About this dataset

Context

The dataset tabulates the Normal population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Normal. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 37,762 (71.37% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Normal population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Normal is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Normal is shown in the following column.

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 Normal Population by Age. You can refer the same here

The estimated median age gives an idea of the age distribution of the population in a given area. A greater median age would suggest that the area of interest has a relatively large number of older residents, while a lower median age suggests that the area has a relatively large number of younger residents.

Champaign County’s estimated median age has risen for over a decade, but has always stayed between 28 and 31. Year-to-year changes from 2017 to 2019 were statistically significant, but not from 2019 to 2023. The Champaign County estimated median age has been consistently younger than the estimated median ages of the United States and State of Illinois. Champaign County’s figure is likely impacted to some degree by the large student population associated with the University of Illinois.

The estimated median age does not provide a significant amount of detail, and it does not provide any information on why the estimated median age is what it is. However, when placed in the context of other pieces of data and other indicators, it is a valuable starting point in understanding county demographics.

Estimated median age data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Median Age by Sex.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (8 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (6 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (13 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (7 April 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (7 April 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

This dataset is one of the sources of data visualisations available on the [Liberal Health Professionals] website(https://data.ameli.fr/pages/data-professionnels-sante-liberaux/). ### General information: The liberal health professions available in this dataset are: * the doctors (with more than twenty medical specialties); * dental surgeons** (including dentofacial orthopaedic specialists – ODF); * the women; * medical assistants with five professions: nurses, massage therapists, speech therapists, orthoptists, pedicures-podologists. They are health professionals active on 31 December of the year concerned and: * exercising their activity as a liberal; * in metropolitan France, Guadeloupe, French Guiana, Reunion, Martinique and Mayotte; * having received at least EUR 1 in fees; * whether they are contracted with the Sickness Insurance or not (when they generate a prescription reimbursed by the Sickness Insurance); * professionals in employment-retirement cumulation are counted in the workforce as long as they meet the previous conditions. This dataset presents demographic information about liberal healthcare professionals such as: *average ages: * women; * men; * global; * share of women; * share of men; * share 60 years of age and older; * share of under 60s. This dataset is complementary to the following dataset: Liberal health professionals: number and density by age group, sex and territory (department, region). Only the national level is available for this data. The data are derived from the National Health Data System (NSDS). For more information (source, field, definitions of modalities), visit the Method page of this site. ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2010).

