United States US: Income Share Held by Highest 20% data was reported at 46.900 % in 2016. This records an increase from the previous number of 46.400 % for 2013. United States US: Income Share Held by Highest 20% data is updated yearly, averaging 46.000 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 46.900 % in 2016 and a record low of 41.200 % in 1979. United States US: Income Share Held by Highest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

United States US: Income Share Held by Highest 10% data was reported at 30.600 % in 2016. This records an increase from the previous number of 30.100 % for 2013. United States US: Income Share Held by Highest 10% data is updated yearly, averaging 30.100 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 30.600 % in 2016 and a record low of 25.300 % in 1979. United States US: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Deptford Township, New Jersey, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 21,507, while the mean income for the highest quintile (20% of households with the highest income) is 257,345. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 384,780, which is 149.52% higher compared to the highest quintile, and 1789.09% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Deptford township median household income. You can refer the same here

United States US: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data was reported at 1.200 % in 2016. This records an increase from the previous number of 1.000 % for 2013. United States US: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data is updated yearly, averaging 0.700 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 1.200 % in 2016 and a record low of 0.500 % in 1991. United States US: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

United States US: Income Share Held by Lowest 20% data was reported at 5.000 % in 2016. This records a decrease from the previous number of 5.100 % for 2013. United States US: Income Share Held by Lowest 20% data is updated yearly, averaging 5.300 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 6.400 % in 1979 and a record low of 5.000 % in 2016. United States US: Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

United States US: Income Share Held by Lowest 10% data was reported at 1.700 % in 2016. This stayed constant from the previous number of 1.700 % for 2013. United States US: Income Share Held by Lowest 10% data is updated yearly, averaging 1.800 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 2.300 % in 1979 and a record low of 1.700 % in 2016. United States US: Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

United States US: Income Share Held by Third 20% data was reported at 15.300 % in 2016. This records a decrease from the previous number of 15.400 % for 2013. United States US: Income Share Held by Third 20% data is updated yearly, averaging 15.700 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 16.900 % in 1979 and a record low of 15.300 % in 2016. United States US: Income Share Held by Third 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

United States US: Income Share Held by Second 20% data was reported at 10.200 % in 2016. This records a decrease from the previous number of 10.300 % for 2013. United States US: Income Share Held by Second 20% data is updated yearly, averaging 10.600 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 12.000 % in 1979 and a record low of 10.200 % in 2016. United States US: Income Share Held by Second 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

When inflation occurs in a country, the value of the currency decreases. That means that the purchasing power consumers have with a fixed amount of money decreases. Wages, especially lower and middle class wages, usually increase at a MUCH slower rate than prices of consumer goods; so consumers are likely to make the same wage, but are not able to buy the same amount of goods and services. Consumers in countries with hyperinflation suffer greatly because of this economic phenomenon.

Data was downloaded from: Link

For notes/metadata regarding the definition, measurement, or data collection for a certain country or group can be found by downloading the excel file from the linked webpage.

Original data provider: International Monetary Fund, World Development Indicators. License : CC BY-4.0.

INDICATOR_CODE: FP.CPI.TOTL.ZG

INDICATOR_NAME: Inflation, consumer prices (annual %)

SOURCE_NOTE: Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly.
The Laspeyres formula is generally used.

Years included: 1960-2016

The following countries have no values for any year:

• Somalia

• Puerto Rico

• Guam

• US Virgin Islands

The dataset also conains some records that refer to groups of countries, which may be useful for those with no recorded values. Some of those groups are:

• Fragile and conflict affected situations

• Heavily indebted poor countries (HIPC)

• Caribbean small states

• Latin America & Caribbean (excluding high income)

• Latin America & the Caribbean (IDA & IBRD countries)

• East Asia & Pacific (excluding high income)

• East Asia & Pacific (IDA & IBRD countries)

• Least developed countries: UN classification

• Middle East & North Africa (IDA & IBRD countries)

If this data is being used for the Kiva Crowdfunding Data Science for Good event; The following countries (as they are named in this dataset), are named slightly differently in the Kiva dataset (to the best of my knowledge). For example, West Bank in Gaza is referred to as Palestine in the Kiva Dataset.

