About this dataset

Context

The dataset tabulates the Excel household income by gender. The dataset can be utilized to understand the gender-based income distribution of Excel income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Excel, AL annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Excel, AL annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Excel income distribution by gender. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Excel. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Excel, the median household income for the households where the householder is White increased by $16,305(31.81%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $51,252 in 2011 and $67,557 in 2021.
• Black or African American: In Excel, the median household income for Black or African American households was $42,823 in 2011(2022 inflation-adjusted dollars). However there is no reported data for 2021, indicating a lack of information for this specific year.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/excel-al-median-household-income-by-race-trends.jpeg" alt="Excel, AL median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Excel.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-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 Excel median household income by race. 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 Excel. The dataset can be utilized to gain insights into gender-based income distribution within the Excel population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Excel, among individuals aged 15 years and older with income, there were 153 men and 160 women in the workforce. Among them, 129 men were engaged in full-time, year-round employment, while 74 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 1.55% fell within the income range of under $24,999, while 24.32% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 10.85% of men in full-time roles earned incomes exceeding $100,000, while none 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)

https://i.neilsberg.com/ch/excel-al-income-distribution-by-gender-and-employment-type.jpeg" alt="Excel, AL gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Excel median household income by gender. You can refer the same here

This is the Mean Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) data taken from the Excel dataset "1999, Ivotuk Mean NDVI and LAI Data (EXCEL) (Epstein)" and translated into Tabular ASCII.

This is the Seasonal Mean Phytomass Data taken from the Excel file of dataset "ATLAS: 1999, Ivotuk NDVI, LAI, and Phytomass Data (EXCEL) (Epstein)" and translated into Tabular ASCII.

Our analyses are based on 148×148 time- and frequency-domain correlation matrices. A correlation matrix covers all the possible use cases of every activity metric listed in the article. With these activity metrics and different preprocessing methods, we were able to calculate 148 different activity signals from multiple datasets of a single measurement. Each cell of a correlation matrix contains the mean and standard deviation of the calculated Pearson’s correlation coefficients between two types of activity signals based on 42 different subjects’ 10-days-long motion. The small correlation matrices presented both in the article and in the appendixes are derived from these 148 × 148 correlation matrices. This published Excel workbook contains multiple sheets labelled according to their content. The mean and standard deviation values for both time- and frequency-domain correlations can be found on their own separate sheet. Moreover, we reproduced the correlation matrix with an alternatively parametrized digital filter, which doubled the number of sheets to 8. In the Excel workbook, we used the same notation for both the datasets and activity metrics as presented in this article with an extension to the PIM metric: PIMs denotes the PIM metric where we used Simpson’s 3/8 rule integration method, PIMr indicates the PIM metric where we calculated the integral by simple numerical integration (Riemann sum). (XLSX)

This dataset contains Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Phytomass data collected at the Ivotuk field site during the growing season of 1999. The worksheets within this Excel file contain Mean NDVI and LAI data, raw NDVI and LAI data, seasonal mean phytomass, peak phytomass data and raw phytomass data separated by sampling period.

In this study, blood proteome characterization in face transplantation using longitudinal serum samples from six face transplant patients was carried out with SOMAscan platform. Overall, 24 serum samples from 13 no-rejection, 5 nonsevere rejection and 6 severe rejection episodes were analyzed.Files attached:HMS-16-007.20160218.adat - raw SomaScan dataset presented in adat format.HMS-16-007_SQS_20160218.pdf - technical validation report on the dataset.HMS-16-007.HybNorm.20160218.adat - SomaScan dataset after hybridization control normalization presented in adat format.HMS-16-007.HybNorm.MedNorm.20160218.adat - SomaScan dataset after hybridization control normalization and median signal normalization presented in adat format.HMS-16-007.HybNorm.MedNorm.Cal.20160218.adat - SomaScan dataset after hybridization control normalization, median signal normalization, and calibration presented in adat format.HMS-16-007.HybNorm.MedNorm.Cal.20160218.xls - SomaScan dataset after hybridization control normalization, median signal normalization, and calibration presented in Microsoft Excel Spreadsheet format.Patients_metadata.txt – metadata file containing patients’ demographic and clinical information presented in tab-delimited text format. Metadata is linked to records in the SomaScan dataset via ‘SampleType’ column.SciData_R_script.R – this script is given as an example of a downstream statistical analysis of the HMS-16-007.HybNorm.MedNorm.Cal.20160218.adat dataset.SciData_R_script_SessionInfo - Session information for SciData_R_script.R script.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