This dataset supports the findings outlined in Regev, J., Zaar, J., Relaño-Iborra, H., and Dau, T. (2025). "Investigating the effects of age and hearing loss on speech intelligibility and amplitude modulation frequency selectivity." The Journal of the Acoustical Society of America, 157(3), 2077-2090. https://doi.org/10.1121/10.0036220The dataset contains data for 10 young and 9 older listeners with normal hearing (young and older NH listeners), as well as 9 older listeners with hearing impairment (older HI listeners).The Readme file provides a description of the dataset's content, and of the related article.The data collected were:Population data: age, tested ear, and reverse digit span scoreAudiogramsSpeech-reception thresholds (SRTs)Average dynamic ranges of masked-threshold patterns (MTPs)TP numbers across datasets (providing the corresponding TP numbers for the present dataset and those of Regev et al., 2024a and Regev et al., 2024b).Population DataDataset giving the population information for the 28 participants (tp), split into young NH, older NH, and older HI (group), tested in the study. The data summarizes the participants' age, tested ear (ear), and their Reverse Digit Span score (rds_score). The age is given in years and the reverse digit span score is given on a normalized scale from 0 to 1.The reverse digit span scores are re-used from Regev et al. (2024a; 2024b).AudiogramAudiometric thresholds (thresh) were collected for 28 participants (tp), split into young NH, older NH, and older HI (group), at frequencies (freq) of 0.125, 0.25, 0.5, 1, 2, 3, 4, 6, and 8 kHz. The thresholds are given in dB Hearing Level (HL).Speech-Reception Thresholds (SRTs)Speech-reception thresholds (SRT) at the 50%-correct point were collected 28 participants (tp), split into young NH, older NH, and older HI (group). The SRTs are given in dB signal-to-noise ratio (SNR). The test used the Danish Hearing in Noise Test (HINT; Nielsen & Dau, 2011).Five different maskers (conditions) were used:a speech-shaped noise (SSN)the ICRA-5 noise (ICRA; Dreschler et al., 2001)a male competing talker (Male comp)a female competing talker (Female comp)a cocktail-party scenario (Cocktail).A detailed description of each masker is available in the article. For each condition, the SRT was assessed twice (repetition), each time using a different list (list) from the target speech corpus. The SRTs were then averaged across repetitions.Dynamic Range of Masked-Threshold Patterns (MTPs)Regev et al. (2024a; 2024b) collected masked-threshold patterns (MTPs) for the 28 participants (tp), split into young NH, older NH, and older HI (group).MTPs were collected at four different target modulation frequencies (fmod) of 4, 16, 64, and 128 Hz.Here, the average dynamic range (dyn_range) of the MTP at the 4-Hz target modulation frequency (fmod) was derived for each participant. For each participant, the peak of the MTP was identified as the maximum threshold. The difference between the peak and the minimum threshold on each side of the peak was then computed, and the average dynamic range was finally calculated as the mean between the differences on both sides. If a single side of the peak was identified, then he threshold difference on that side was taken as the dynamic range.Masked thresholds for TP23 could not be could not be obtained for the 4-Hz target modulation frequency by Regev et al. (2024b; where the participant was labeled TP07). Hence, the dynamic range was registered as NaN.TP numbers across datasetsThe participant in this study previously provided data reported in the datasets by Regev et al. (2024a, 2024b). Some of these data were re-used in this study, either directly (i.e., the RDS scores) or to derive new measures (i.e., the MTPs to derive the dynamic ranges). This sheet provides the correspondence of the TP numbers between this dataset (tp) and those of Regev et al. (2024a, 2024b; tp_Regev_2024a and tp_Regev_2024b, respectively), for each listener group (group). The sheet states NA in case the participant was not included in the previous dataset.Ethical statementAll listeners were financially compensated for their time and gave written informed consent. Ethical approval for the study was provided by the Science-Ethics Committee for the Capital Region of Denmark (reference H-16036391).AcknowledgmentsThe authors thank Borgný Súsonnudóttir Hansen for her contribution to the data collection. The authors also thank Christian Stender Simonsen for kindly sharing the experimental framework used to run the listening test, as well as several of the signal recordings, and Jens Hjortkjær and Jonatan Märcher-Rørsted for kindly providing their implementations of the reverse digit span test and of the Cambridge formula (CamEq).ReferencesDreschler, W. A., Verschuure, H., Ludvigsen, C., & Westermann, S. (2001). ICRA Noises: Artificial Noise Signals with Speech-like Spectral and Temporal Properties for Hearing Instrument Assessment. Audiol, 40(3), 148–157. https://doi.org/10.3109/00206090109073110Nielsen J. B. & Dau T. (2011). The Danish hearing in noise test. Int J Audiol. 50(3):202-8. https://doi.org/10.3109/14992027.2010.524254Regev, J., Zaar, J.; Relaño-Iborra, H., & Dau, T. (2024a). Dataset for: "Age-related reduction of amplitude modulation frequency selectivity". Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.25134527Regev, J., Relaño-Iborra, H., Zaar, J., & Dau, T.(2024b). Dataset for: "Disentangling the effects of hearing loss and age on amplitude modulation frequency selectivity". Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.25134611

The dataset contains a total of 368 scans with 205 healthy elderly volunteers from China. 163 of them were scanned for twice. All volunteers were recruited through methods such as WeChat moments, poster advertising, and word of mouth. Data collection was conducted at the 3T scanner (GE MR 750) of the Magnetic Resonance Research Center at the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol included a set of high-resolution T1-weighted structural images, T2-weighted structural images, resting-state functional images, and task-state functional MRI sequences. Behavioral data included tests such as word list recall, attention cancellation, number-symbol, and Wechsler Block Design. The dataset is divided into three parts: The first part includes 42 elderly volunteers, with 19 females and 23 males; age range 55 to 78 years, average age = 65.07 ± 6.43 years. The second part is a gamified working memory training data set, including 63 participants, among which 57 had a posttest. 43 females and 20 males with age range 60 to 90 years were included in the second part. The third part is a gamified cognitive control training data set, including 100 participants, among which 93 had a posttest. 82 females and 18 males with age range 60 to 90 years were included in the third part.

This database by department provides mainly data on localised social and tax income and personalised autonomy allowance. It contains nearly 100 variables. This database makes it possible to study the standard of living as well as the inequalities of poverty of this population. It will also make it possible to observe the ageing population by counting the number of people using allowances and their amount, in order to allow them to stay in their homes or to help them pay part of the Ephad in which they reside. Data determining the degree of loss of autonomy of these individuals (divided into 6 groups called “GIR”) ranging from a person still independent to one person at the end of life, it will be possible to study and compare the number of these people per department and to see if some departments are more affected by the ageing of the population than others. This database also contains indicators projected at 2050 on the number of people, their average age and their life expectancy per high or low group. Census data will also show the type of housing for people aged 60 and over.

About this dataset

Context

The dataset tabulates the population of Normal by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Normal. The dataset can be utilized to understand the population distribution of Normal by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Normal. 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 Normal.

