variation in populations and places. Here, we integrate a myriad of conditions that are spatially explicit across distributions of carnivores to identify gaps in capacity necessary for their conservation. Expanding on traditional gap analyses that focus almost exclusively on quantifying discordance in protected area coverage across a species' range, our work aggregates threat layers (e.g., drought, human pressures) with resources layers (e.g., protected areas, cultural diversity) to identify gaps in available conservation capacity (ACC) across ranges for 91 African carnivores. Our model indicated that all species have some portion of their range at risk of contraction, with an average of 15 percentage range loss. We found that the ACC differed based on body size and taxonomy. Results deviated from current perceptions of extinction risks for species with an International Union for Conservation of Nature (IUCN) threat status of Least Concern and yielded insights for species categorized as Data Deficient. Our socio-ecological gap analysis presents a geospatial approach to inform decision-making and resource allocation in conservation. Ultimately, our work advances forecasting dynamics of species' ranges that are increasingly vital in an era of great socio-ecological change to mitigate human–wildlife conflict and promote inclusive carnivore conservation across geographies.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in South Range: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 38(14.29%) households where the householder is under 25 years old, 72(27.07%) households with a householder aged between 25 and 44 years, 80(30.08%) households with a householder aged between 45 and 64 years, and 76(28.57%) households where the householder is over 65 years old.
• In South Range, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income by age. You can refer the same here

Polymorphisms in a population are expected to increase growth rate and stability of the population, leading to the expansion of geographic distribution and mitigation of extinction risk of a species. However, the generality of such ecological consequences of color polymorphism remains uncertain. Here, via a comparative approach, we assessed whether color polymorphisms influence climatic niche breadth and extinction risk in some groups of damselflies, butterflies, and vertebrates. The climatic niche breadth was greater and extinction risk was lower in polymorphic species than in monomorphic species in all taxa analyzed. The results suggest that color polymorphism facilitates range expansion and species persistence.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias 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 (https://www.wildlife.ca.gov/Data/CWHR). 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.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias 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 (https://www.wildlife.ca.gov/Data/CWHR). 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.

Described in Materials and methods section in paper by Rahimi O., Ohana-Levi N., Brauner H., Inbar N., Hübner S. and Drori E. (2021) "Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range"

1. A central focus of ecology is to understand the conditions under which biotic interactions affect species’ abundance and distribution. Classic and recent studies have shown that biotic interactions can strongly impact local or regional patterns of species abundance, but two fundamental questions remain largely unaddressed for non-competitive biotic interactions. First, do the effects of these interactions on population performance change predictably with environmental context? Second, to what extent do population-scale effects contribute to limiting species’ geographic distributions?
2. To address these questions, we experimentally assessed the extent to which pollen limitation and insect seed predators affected the fecundity and projected population growth rate (λ) of the native forb Astragalus utahensis. We studied populations at the center and northern edge of the latitudinal range of A. utahensis that occur across a gradient in abiotic harshness characterized primarily by decli...

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias 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 (https://www.wildlife.ca.gov/Data/CWHR). 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.

a. Data content (data file/table name, including observation index content)

Data file name: Liaoning Province 1:100000 Plant Distribution Dataset (2000)

Main content: Liaoning Province 1:100000 Plant Distribution Dataset (2000) - Data Entity. shp

b. Construction purpose

Used for research on agricultural ecological effects, land use evaluation, etc.

c. Service object

Used by scientific and technological workers engaged in research on terrestrial ecosystems, agricultural ecology, land use, and other related fields, and also applicable to government decision-making departments.

d. Time range of data

The data was collected in 2000.

e. The spatial range and projection method of data

Space scope: Liaoning Province