The fitted slopes of the E3C/E2C data distributions as a function of jet pt are used to illustrate the dependency...

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Blockchain data query: Falcon distribution data

Introduction

The Global Data Distribution Service (DDS) Market is poised for remarkable growth, expected to reach USD 71.7 billion by 2034, up from USD 4.4 billion in 2024, growing at a CAGR of 32.2% from 2025 to 2034. North America is leading the market, with a dominant 40.5% share, generating USD 1.78 billion in revenue in 2024.

This growth is driven by the increasing demand for high-performance data distribution solutions in industries such as defense, healthcare, automotive, and industrial automation. DDS enables real-time data exchange, making it essential for applications requiring low latency and high reliability.

About this dataset

Context

The dataset tabulates the population of Bay View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Bay View. The dataset can be utilized to understand the population distribution of Bay View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Bay View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Bay View.

Key observations

Largest age group (population): Male # 60-64 years (60) | Female # 65-69 years (54). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Bay View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Bay View is shown in the following column.
• Population (Female): The female population in the Bay View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Bay View for each age group.

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 Bay View Population by Gender. You can refer the same here

We study the impact of post-1990 school finance reforms, during the so-called "adequacy" era, on absolute and relative spending and achievement in low-income school districts. Using an event study research design that exploits the apparent randomness of reform timing, we show that reforms lead to sharp, immediate, and sustained increases in spending in low-income school districts. Using representative samples from the National Assessment of Educational Progress, we find that reforms cause increases in the achievement of students in these districts, phasing in gradually over the years following the reform. The implied effect of school resources on educational achievement is large.

About this dataset

Context

The dataset tabulates the population of Lake View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lake View. The dataset can be utilized to understand the population distribution of Lake View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lake View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Lake View.

Key observations

Largest age group (population): Male # 50-54 years (62) | Female # 50-54 years (75). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Lake View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lake View is shown in the following column.
• Population (Female): The female population in the Lake View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Lake View for each age group.

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 Lake View Population by Gender. You can refer the same here

This data set contains the Navy's Master Oceanographic Observation Data Set (MOODS) declassified data from April 1, 1985-November 30, 1990. This data which contains only the radio declassified radio message bathythermograph reports has been quality improved to remove duplicates, identify platforms. The data was submitted by A.D. Stroud of Ocean Applications Group, Fleet Numerical Oceanography Center, Monterey, CA. The data are binary in the MOODS format. The documentation includes data distribution maps. *Original data was submitted on tape A01351.

The dataset contains the research data for the manuscript entitled ‘Puncture Failure Size Probability Distribution for CO2 Pipelines’ which is submitted to Internaltional Journal of Greenhouse Gas Control. The data are the raw data the authors used to produce all the figures in the manuscript. The data are made publicly available so that other researchers can make future comparisons. Anyone reading the paper may be able to download the data for reseaech purposes.

About this dataset

Context

The dataset tabulates the population of Ocean View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Ocean View. The dataset can be utilized to understand the population distribution of Ocean View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Ocean View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Ocean View.

Key observations

Largest age group (population): Male # 75-79 years (253) | Female # 75-79 years (268). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Ocean View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Ocean View is shown in the following column.
• Population (Female): The female population in the Ocean View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Ocean View for each age group.

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 Ocean View Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Forest View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Forest View. The dataset can be utilized to understand the population distribution of Forest View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Forest View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Forest View.

Key observations

Largest age group (population): Male # 5-9 years (106) | Female # 0-4 years (78). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Forest View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Forest View is shown in the following column.
• Population (Female): The female population in the Forest View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Forest View for each age group.