This excel contains data for Chapter 1 “Temperature” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual average water temperature at Woods Hole, MA 1880-2015,” (page 51); Figure 2, “Annual mean air temperatures at Worcester, MA 1949-2015,” (page 54); Figure 3, “Annual mean air temperature at Warwick, RI 1895-2015,” (page 54); Figure 4, "Annual mean surface water temperatures in Narragansett Bay 1960-2010," (page 55); Figure 5, "Annual mean river/stream water temperatures in Scituate Reservoir," (page 55); Figure 6, "Annual mean river/stream water temperatures at Millville, MA," (page 56); Figure 7, "Annual mean river/stream water temperatures from 2007-2014," (page 56); and Figure 8 "Seasonal air temperature projections for RI from 1950-2100," (page 58). For more information, please reference the Technical Report or contact info@nbep.org. Original figures are available at http://nbep.org/the-state-of-our-watershed/figures/.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Data from a small meteorological station set-up near 21 plots in 2013. Campbell Scientific CR10 datalogger, Campbell 215 temp/humidity sensor, two Apogee PAR sensors (one facing up, another facing down), soil temperature with type T thermocouple, Campbell CS616 soil reflectometer for soil water content. Data collected between DOY153 and DOY224. Logger collected a measurement every 60 seconds and averaged to 5 min data table. Post-processing to 60 min averages and daily mean, max, and min. MS Excel (.xls) workbook with three worksheets. Worksheet 5_min data columns: year, day of year, hour, minute, fractional day of year, incoming PAR (umol m-2 s-1), reflected PAR (umol m-2 s-1), albedo calculated as (par_out/par_in)*100, air temperature (C), relative humidity (%), soil temp (C), raw reflectance time reported by CS616, calculated volumetric water content corrected for soil temperature (v/v), battery voltage. Worksheet 60_min data columns (units as above): day of year, hour, fractional day of year, week of year, air temperature, relative humidity, incoming PAR, outgoing PAR, albedo, soil temperature, and volumetric water content. Worksheet daily (units as above unless indicated): date, day of year, air temperature min, air temperature max, air temperature mean, relative humidity min, relative humidity max, relative humidity mean, soil temperature mean, soil water content mean, total incoming PAR (mol m-2 d-1), out going PAR (mol m-2 d-1), albedo, minimum battery voltage. missing values are -6999 or 6999. Soil temperature and VWC not valid until instruments could be installed in the soil DOY 163. RH sensor failed DOY177, did not function again. Battery issue DOY 183.

BackgroundThe goal of a pediatric intensive care unit is to treat life-threatening illnesses. Yet, there is a lack of data on survival rates and factors influencing survival in pediatric intensive care units (PICUs) in low-income countries like Ethiopia.ObjectiveThe purpose of this study was to evaluate survival and its predictors in the pediatric intensive care unit of Ayder Comprehensive Specialized Hospital, Tigray, Ethiopia.MethodA retrospective cohort study was implemented on a total of 223 patients admitted to the PICU from September 2019 to August 2020. Using a checklist, trained healthcare workers gathered secondary data from patient charts. The dependent variable was time-to-death. EpiData 4.6 and STATA 16 were used for data entry and data analysis, respectively. Descriptive statistics, cumulative incidence, incidence density, median survival time, and adjusted hazard ratio were calculated to describe variables, estimate mortality rate and risk, and identify factors associated with survival. P