The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

The Wave III public-use data are helpful in analyzing the transition between adolescence and young adulthood. Included in this dataset are data on pregnancy.

ShimFall&ADL dataset

Version 1.0 (2020-06-19)

Please cite as: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"

Disclaimer While every care has been taken to ensure the accuracy of the data included in the ShimFall&ADL dataset, the authors and the University of the West of Scotland do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.

Contact For inquiries regarding the ShimFall&ADL dataset, please contact: Dr Stamos Katsigiannis, Stamos.Katsigiannis@uws.ac.uk, University of the West of Scotland Prof. Naeem Ramzan, Naeem.Ramzan@uws.ac.uk, University of the West of Scotland

Acknowledgment

The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.

Dataset summary The ShimFall&ADL dataset contains recordings from 35 individuals, acquired using a chest-strapped Shimmer v2 tri-axial accelerometer, recording at a 50Hz sampling rate. Experiments were conducted in a controlled environment at a research lab in the University of the West of Scotland. Thirty five (35) healthy individuals were recruited among young or mid-aged volunteers, aged between 19 and 34 years old, having a body weight between 52 and 113 kg, and a body height between 1.45 and 1.82 m.

Participants performed the following activities of daily living (ADL): Jumping Lying down Bending/picking up Sitting to a chair Standing up from a chair Walking

Participants performed the following falls: Steep (hard) Front (soft) Front (hard) Left (soft) Left (hard) Right (soft) Right (hard) Back (soft) Back (hard)

Data Each ".dat" file in the dataset corresponds to one event for one individual and contains 101 accelerometer samples corresponding to the event. Each row of the file corresponds to one 3-channel sample, dividing the x, y, z axes values using the "\t" character, as follows: Row 1: x1\ty1\tz1 Row 2: x2\ty2\tz2 ... Row N: xN\tyN\tzN

The files within the dataset are named as follows: adl_.dat fall_.dat

For example, the file "adl_standingfromchair_18.dat" corresponds to the accelerometer recording of the 18th participant, performing the "standing up from chair" ADL. The file, "leftfall_soft_11.dat" corresponds to the accelerometer recording of the 11th participant, performing a soft left fall.

Additional information For additional information regarding the creation of the ShimFall&ADL dataset, please refer to the associated publication: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Context

There's a story behind every dataset and here's your opportunity to share yours.

This dataset collects characteristics of the population in each region (age distribution, unemployment rate, immigration percent and primary economic sector) and cross it with the votes per each political part.

It has 52 fields:

1) Code [String]: Region code of the different Spanish areas. There are 8126 different regions, but the dataset only contains 8119, because some sources were incomplete.

2) RegionName [String]: Name of the region.

3) Population [Int]: Amount of people living in that area (1st January 2015)

4) TotalCensus [Int]: Number of people over 18 years old, which means that can vote.

5) TotalVotes [Int]: Number of total votes.

6) AbstentionPtge [Float]: Percent of the people that have not votes in the election. (TotalCensus-TotalVotes)/TotalCensus*100 %

7) BlankVotesPtge [Float]: Percent of votes that were blank. Calculated as follows: BlankVotes/TotalVotes*100 %

8) NullVotesPtge [Float]: Percent of votes that were null. Calculated as follows: NullVotes/TotalVotes*100 %

9) PP_Ptge [Float]: Percent of the votes given to the political party called “Partido Popular”. (PP_Votes)/TotalVotes*100 %

10) PSOE_Ptge [Float]: Percent of the votes given to the political party called “Partido Socialista Obrero Español” (PSOE_Votes)/TotalVotes*100 %

11) Podemos_Ptge [Float]: Percent of the votes given to the political party called “Podemos” (Podemos_Votes)/TotalVotes*100 %

12) Ciudadanos_Ptge [Float]: Percent of the votes given to the political party called “Ciudadanos” (Ciudadanos_Votes)/TotalVotes*100 %

13) Others_Ptge [Float]: Percent of the votes given to the others political parties (∑▒MinoritaryVotes)/TotalVotes*100 %

14) Age_0-4_Ptge [Float]: Percent of the populations which age is between 0 and 4 years old. It is calculated as follows: (Number of people in (0-4))/TotalPopulation*100 %

15) Age_5-9_Ptge [Float]: Percent of the populations which age is between 5 and 9 year old.

16) Age_10-14_Ptge [Float]: Percent of the populations which age is between 10 and 14 years old

17) Age_15-19_Ptge [Float]: Percent of the populations which age is between 15 and 19 years old

18) Age_20-24_Ptge [Float]: Percent of the populations which age is between 20 and 24 years old

19) Age_25-29_Ptge [Float]: Percent of the populations which age is between 25 and 29 years old

20) Age_30-34_Ptge [Float]: Percent of the populations which age is between 30 and 34 years old

21) Age_35-39_Ptge [Float]: Percent of the populations which age is between 35 and 39 years old

22) Age_40-44_Ptge [Float]: Percent of the populations which age is between 40 and 44 years old

23) Age_45-49_Ptge [Float]: Percent of the populations which age is between 45 and 49 years old

