About this dataset

Context

The dataset tabulates the population of Height of Land township by race. It includes the population of Height of Land township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Height of Land township across relevant racial categories.

Key observations

The percent distribution of Height of Land township population by race (across all racial categories recognized by the U.S. Census Bureau): 96.70% are white, 0.27% are Black or African American, 1.79% are American Indian and Alaska Native and 1.24% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Height of Land township
• Population: The population of the racial category (excluding ethnicity) in the Height of Land township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Height of Land township 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 Height of Land township Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Height of Land township by race. It includes the distribution of the Non-Hispanic population of Height of Land township across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Height of Land township across relevant racial categories.

Key observations

Of the Non-Hispanic population in Height of Land township, the largest racial group is White alone with a population of 679 (96.59% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Height of Land township
• Population: The population of the racial category (for Non-Hispanic) in the Height of Land township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Height of Land township total Non-Hispanic 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 Height of Land township Population by Race & Ethnicity. You can refer the same here

This statistic depicts the average body weight of U.S. men aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average male body weight for those that identified as non-Hispanic white has increased from 192.3 in 1999-2000 to 202.2 in 2015-2016.

Assessing rockfish abundance in untrawlable habitats is a key area of study for the Alaska Fisheries Science Center. In order to accurately estimate abundance knowledge of rockfish height off bottom by species and fish length. Since 2013, we have performed a series of experiments to examine rockfish height off bottom using a triggered camera system. These data area stored as image files, .Rdata files, .sql3 files and as .xlsx files.

Additional file 1: Table S1. Patterns of service era per birth cohort and across all MVP participants stratified by sex and HARE superpopulations. Each row represents a distinct pattern of service across nine service eras; the frequency of each is calculated by birth cohort and for all MVP participants. Service patterns with less than 11 participants were omitted to preserve data privacy of the participant so HARE total population sample sizes are slightly lower than those reported in Table 1. Table S2. Sample size per birth cohort derived from cumulative distribution function of year of birth. Table S3. Mean ancestry proportion of five 1kGP reference populations in all birth cohorts and HARE superpopulations. Two-sided Z-tests were used to compare the statistical difference in means between groups and the corresponding p values reflect this difference. Standardized mean differences reflect the magnitude of effect size difference between two groups. Table S4. Comparison of height across birth cohorts in each MVP HARE superpopulations. Table S5. Metrics for GWAS of height in each ancestry per birth cohort using both methods of population assignment. Heritability, LDSC intercepts, and attenuation ratios were compared across birth cohorts, within each method, using two-sided Z-tests. Multiple testing correction was applied using a false discovery rate of 5%; differences surviving multiple testing correction are highlighted in yellow. Table S6. Metrics for GWAS of height compared across method used to define superpopulations. Two-sided Z-tests were used to compare heritability, LDSC intercepts, and attenuation ratios between HARE and 1kGP+HGDP superpopulation assignments. Multiple testing correction was applied using a false discovery rate of 5%.

This statistic depicts the average body weight of U.S. females aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average female body weight for those that identified as non-Hispanic white has increased from ***** in ********* to ***** in *********.

The purpose of this data collection is to provide height data for runaway apprentices and military deserters in colonial and early Republican America (1726-1825). Data were taken from newspaper advertisements describing the runaways. Variables include year, decade, and state in which the ad appeared, year, decade, and place of birth (Germany, Ireland, or region of the United States) of the runaway, and runaway's former place of residence. Additional information concerning the runaways includes first and last name, race, sex, age, height, and whether the deserter was a member of the Army or Navy.