• Congo, Dem. Rep.

• Congo, Rep.

• Kyrgyz Republic

• Lao PDR

• Myanmar

• West Bank and Gaza

• St. Vincent and the Grenadines

• Virgin Islands (U.S.)

• Yemen, Rep.

United States US: Income Share Held by Fourth 20% data was reported at 22.600 % in 2016. This records a decrease from the previous number of 22.700 % for 2013. United States US: Income Share Held by Fourth 20% data is updated yearly, averaging 22.700 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 23.600 % in 1986 and a record low of 22.300 % in 2000. United States US: Income Share Held by Fourth 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

A Gold Standard Corpus for Activity Information

Dataset Title: A Gold Standard Corpus for Activity Information (GoSCAI)

Dataset Curators: The Epidemiology & Biostatistics Section of the NIH Clinical Center Rehabilitation Medicine Department

Dataset Version: 1.0 (May 16, 2025)

Dataset Citation and DOI: NIH CC RMD Epidemiology & Biostatistics Section. (2025). A Gold Standard Corpus for Activity Information (GoSCAI) [Data set]. Zenodo. doi: 10.5281/zenodo.15528545

EXECUTIVE SUMMARY

This data statement is for a gold standard corpus of de-identified clinical notes that have been annotated for human functioning information based on the framework of the WHO's International Classification of Functioning, Disability and Health (ICF). The corpus includes 484 notes from a single institution within the United States written in English in a clinical setting. This dataset was curated for the purpose of training natural language processing models to automatically identify, extract, and classify information on human functioning at the whole-person, or activity, level.

CURATION RATIONALE

This dataset is curated to be a publicly available resource for the development and evaluation of methods for the automatic extraction and classification of activity-level functioning information as defined in the ICF. The goals of data curation are to 1) create a corpus of a size that can be manually deidentified and annotated, 2) maximize the density and diversity of functioning information of interest, and 3) allow public dissemination of the data.

LANGUAGE VARIETIES

Language Region: en-US

Prose Description: English as written by native and bilingual English speakers in a clinical setting

LANGUAGE USER DEMOGRAPHIC

The language users represented in this dataset are medical and clinical professionals who work in a research hospital setting. These individuals hold professional degrees corresponding to their respective specialties. Specific demographic characteristics of the language users such as age, gender, or race/ethnicity were not collected.

ANNOTATOR DEMOGRAPHIC

The annotator group consisted of five people, 33 to 76 years old, including four females and one male. Socioeconomically, they came from the middle and upper-middle income classes. Regarding first language, three annotators had English as their first language, one had Chinese, and one had Spanish. Proficiency in English, the language of the data being annotated, was native for three of the annotators and bilingual for the other two. The annotation team included clinical rehabilitation domain experts with backgrounds in occupational therapy, physical therapy, and individuals with public health and data science expertise. Prior to annotation, all annotators were trained on the specific annotation process using established guidelines for the given domain, and annotators were required to achieve a specified proficiency level prior to annotating notes in this corpus.

LINGUISTIC SITUATION AND TEXT CHARACTERISTICS

The notes in the dataset were written as part of clinical care within a U.S. research hospital between May 2008 and November 2019. These notes were written by health professionals asynchronously following the patient encounter to document the interaction and support continuity of care. The intended audience of these notes were clinicians involved in the patients' care. The included notes come from nine disciplines - neuropsychology, occupational therapy, physical medicine (physiatry), physical therapy, psychiatry, recreational therapy, social work, speech language pathology, and vocational rehabilitation. The notes were curated to support research on natural language processing for functioning information between 2018 and 2024.

PREPROCESSING AND DATA FORMATTING

The final corpus was derived from a set of clinical notes extracted from the hospital electronic medical record (EMR) for the purpose of clinical research. The original data include character-based digital content originally. We work in ASCII 8 or UNICODE encoding, and therefore part of our pre-processing includes running encoding detection and transformation from encodings such as Windows-1252 or ISO-8859 format to our preferred format.