24) Age_50-54_Ptge [Float]: Percent of the populations which age is between 50 and 54 years old

25) Age_55-59_Ptge [Float]: Percent of the populations which age is between 55 and 59 years old

26) Age_60-64_Ptge [Float]: Percent of the populations which age is between 60 and 64 years old

27) Age_65-69_Ptge [Float]: Percent of the populations which age is between 65 and 69 years old

28) Age_70-74_Ptge [Float]: Percent of the populations which age is between 70 and 74 years old

29) Age_75-79_Ptge [Float]: Percent of the populations which age is between 75 and 79 year old

30) Age_80-84_Ptge [Float]: Percent of the populations which age is between 80 and 84 years old

31) Age_85-89_Ptge [Float]: Percent of the populations which age is between 85 and 89 year old

32) Age_90-94_Ptge [Float]: Percent of the populations which age is between 90 and 94 years old

33) Age_95-99_Ptge [Float]: Percent of the populations which age is between 95 and 99 years old

34) Age_100+_Ptge [Float]: Percent of the populations which is older than 100 years old.

35) ManPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: ManPopulation/TotalPopulation*100

36) WomanPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: WomanPopulation/TotalPopulation*100

37) SpanishPtge [Float]: Percentage of people with spanish nationality in a region. Calculated as follows: NativeSpanishPopulation/TotalPopulation*100

38) ForeignersPtge [Float]: Percentage of foreign people in a region. Calculated as follows: ForeignPopulation/TotalPopulation*100

39) SameComAutonPtge [Float]: Percentage of people who live in the same autonomic community (same province) that was born. Calculated as follows: SameComAutonPopulation/TotalPopulation*100

40) SameComAutonDiffProvPtge [Float]: Percentage of people who live in the same autonomic community (different province) that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

41) DifComAutonPtge [Float]: Percentage of people who live in different autonomic community that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

42) UnemployLess25_Ptge [Float]: Percent of unemployed people that are under 25 years and older than 18. It is calculated over the total amount of unemployment. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalUnemployment*100

43) Unemploy25_40_Ptge [Float]: Percent of unemployed people that are 25-40 years over the total amount of unemployment. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalUnemployment*100

44) UnemployMore40_Ptge [Float]: Percent of unemployed people that are older that 40 and younger than 69 years over the total amount of unemployment. (Unemployment(40-69)_Man+Unemployment(40-69)_Woman)/TotalUnemployment*100

45) UnemployLess25_population_Ptge [Float]: Percent of unemployed people younger than 25 and older than 18, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

46) Unemploy25_40_population_Ptge [Float]: Percent of unemployed people (25-40) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalPopulation*100

47) UnemployMore40_population_Ptge [Float]: Percent of unemployed people (40-69) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

48) AgricultureUnemploymentPtge [Float]: Percent of unemployment in the agriculture sector relative to the total amount of unemployment. PeopleUnemployedInAgriculture/TotalUnemployment*100

49) IndustryUnemploymentPtge [Float]: Percent of unemployment in the industry sector relative to the total amount of unemployment. PeopleUnemployedInIndustry/TotalUnemployment*100

50) ConstructionUnemploymentPtge [Float]: Percent of unemployment in the construction sector relative to the total amount of unemployment. PeopleUnemployedInConstruction/TotalUnemployment*100

51) ServicesUnemploymentPtge [Float]: Percent of unemployment in the services sector relative to the total amount of unemployment. PeopleUnemployedInServices/TotalUnemployment*100

52) NotJobBeforeUnemploymentPtge [Float]: Percent of unemployment of people that didn’t have an employ before, over the total amount of unemployment. PeopleUnemployedWithoutEmployBefore/TotalUnemployment*100

References:

[1] Unemployment: www.datos.gob.es/es/catalogo/e00142804-paro-registrado-por-municipios

[2] Age distribution per region Relation between Spanish and foreigners Relation between woman and man Relation between people born in the same area or different areas of Spain http://www.ine.es/dynt3/inebase/index.htm?type=pcaxis&file=pcaxis&path=%2Ft20%2Fe245%2Fp05%2F%2Fa2015

[3] Congress elections result of Spanish election (June 2016) http://www.infoelectoral.interior.es/min/areaDescarga.html?method=inicio

Overview

In the field of neurobiology of language, existing research predominantly focuses on data from a limited number of Indo-European languages and primarily involves younger adults, overlooking other age groups. This experiment aims to address these gaps by creating a comprehensive multimodal database. The primary goal is to advance our understanding of language processing in older adults and the impact of healthy aging on brain-behavior relationships.

The experiment involves collecting task-based and resting-state fMRI, structural MRI, and EEG data from 52 healthy right-handed older Cantonese participants over 65 years old as they listen to excerpts from “The Little Prince” in Cantonese. Additionally, the database includes detailed information on participants’ language history, lifetime experiences, linguistic and cognitive skills, as well as extensive audio and text annotations, such as time-aligned speech segmentation and prosodic features, along with word-by-word predictors from natural language processing (NLP) tools. Quality diagnostics of the MRI and EEG data confirm their robustness, positioning this database as a valuable resource for studying the spatiotemporal dynamics of language comprehension in older adults.