Key observations

Largest age group (population): Male # 20-24 years (5,464) | Female # 20-24 years (6,317). 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 Normal population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Normal is shown in the following column.
• Population (Female): The female population in the Normal 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 Normal 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 Normal Population by Gender. You can refer the same here

This dataset contains counts of in-hospital births by mother’s age groups (i.e., teen mothers, typical aged mothers and older mothers) based on the mother’s county of residence and year. This dataset does not include all births in California; only those births that occurred in a hospital.

Modified on October 11, 2018

This report estimates the economic value of family caregiving at $450 billion in 2009 based on 42.1 million caregivers age 18 or older providing an average of 18.4 hours of care per week to care recipients age 18 or older, at an average value of $11.16 per hour.

This data is not collected by a government agency. The findings and conclusions in this report are those of the author and do not necessarily represent the views of the Department of Health & Human Services.

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

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138. Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152. Johnson AG, Linde L, Payne AB, et al. Notes from the Field: Comparison of COVID-19 Mortality Rates Among Adults Aged ≥65 Years Who Were Unvaccinated and Those Who Received a Bivalent Booster Dose Within the Preceding 6 Months — 20 U.S. Jurisdictions, September 18, 2022–April 1, 2023. MMWR Morb Mortal Wkly Rep 2023;72:667–669.

Families of tax filers; Census families by age of older partner or parent and number of children (final T1 Family File; T1FF).

Provisional deaths registration data for single year of age and average age of death (median and mean) of persons whose death involved coronavirus (COVID-19), England and Wales. Includes deaths due to COVID-19 and breakdowns by sex.

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

Data Dictionary:

SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict for this challenge. MSSubClass: The building class 20 1-STORY 1946 & NEWER ALL STYLES 30 1-STORY 1945 & OLDER 40 1-STORY W/FINISHED ATTIC ALL AGES 45 1-1/2 STORY - UNFINISHED ALL AGES 50 1-1/2 STORY FINISHED ALL AGES 60 2-STORY 1946 & NEWER 70 2-STORY 1945 & OLDER 75 2-1/2 STORY ALL AGES 80 SPLIT OR MULTI-LEVEL 85 SPLIT FOYER 90 DUPLEX - ALL STYLES AND AGES 120 1-STORY PUD (Planned Unit Development) - 1946 & NEWER 150 1-1/2 STORY PUD - ALL AGES 160 2-STORY PUD - 1946 & NEWER 180 PUD - MULTILEVEL - INCL SPLIT LEV/FOYER 190 2 FAMILY CONVERSION - ALL STYLES AND AGES MSZoning: Identifies the general zoning classification of the sale. A Agriculture C Commercial FV Floating Village Residential I Industrial RH Residential High Density RL Residential Low Density RP Residential Low Density Park RM Residential Medium Density LotFrontage: Linear feet of street connected to property LotArea: Lot size in square feet Street: Type of road access to property Grvl Gravel Pave Paved Alley: Type of alley access to property Grvl Gravel Pave Paved NA No alley access LotShape: General shape of property

Reg Regular IR1 Slightly irregular IR2 Moderately Irregular IR3 Irregular LandContour: Flatness of the property