On the larger corpus, we applied sampling to match our curation rationale. Given the resource constraints of manual annotation, we set out to create a dataset of 500 clinical notes, which would exclude notes over 10,000 characters in length.

To promote density and diversity, we used five note characteristics as sampling criteria. We used the text length as expressed in number of characters. Next, we considered the discipline group as derived from note type metadata and describes which discipline a note originated from: occupational and vocational therapy (OT/VOC), physical therapy (PT), recreation therapy (RT), speech and language pathology (SLP), social work (SW), or miscellaneous (MISC, including psychiatry, neurology and physiatry). These disciplines were selected for collecting the larger corpus because their notes are likely to include functioning information. Existing information extraction tools were used to obtain annotation counts in four areas of functioning and provided a note’s annotation count, annotation density (annotation count divided by text length), and domain count (number of domains with at least 1 annotation).

We used stratified sampling across the 6 discipline groups to ensure discipline diversity in the corpus. Because of low availability, 50 notes were sampled from SLP with relaxed criteria, and 90 notes each from the 5 other discipline groups with stricter criteria. Sampled SLP notes were those with the highest annotation density that had an annotation count of at least 5 and a domain count of at least 2. Other notes were sampled by highest annotation count and lowest text length, with a minimum annotation count of 15 and minimum domain count of 3.

The notes in the resulting sample included certain types of PHI and PII. To prepare for public dissemination, all sensitive or potentially identifying information was manually annotated in the notes and replaced with substituted content to ensure readability and enough context needed for machine learning without exposing any sensitive information. This de-identification effort was manually reviewed to ensure no PII or PHI exposure and correct any resulting readability issues. Notes about pediatric patients were excluded. No intent was made to sample multiple notes from the same patient. No metadata is provided to group notes other than by note type, discipline, or discipline group. The dataset is not organized beyond the provided metadata, but publications about models trained on this dataset should include information on the train/test splits used.

All notes were sentence-segmented and tokenized using the spaCy en_core_web_lg model with additional rules for sentence segmentation customized to the dataset. Notes are stored in an XML format readable by the GATE annotation software (https://gate.ac.uk/family/developer.html), which stores annotations separately in annotation sets.

CAPTURE QUALITY

As the clinical notes were extracted directly from the EMR in text format, the capture quality was determined to be high. The clinical notes did not have to be converted from other data formats, which means this dataset is free from noise introduced by conversion processes such as optical character recognition.

LIMITATIONS

Because of the effort required to manually deidentify and annotate notes, this corpus is limited in terms of size and representation. The curation decisions skewed note selection towards specific disciplines and note types to increase the likelihood of encountering information on functioning. Some subtypes of functioning occur infrequently in the data, or not at all. The deidentification of notes was done in a manner to preserve natural language as it would occur in the notes, but some information is lost, e.g. on rare diseases.

METADATA

Information on the manual annotation process is provided in the annotation guidelines for each of the four domains:

- Communication & Cognition (https://zenodo.org/records/13910167)

- Mobility (https://zenodo.org/records/11074838)

- Self-Care & Domestic Life (SCDL) (https://zenodo.org/records/11210183)

- Interpersonal Interactions & Relationships (IPIR) (https://zenodo.org/records/13774684)

Inter-annotator agreement was established on development datasets described in the annotation guidelines prior to the annotation of this gold standard corpus.

The gold standard corpus consists of 484 documents, which include 35,147 sentences in total. The distribution of annotated information is provided in the table below.

United States US: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 2.000 % in 2016. This stayed constant from the previous number of 2.000 % for 2013. United States US: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 1.500 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 2.000 % in 2016 and a record low of 1.200 % in 1986. United States US: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

United States US: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 1.500 % in 2016. This records an increase from the previous number of 1.200 % for 2013. United States US: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 1.000 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 1.500 % in 2016 and a record low of 0.700 % in 1991. United States US: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.000 % in 2012. This records a decrease from the previous number of 7.800 % for 2009. US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 7.000 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 8.100 % in 2005 and a record low of 5.400 % in 1991. US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

A dataset listing the richest zip codes in North Carolina per the most current US Census data, including information on rank and average income.