Includes accelerometer data using an ActiGraph to assess usual sedentary, moderate, vigorous, and very vigorous activity at baseline, 6 weeks, and 10 weeks. Includes relative reinforcing value (RRV) data showing how participants rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes data on the breakpoint, or Pmax of the RRV, which was the last schedule of reinforcement (i.e. 4, 8, 16, …) completed for the behavior (exercise or sedentary). For both Pmax and RRV score, greater scores indicated a greater reinforcing value, with scores exceeding 1.0 indicating increased exercise reinforcement. Includes questionnaire data regarding preference and tolerance for exercise intensity using the Preference for and Tolerance of Intensity of Exercise Questionnaire (PRETIEQ) and positive and negative outcome expectancy of exercise using the outcome expectancy scale (OES). Includes data on height, weight, and BMI. Includes demographic data such as gender and race/ethnicity. Resources in this dataset:Resource Title: Actigraph activity data. File Name: AGData.csvResource Description: Includes data from Actigraph accelerometer for each participant at baseline, 6 weeks, and 10 weeks.Resource Title: RRV Data. File Name: RRVData.csvResource Description: Includes data from RRV at baseline, 6 weeks, and 10 weeks, OES survey data, PRETIE-Q survey data, and demographic data (gender, weight, height, race, ethnicity, and age).

Background: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life. Methods: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) then in a large, outpatient, pediatric sample (N=14,695). Results: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby ..., Sources of data: Information on infants was ascertained from two sources: the STORK birth cohort and the STARR research registry. (1) Detailed methods for the STORK birth cohort have been described previously. In brief, a multiethnic cohort of mothers and babies was followed from the second trimester of pregnancy to the babies’ third birthday. Healthy women aged 18–42 years with a single-fetus pregnancy were enrolled. Households were visited every four months until the baby’s third birthday (nine baby visits), with the weight of the baby at each visit recorded in pounds. Medical charts were abstracted for birth weight and length. (2) STARR (starr.stanford.edu) contains electronic medical record information from all pediatric and adult patients seen at Stanford Health Care (Stanford, CA). STARR staff provided anonymized information (weight, height and age in days for each visit through age three years; sex; race/ethnicity) for all babies during the period 03/2013–01/2022 followed from bi..., The R code, as written in RStudio, are saved as MME_weights.RMD, MME_heights.RMD, MME_predictions_weights.RMD, and MME_predictions_heights.RMD. The tab-delimited and anonymized source data for weights and heights (both jittered) are posted. These can be used with the R code-but the user will need to correct input and output filepaths used in the script. The HTML version of these files is available as well, in case viewing the scripts without opening them in R is desired. R_sessionInfo.txt contains the R software version, as well as the versions of the packages included in the code. See the methods section for the description of the starting parameters for the nls() function., # Data for: A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the US

https://doi.org/10.5061/dryad.4j0zpc8jf

Description of the data and file structure

Data for this study include, per baby: sex, age in days, and, over time, weight in Kg and height in cm. Each baby had at least 5 visits. Our goal was to fit each baby’s data to a curve as described by a modified Michaelis-Menten equation, allowing interpolation of missing weight or height values. Among the subset of all infants who had 7 well-baby visits in the first year of life, and 12 visits over 3 years, we further explored the minimum number of, and which, data points were necessary for good fit. Finally, among babies with 5 time points in year 1, and 2 in both year 2 and year 3, we examined whether weight or height data early in life could predict growth in later months.

To meet anonymization guidelines, we are providing only STARR dat...

The Court Eviction Diversion Program provides financial assistance for both past due and future rent for households below 80% of area median income who are facing eviction and received a Forcible Detainer.

Objectives: The purpose of this study was to determine and weigh the anthropometric indicators that were associated with pacing performances for each Olympic rowing category.Methods: Between 2010 and 2015, 1,148 rowers (650 men and 498 women) participated in the finals of World Championships in each heavyweight Olympic event. They were categorized into four morphological clusters according to their height and body mass index (BMI): tall and thin (TT), tall and robust (TR), small and thin (ST), and small and robust (SR). Time and speed, were collected every 50 m for all boats in each competition. Non-parametric inferential methods were used to understand the differences in performance between morphological clusters over the entire race. After, we calculated a new indicator to determine the differences between these morphotypes within the race.Results: In this article, we determined which morphologies had a significant effect on speed for both men and women. For example, the biggest rowers were the fastest in skiff. Analysis of each 50 m demonstrated that between the four morphological categories that the TR male athletes were significantly faster than their ST counterparts between the 800 and 2,000 m of the race by 1.76% of mean speed. Furthermore, the SR were the fastest in female coxless pairs over the majority of the race. These differences in speed by morphological cluster are summarized, by race segment, for all categories and sex.Conclusion: Anthropometric factors impact pacing among rowers' categories. Coupling anthropometry and race pacing is not only helpful to understand which factors work where, but is also helpful in improving training and performance. This can help both in the recruiting of rowers for specific boats and adapting the race strategy. In future, the method used can be adapted for factors other than anthropometry. It can also be individualized to enable athletes to prepare for their race according to future competitors.

