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

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.

This data set is similar to gamma-ray spectroscopy data and is designed for machine-learning data analysis. This dataset is generated by computer.

Scientific Information about Dataset

In gamma-ray spectroscopy, data is generated by capturing the number of emissions within a specific channel range of the radiation emitted by the sample. In scientific data, the sample produces photopeaks exhibiting a Gaussian distribution when statistically examined. A Gaussian distribution (Normal distribution) is a probability distribution dependent on three parameters.

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• x0 : Standard deviation
• σ (sigma)∶ Width of the Gaussian Distribution
• N : Number of the occurrences of the event

for more information: https://en.wikipedia.org/wiki/Normal_distribution

In Gamma Ray Spectroscopy

• x0 = Photopeak location
• σ (sigma) ∝ Detector resolution
• N ∝ Activity of the sample

Co-60 Gamma-ray Spectroscopy Example https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7989877%2F8fad8994bf11dca48657dc3d3e21f628%2Fco60-repc.png?generation=1689324671029928&alt=media" alt="">

Dataset Content

cha_ : Number of radiations captured by the channel from 0 to 2000 with 10 intervals

• cha_5 : Number of radiations captured by the channel between 0-10
• cha_15: Number of radiations captured by the channel between 10-20
• cha_25: Number of radiations captured by the channel between 20-30 . . .
• cha_n: Number of radiations captured by the channel between (n-5)-(n+5)
• x0 : Standard deviation of the Gaussian Distribution
• sigma : Width of the Gaussian Distribution
• N: Number of the emission in the Gaussian Distribution range

The warming global climate is threatening terrestrial ecosystem stability, including plant community structure and diversity. However, it remains unclear how distribution, richness and turnover of plant species are impacted by warming and wetting in northern China. In the present study, species distribution models were applied to predict the spatial distribution of 5,111 plant species based on 111,071 occurrence records in northern China. Additionally, variations in species richness and turnover rates were predicted for 2100 under three scenarios. The results indicated that approximately 70% of plant species will expand in their distribution, resulting in an increase in species richness. These changes will be driven mainly by temperature seasonality (TSN), annual precipitation (MAP), and mean temperature of the coldest quarter (MTCQ). However, about 30-40% of the species will face extinction risks, including a considerable number of endemic and Red-Listed species, and suitable habitat l..., , # Distribution range and richness of plant species are predicted to increase by 2100 due to a warmer and wetter climate in northern China.

Dataset DOI: 10.5061/dryad.zpc866tmp

Description of the data and file structure

This dataset consists of a main folder, Datasets.zip, which contains three sub-folders, and descriptions of the files are mentioned in the README included within the folder.

DataS1: Environmental data (climate and land cover) corresponding to grid cells in northern China and species occurrence data.

Note: For species occurrence data, all geographic coordinates associated with these species have been generalized to protect sensitive plant species classified as Vulnerable, Critically Endangered, or Near Threatened according to the IUCN Red List. Specifically, latitude and longitude values have been rounded to one decimal place. This generalization was performed to minimize potential risks to species and comply with best practices for s...,

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.

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 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

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The World Bank, the World Inequality Database (WID), and the Luxembourg Income Study (LIS) are all sources of data on poverty and inequality. They differ in terms of the income measure they use, the countries they cover, and the frequency of their data updates.

The World Bank uses a measure of income after taxes and transfers, which is called disposable income. It covers a wide range of countries, but the data is not updated as frequently as the data from the other two sources. The WID uses a measure of net national income after taxes, which is called net national income per adult. It covers a smaller range of countries than the World Bank, but the data is updated more frequently. The LIS uses a measure of disposable household income per capita. It covers a smaller range of countries than the World Bank or the WID, but the data is very detailed and goes back further in time. In general, the LIS data is considered to be the most reliable source of data on poverty and inequality. However, the World Bank and WID data are also useful, especially for countries that are not covered by the LIS.