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 Forest View Population by Gender. You can refer the same here

These data are a compilation of currently available seagrass GIS data sets for the west coast of the United States. These data have been compiled from seventeen different data sources. The source data were acquired over a large range of time periods, at many different spatial resolutions using a variety of methods, including aerial photography, videography, multispectral sensors, sonar, and field surveys. Users are cautioned to use these data as only a regional view of seagrass locations. Areas without mapped seagrass may contain seagrass, but digital data were unavailable during this data compilation

Resetting a stochastic process has been shown to expedite the completion time of some complex task, such as finding a target for the first time. Here we consider the cost of resetting by associating a cost to each reset, which is a function of the distance travelled during the reset event. We compute the Laplace transform of the joint probability of first passage time $t_f$, number of resets $N$ and resetting cost $C$, and use this to study the statistics of the total cost. We show that in the limit of zero resetting rate the mean cost is finite for a linear cost function, vanishes for a sub-linear cost function and diverges for a super-linear cost function. This result contrasts with the case of no resetting where the cost is always zero. For the case of an exponentially increasing cost function we show that the mean cost diverges at a finite resetting rate. We explain this by showing that the distribution of the cost has a power-law tail with continuously varying exponent that depends on the resetting rate. The dataset is related to the upcoming paper John C. Sunil, Richard A. Blythe, Martin R. Evans and Satya N. Majumdar (in submission), 'The Cost of Stochastic Resetting'.

The map viewer is a high-level display of PacifiCorp’s distribution system to facilitate a variety of perspectives and reflects information as of Fall 2021. One view supports a generalized assessment of installed distributed generation resources, such as qualified facilities, solar, battery or other technologies. The data presented is at a point in time and this is provided for information only.

Benchmarking the robustness to distribution shifts traditionally relies on dataset collection which is typically laborious and expensive, in particular for datasets with a large number of classes like ImageNet. An exception to this procedure is ImageNet-C (Hendrycks & Dietterich, 2019), a dataset created by applying common real-world corruptions at different levels of intensity to the (clean) ImageNet images. Inspired by this work, we introduce ImageNet-Cartoon and ImageNet-Drawing, two datasets constructed by converting ImageNet images into cartoons and colored pencil drawings, using a GAN framework (Wang & Yu, 2020) and simple image processing (Lu et al., 2012), respectively.

This repository contains ImageNet-Cartoon and ImageNet-Drawing. Checkout the official GitHub Repo for the code on how to reproduce the datasets.

If you find this useful in your research, please consider citing:

@inproceedings{imagenetshift,
 title={ImageNet-Cartoon and ImageNet-Drawing: two domain shift datasets for ImageNet},
 author={Tiago Salvador and Adam M. Oberman},
 booktitle={ICML Workshop on Shift happens: Crowdsourcing metrics and test datasets beyond ImageNet.},
 year={2022}
}

This dataset provides supporting information for the species distribution data used in the associated manuscript. Collections of five non-native fish species were made by a number of institutions, and several capture techniques were used. This dataset also includes number of individuals of each species captured at each locality.

About this dataset

Context

The dataset tabulates the population of Valley View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Valley View. The dataset can be utilized to understand the population distribution of Valley View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Valley View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Valley View.

Key observations

Largest age group (population): Male # 10-14 years (58) | Female # 5-9 years (47). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Valley View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Valley View is shown in the following column.
• Population (Female): The female population in the Valley View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Valley View for each age group.

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 Valley View Population by Gender. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Bay View. The dataset can be utilized to gain insights into gender-based income distribution within the Bay View population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Bay View, among individuals aged 15 years and older with income, there were 262 men and 255 women in the workforce. Among them, 157 men were engaged in full-time, year-round employment, while 120 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 12.10% fell within the income range of under $24,999, while 16.67% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 18.47% of men in full-time roles earned incomes exceeding $100,000, while 9.17% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

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:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Bay View median household income by race. You can refer the same here

1. Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population.

2. Here, we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the R package SDLfilter.

3. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking data-set of flatback turtles, Natator d...

Code for simulations and analysis were written by Ryan S. Terrill. Hinge Model functions and the R package "chngpt" were written by Youyi Fong. Painted Bunting data were collected at the Moore Lab of Zoology, and in the field in Alamos, Sonora, Mexico, by Ryan S. Terrill.