Includes 24 hour recall data that children were instructed to fill-out describing the previous day’s activities at baseline, weeks 2 and 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks). Includes accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks. Includes demographic data such as weight, height, gender, race, ethnicity, and birth year. Includes relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes questionnaire data regarding exercise self-efficacy using the Children’s Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG). Resources in this dataset:Resource Title: 24 Hour Recall Data. File Name: 24 hour recalldata.xlsxResource Description: Children were instructed to fill out questions describing the previous day's activities at baseline, week 2, and week 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks).Resource Title: Actigraph activity data. File Name: actigraph activity data.xlsxResource Description: Accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks.Resource Title: Liking Data. File Name: liking data.xlsxResource Description: Relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10.Resource Title: Demographics. File Name: Demographics (Birthdate-Year).xlsxResource Description: Includes demographic data such as weight, height, gender, race, ethnicity, and year of birth.Resource Title: Questionnaires. File Name: questionnaires.xlsxResource Description: Questionnaire data regarding exercise self-efficacy using the Children's Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG).

Geopotential height (m). Race 2024-10-25 hours 12 UTC - Valid from 2024-10-25 hours 12 UTC to 2024-10-29 hours 00 UTC. WRF meteorological model (Weather Research and Forecasting model), ARW core (version 3.2) with spatial resolution at 3km, temporal resolution 60 hours, interval 1 hour.

The purpose of this project is to pay electric and water/sewer expenses for residents who would otherwise have their utilities turned off.

American Community Survey (ACS) 5-Year 2009-2013 demographic, socioeconomic, and housing subset information selected by HUD, and compiled at the 2010 census tract level for the analysis of areas in Florida affected by Hurricane Irma (DR4337).

Selected characteristics include:

Poverty

• Universe for Persons in Poverty
  
• Total: Persons in Poverty
  
• Poverty Rate
  

Housing Tenure

• Owner Occupied
  
• Owner Occupied as a %
  
• Renter Occupied Renter Occupied as a %
  

Race and Ethnicity

• Total Population
  
• White alone (not Hispanic)
  
• White alone (not Hispanic) as a %
  
• Black or African American alone (not Hispanic)
  
• Black or African American alone (not Hispanic) as a %
  
• American Indian and Alaska Native alone (not Hispanic)
  
• American Indian and Alaska Native alone (not Hispanic) as a %
  
• Asian alone (not Hispanic) Asian alone (not Hispanic) as a %
  
• Native Hawaiian and Other Pacific Islander alone (not Hispanic)
  
• Native Hawaiian and Other Pacific Islander alone (not Hispanic) as a %
  
• Some other race alone (not Hispanic)
  
• Some other race alone (not Hispanic) as a %
  
• Two or more races (not Hispanic)
  