1. The evolutionary distinctiveness (ED) score is a measure of phylogenetic isolation that quantifies the evolutionary uniqueness of a species.
2. Here, we compared the ED score of parasitic and non-parasitic cuckoo species worldwide, to understand whether parental care or parasitism represent the largest amount of phylogenetic uniqueness. Next, we focused only on 46 cuckoo species characterized by brood parasitism with a known number of host species, we explored the associations among ED score, number of host species and breeding range size for these species. We assessed these associations using phylogenetic generalized least squares (PGLS) models, taking into account the phylogenetic signal.
3. Parasitic cuckoo species were not more unique in terms of evolutionary distinctiveness than non-parasitic species. However, we found a significant negative association between the evolutionary uniqueness and host range, and a positive correlation between the number of host species and range size of parasitic cuckoos, probably suggesting a passive sampling of hosts by parasitic species as the breeding range broadens.
4. The findings of this study showed that more generalist brood parasites occupied very different positions in a phylogenetic tree, suggesting that they have evolved independently within the Cuculiformes order. Finally, we demonstrated that specialist cuckoo species also represent the most evolutionarily unique species in the order of Cuculiformes.

Depicts structural range improvements that are lines. This will include fences, Stock Driftway/Feedway (handling facility) and distribution pipelines (Range Water System). These improvements are assets tracking expenditures on the ground across the landscape.

We delineated the existing empirical ranges of western and eastern Joshua trees (Yucca brevifolia and Y. jaegeriana, respectively) with high fidelity across their ranges in Arizona, California, Nevada, and Utah, USA. Most species distribution models (SDMs) rely on sparse species occurrence datasets and random pseudoabsences. In contrast, the tall stature and distinctive branching arms of Joshua trees enabled us to definitively identify this species in publicly available satellite imagery, allowing us to use intensive visual grid searches to map empirical presences and absences at a 0.25 km2 resolution across most of the species’ ranges. We used the resulting presence/absence data to train species distribution models (SDMs) for each Joshua tree species, as well as a rangewide model comprising the distribution data from both species. Species distribution models link species' presence / absence data with environmental characteristics including topography, climate, and soils, revealin ...

The project lead for the collection of this data was Carrington Hilson. Elk (4 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Rowdy herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 4 elk, including 7 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50thpercentile contour (high use) and the 99thpercentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

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. Methods We obtained a species list from the IUCN Red List of 91 extant terrestrial African carnivores excluding Otariidae and Phocidae species. Threat layers included human modification, drought, and hunting pressure. Resource layers included habitat, protected area, biodiversity, and cultural diversity (Table S3). Because the spatial data obtained for threat and resource variables varied widely in format, resolution and spatial projection, we completed several pre-processing steps prior to analysis that depended on the format of the data. Data stored as polygons (e.g., PA) were processed to be represented in a numerical raster format, specifying the cell size of the output to be 5km2. The dataset of threat and resource variables had a wide range of values including continuous and binary classification. To facilitate comparison and calculation of the available conservation capacity index, all variables were normalized to scale from 0-1. To achieve this, we clipped each resource and threat raster file to the extent and geometry of each species range and then normalized the values of each variable at the clipped extent. Normalization was performed at the clipped extent, rather than at the continental scale to better capture the localized variability in resource and threat values occurring at the scale relevant to the species in question. The ACC index represents the difference between the resources available and threats occurring in a spatially explicit manner. For each species, the ACC was calculated for each grid cell within a species’ geographic range as well as at global level as an aggregated total (Eq 1). We assigned equal weight to each variable, although future analysis could scale particular variables based on their ecological importance for a given species or group of species, if this information is known. Eq 1:

ACCj represents the global level as the total capacity gap for species j where R is the sum of normalized resources values and T is the sum of normalized threat values across n locations of a species’ geographic range. Because all resource and threat variables may not be present at each location and to make that all variables that were present are weighted equally, we divided R and T byxij and yij represent the number of resources and threats included, respectively. ACCi were mapped for each 5 km2 grid cell across the species range. Positive values indicate a surplus of available resources that presumably can combat threats across landscapes, while negative values signal a deficit of resources and raise concerns for the local persistence of species. ACCi values that resulted in differences between resources and threats of <|0.01| were deemed negligible and assigned 0 as the functional value. In summary, the mean difference of averaged normalized resources and threats values were calculated to derive the ACC at the global scale as a single value (ACCj) and for each individual cell within a species range (ACCi).