Lvl Near Flat/Level Bnk Banked - Quick and significant rise from street grade to building HLS Hillside - Significant slope from side to side Low Depression Utilities: Type of utilities available AllPub All public Utilities (E,G,W,& S) NoSewr Electricity, Gas, and Water (Septic Tank) NoSeWa Electricity and Gas Only ELO Electricity only LotConfig: Lot configuration Inside Inside lot Corner Corner lot CulDSac Cul-de-sac FR2 Frontage on 2 sides of property FR3 Frontage on 3 sides of property LandSlope: Slope of property Gtl Gentle slope Mod Moderate Slope Sev Severe Slope Neighborhood: Physical locations within Ames city limits Blmngtn Bloomington Heights Blueste Bluestem BrDale Briardale BrkSide Brookside ClearCr Clear Creek CollgCr College Creek Crawfor Crawford Edwards Edwards Gilbert Gilbert IDOTRR Iowa DOT and Rail Road MeadowV Meadow Village Mitchel Mitchell Names North Ames NoRidge Northridge NPkVill Northpark Villa NridgHt Northridge Heights NWAmes Northwest Ames OldTown Old Town SWISU South & West of Iowa State University Sawyer Sawyer SawyerW Sawyer West Somerst Somerset StoneBr Stone Brook Timber Timberland Veenker Veenker Condition1: Proximity to main road or railroad Artery Adjacent to arterial street Feedr Adjacent to feeder street Norm Normal RRNn Within 200' of North-South Railroad RRAn Adjacent to North-South Railroad PosN Near positive off-site feature--park, greenbelt, etc. PosA Adjacent to postive off-site feature RRNe Within 200' of East-West Railroad RRAe Adjacent to East-West Railroad Condition2: Proximity to main road or railroad (if a second is present) Artery Adjacent to arterial street Feedr Adjacent to feeder street Norm Normal RRNn Within 200' of North-South Railroad RRAn Adjacent to North-South Railroad PosN Near positive off-site feature--park, greenbelt, etc. PosA Adjacent to postive off-site feature RRNe Within 200' of East-West Railroad RRAe Adjacent to East-West Railroad BldgType: Type of dwelling 1Fam Single-family Detached 2FmCon Two-family Conversion; originally built as one-family dwelling Duplx Duplex TwnhsE Townhouse End Unit TwnhsI Townhouse Inside Unit HouseStyle: Style of dwelling 1Story One story 1.5Fin One and one-half story: 2nd level finished 1.5Unf One and one-half story: 2nd level unfinished 2Story Two story 2.5Fin Two and one-half story: 2nd level finished 2.5Unf Two and one-half story: 2nd level unfinished SFoyer Split Foyer SLvl Split Level OverallQual: Overall material and finish quality 10 Very Excellent 9 Excellent 8 Very Good 7 Good 6 Above Average 5 Average 4 Below Average 3 Fair 2 Poor 1 Very Poor OverallCond: Overall condition rating 10 Very Excellent 9 Excellent 8 Very Good 7 Good 6 Above Average 5 Average 4 Below Average 3 Fair 2 Poor 1 Very Poor YearBuilt: Original construction date YearRemodAdd: Remodel date (same as construction date if no remodeling or additions) RoofStyle: Type of roof Flat Flat Gable Gable Gambrel Gabrel (Barn) Hip Hip Mansard Mansard Shed Shed RoofMatl: Roof material ClyTile Clay or Tile CompShg Standard (Composite) Shingle Membran Membrane Metal Metal Roll Roll Tar&Grv Gravel & Tar WdShake Wood Shakes WdShngl Wood Shingles Exterior1st: Exterior covering on house AsbShng Asbestos Shingles AsphShn Asphalt Shingles BrkComm Brick Common BrkFace Brick Face CBlock Cinder Block CemntBd Cement Board HdBoard Hard Board ImStucc Imitation Stucco MetalSd Metal Siding Other Other Plywood Plywood PreCast PreCast Stone Stone Stucco Stucco VinylSd Vinyl Siding Wd Sdng Wood Siding WdShing Wood...

Indicators included have been derived from the published 2019 mid-year population estimates for the UK, England, Wales, Scotland and Northern Ireland. These are the number of persons and percentage of the population aged 65 years and over, 85 years and over, 0 to 15 years, 16 to 64 years, 16 years to State Pension age, State Pension age and over, median age and the Old Age Dependency Ratio (the number of people of State Pension age per 1000 of those aged 16 years to below State Pension age).

This dataset has been produced by the Ageing Analysis Team for inclusion in a subnational ageing tool, which was published in July 2020. The tool enables users to compare latest and projected measures of ageing for up to four different areas through selection on a map or from a drop-down menu.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.

The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.

The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns.

The dataset contains anonymized transcripts, annotation scores, and self-reported user perceptions. Link: https://zenodo.org/records/12588810

Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.

PurposeTo quantify the global impact of vision impairment in individuals aged 65 years and older between 1990 and 2019, segmented by disease, age, and sociodemographic index (SDI).MethodsUsing the Global Burden of Diseases 2019 (GBD 2019) dataset, a retrospective demographic evaluation was undertaken to ascertain the magnitude of vision loss over this period. Metrics evaluated included case numbers, prevalence rates per 100,000 individuals, and shifts in prevalence rates via average annual percentage changes (AAPCs) and years lived with disability (YLDs).ResultsFrom 1990 to 2019, vision impairment rates for individuals aged 65 years and older increased from 40,027.0 (95% UI: 32,232.9-49,945.1) to 40,965.8 (95% UI: 32,911-51,358.3, AAPC: 0.11). YLDs associated with vision loss saw a significant decrease, moving from 1713.5 (95% UI: 1216.2–2339.7) to 1579.1 (95% UI: 1108.3–2168.9, AAPC: −0.12). Gender-based evaluation showed males had lower global prevalence and YLD rates compared to females. Cataracts and near vision impairment were the major factors, raising prevalence by 6.95 and 2.11%, respectively. Cataract prevalence in high-middle SDI regions and near vision deficits in high SDI regions significantly influenced YLDs variation between 1990 and 2019.ConclusionOver the past three decades, there has been a significant decrease in the vision impairment burden in individuals aged 65 and older worldwide. However, disparities continue, based on disease type, regional SDI, and age brackets. Enhancing eye care services, both in scope and quality, is crucial for reducing the global vision impairment burden among the older adults.