• Two or more races (not Hispanic) as a %
  
• Persons of Hispanic Origin Persons of Hispanic Origin as a %
  

ABSTRACT The objective of this study was to determine the effects of morphometric measurements on race performance (m/sec) of Thoroughbred horses. Data of morphometric measurements (withers height, rump height, chest girth, chest width, front chest width, chest depth, neck length, shoulder length, length of withers to rump, rump length, body length, head width, head length, and cannon circumference) were taken from 244 Thoroughbred horses chosen at random. A total of 2888 racing records were considered for race performance. The effects of environmental factors on morphometric measurements (stallion, gender, age, and mother age) and race performance (gender, age, mother age, year, hippodrome, race distance, racetrack, and race type) were analyzed using the least squares method. Principal component analysis (PCA) was performed for morphometric measurements, and then the factor loadings were rotated by Varimax method. Multiple linear regression analysis was applied for the significance of the obtained factors on race performance. Significant effects for stallion on all morphometric measurements, except head length and width, and for gender on withers height, cannon circumference, and head width were determined. Race performance was significantly influenced by stallion, gender, age, year, hippodrome, race distance, racetrack, and race type. After PCA, four factors with eigenvalues >1 were attained. The effects of factors on race performance were not significant, according to the results of multiple linear regression analysis. Therefore, the effects of the morphometric measurements examined on the race performance were not significant in Thoroughbred horses.

Geopotential height (m). Race of 2024-08-27 hours 00 UTC - Valid from 00 UTC hours of 2024-08-27 to 00 UTC hours of 2024-08-30. WRF meteorological model (Weather Research and Forecasting model), ARW core (version 3.2) with spatial resolution at 3km, temporal resolution 60 hours, interval 1 hour.

About this dataset

Context

The dataset presents the median household income across different racial categories in Height of Land township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Height of Land township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 96.70% of the total residents in Height of Land township. Notably, the median household income for White households is $72,031. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $72,031.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Height of Land township.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Height of Land township median household income by race. You can refer the same here

This dataset contains comprehensive annual data from 30 elite female race walkers collected during 2021–2024. The dataset includes anthropometric variables (e.g., body mass, height, fat mass), physiological indicators (e.g., VO₂max, heart rate, lactate threshold, oxygen pulse), and biomechanical measures (e.g., step length, walking speed), as well as neuromuscular performance parameters (e.g., 1RM, power output, RFD).

The dataset is structured across four Excel sheets representing consecutive years. Each sheet includes anonymized rows for each athlete and columns for the assessed variables. These data were used in the study: "Optimizing Race Walking Performance through Advanced Modeling and AI-based Training Analysis."

This resource supports time-series analysis, seasonality modeling, and development of machine learning algorithms in elite sport research.

Horse Racing Market Outlook 2032

The global horse racing market size was USD 431.6 Billion in 2023 and is likely to reach USD 937.5 Billion by 2032, expanding at a CAGR of 9% during 2024–2032. The market is propelled by the growing popularity of sports betting.



Increasing globalization and digital connectivity are broadening horse racing’s appeal and accessibility and are expected to boost the market during the forecast period. The integration of online betting platforms and live streaming services has transformed traditional wagering and viewing experiences, attracting a younger, tech-savvy audience. Additionally, the industry is experiencing a surge in international investments, with stakeholders from around the globe investing in breeding, training, and racing facilities. This influx of capital not only enhances the quality and competitiveness of the sport but also expands its market reach and economic impact.

• In February 2023, Churchill Downs Incorporated (CDI) announced its acquisition of Turfway Park, a thoroughbred racing facility in Florence, Kentucky, for USD 250 million. This acquisition includes the racetrack, the property, and an off-track betting facility, with plans to invest an additional USD 100 million in renovations, including a new grandstand and clubhouse.

Growing interest in themed entertainment and hospitality experiences is further shaping the horse racing sector. Racecourses are increasingly becoming venues for a variety of events, including concerts, family days, and gourmet food festivals, which attract diverse crowds beyond traditional racing enthusiasts. This strategy not only revitalizes race tracks as multi-use destinations but also increases revenue streams through enhanced on-site consumer spending. Moreover, luxury hospitality packages offering fine dining, exclusive viewing areas, and VIP treatment are becoming popular, adding a premium dimension to the race-going experience.



Rising awareness of animal welfare and ethical standards is driving changes in the horse racing industry. There is a growing emphasis on the health and safety of the horses, with stringent regulations and improved veterinary care practices being implemented. These initiatives are crucial for maintaining the integrity of the sport and its public image. Furthermore, sustainable practices in racecourse management and operations are being ad