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.

The Forest Reserve Range Distribution Units dataset represent the functional grazing management areas within the Rocky Mountains Forest Reserve. Boundaries of the allotments and/or distribution units may be defined by fencelines, height of land, natural boundaries, and/or a combination of these. This is currently the most accurate representation of the distribution unit boundary and is subject to change. In some cases these boundaries may extend beyond the boundary of the Rocky Mountains Forest Reserve. In these cases this is a representation of the management unit as a whole.

The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2022-2023. The Sherwood herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-7 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 2 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less then 27 hours. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

E-commerce Sales Prediction Dataset

This repository contains a comprehensive and clean dataset for predicting e-commerce sales, tailored for data scientists, machine learning enthusiasts, and researchers. The dataset is crafted to analyze sales trends, optimize pricing strategies, and develop predictive models for sales forecasting.

📂 Dataset Overview

The dataset includes 1,000 records across the following features:

📊 Data Summary

General Properties

Date: - Range: 01-01-2023 to 12-31-2023. - Contains 1,000 unique values without missing data.

Product_Category: - Categories: Electronics (21%), Sports (21%), Other (58%). - Most common category: Electronics (21%).

Price: - Range: From 244 to 999. - Mean: 505, Standard Deviation: 290. - Most common price range: 14.59 - 113.07.

Discount: - Range: From 0.01% to 49.92%. - Mean: 24.9%, Standard Deviation: 14.4%. - Most common discount range: 0.01 - 5.00%.

Customer_Segment: - Segments: Regular (35%), Occasional (34%), Other (31%). - Most common segment: Regular.

Marketing_Spend: - Range: From 2.41k to 10k. - Mean: 4.91k, Standard Deviation: 2.84k.

Units_Sold: - Range: From 5 to 57. - Mean: 29.6, Standard Deviation: 7.26. - Most common range: 24 - 34 units sold.

📈 Data Visualizations

The dataset is suitable for creating the following visualizations: - 1. Price Distribution: Histogram to show the spread of prices. - 2. Discount Distribution: Histogram to analyze promotional offers. - 3. Marketing Spend Distribution: Histogram to understand marketing investment patterns. - 4. Customer Segment Distribution: Bar plot of customer segments. - 5. Price vs Units Sold: Scatter plot to show pricing effects on sales. - 6. Discount vs Units Sold: Scatter plot to explore the impact of discounts. - 7. Marketing Spend vs Units Sold: Scatter plot for marketing effectiveness. - 8. Correlation Heatmap: Identify relationships between features. - 9. Pairplot: Visualize pairwise feature interactions.

💡 How the Data Was Created

The dataset is synthetically generated to mimic realistic e-commerce sales trends. Below are the steps taken for data generation:

1. Feature Engineering:

   • Identified key attributes such as product category, price, discount, and marketing spend, typically observed in e-commerce data.
   • Generated dependent features like units sold based on logical relationships.

2. Data Simulation:

   • Python Libraries: Used NumPy and Pandas to generate and distribute values.
   • Statistical Modeling: Ensured feature distributions aligned with real-world sales data patterns.

3. Validation:

   • Verified data consistency with no missing or invalid values.
   • Ensured logical correlations (e.g., higher discounts → increased units sold).

Note: The dataset is synthetic and not sourced from any real-world e-commerce platform.

🛠 Example Usage: Sales Prediction Model

Here’s an example of building a predictive model using Linear Regression:

Written in python

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load the dataset
df = pd.read_csv('ecommerce_sales.csv')

# Feature selection
X = df[['Price', 'Discount', 'Marketing_Spend']]
y = df['Units_Sold']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model training
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse:.2f}')
print(f'R-squared: {r2:.2f}')

Genotypes obtained from microsatellite analysis for Pistacia lentiscus populations

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 (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.