Methods

Participants

We recruited 52 healthy, right-handed older Cantonese participants (40 females, mean age=69.12, SD=3.52) from Hong Kong for the experiment, which consists of an fMRI and an EEG session. In both sessions, participants listened to the same sections of The Little Prince in Cantonese for approximately 20 minutes. We made sure each participant was right-handed and a native Cantonese speaker using the Language History Questionnaire8 (LHQ3). Additionally, participants reported normal or corrected normal hearing. They confirmed they had no cognitive decline. Two participants did not take part in the fMRI session and an additional 4 participants’ fMRI data were removed due to excessive head movement, resulting in a total of 46 participants (39 females, mean age=69.08yrs, SD=3.58) for the fMRI session and 52 participants (40 females, mean age=69.12yrs, SD=3.52) for the EEG session. Prior to the experiment, all participants were provided with written informed consent. All participants received monetary compensation after each session. Ethical approval was obtained from the Human Subjects Ethics Application Committee at the Hong Kong Polytechnic University (application number HSEARS20210302001). This study was performed in accordance with the Declaration of Helsinki and all other regulations set by the Ethics Committee.

Experiment Procedures

The study consisted of an fMRI session and an EEG session. The order of the EEG and fMRI sessions was counterbalanced across all participants, and a minimum two-week interval was maintained between sessions.

fMRI experiment

Before the scanning day, an MRI safety screening form was sent to the participants to make sure MRI scanning was safe for them. We also sent them simple readings and videos about MRI scanning so that they could have an idea of what it would be like to be in a scanner. On the day of scanning, participants were initially introduced to the MRI facility and comfortably positioned inside the scanner, with their heads securely supported using paddings. An MRI-safe headphone (Sinorad package) was provided for participants to wear inside the head coil. The audio volume for the listening task was adjusted to ensure audibility for each participant. A mirror attached to the head coil allowed participants to view the stimuli presented on a screen. Participants were instructed to stay focused on the visual fixation sign while listening to the audiobook. The scanning session commenced with the acquisition of structural (T1-weighted) scans. Subsequently, participants engaged in the listening task concurrently with fMRI scanning. The task-based fMRI experiment was divided into four runs, each corresponding to a section of the audiobook. Comprehension was assessed by a series of 5 yes/no questions (20 questions in total) on the content they had listened to. These questions were presented on the screen, with participants indicating their answers by pressing a button. The session concluded with the collection of resting-state fMRI data.

Cognitive tasks

Four cognitive tasks were selected to assess participants’ cognitive abilities in various domains, including the forward digit span task, picture naming task, verbal fluency task, and Flanker task. These tasks were delivered after the fMRI session in a separate soundproof booth.

EEG experiment

During the EEG experiment, participants were seated comfortably in a quiet room and standard procedures were followed for electrode placement and EEG cap preparation. Participants were instructed to focus on a fixation sign displayed on a monitor. The EEG recording was then initiated, with participants listening to the audiobook. The audio volume was adapted to each participant’s hearing ability before the recording using a different set of stimuli. We used Foam Ear Inserts (Medium 14mm). Similar to the fMRI experiment, participants listened to four sections of the audiobook, each lasting approximately 5 minutes. After each run, participants were asked to answer a total of 20 yes/no questions, with 5 questions assigned to each run. They indicated their answers by pressing a button. The EEG recording was conducted continuously throughout all four runs until their completion.

Questionnaires.

We administered LHQ3 and the Lifetime of Experiences Questionnaire (LEQ) during EEG cap preparation. The participants did not need to move or fill in these questionnaires themselves; a research assistant asked the questions one by one in Cantonese and input the responses in an online Google form. LHQ is designed to document language history by producing aggregate scores for language proficiency, exposure, and dominance in all the languages spoken by the participants. LEQ is a tool to document what sorts of activities (e.g. sports, music, education, profession, etc) participants engage in over their lifetime. It measures lifetime experiences in three periods of life: from 13 to 30 (young adulthood), from 30 to 65 (midlife), and after 65 (late life). LEQ produces a total score (see participants.tsv) which is an indication of cognitive activity. Collecting data using these two questionnaires allowed us to have a thicker description of our participants’ linguistic, social, and cognitive experiences.

Acquisition

The MRI data were collected at the University Research Facility in Behavioral and Systems Neuroscience (UBSN) at The Hong Kong Polytechnic University. EEG data was collected at the Speech and Language Sciences Laboratory within the Department of Chinese and Bilingual Studies at the same university. Data acquisition for this project started in July 2021 and ended in December 2022.

fMRI data.

MRI imaging data were acquired using a 3T Siemens MAGNETOM Prisma system MRI scanner with a 20-channel coil. Structural MRI was acquired for each participant using a T1-weighted sequence with the following parameters: repetition time (TR) = 2,500 ms, echo time (TE) = 2.22 ms, inversion time (TI) = 1,120 ms, flip angle α (FA) = 8°, field of view (FOV) = 240 × 256 × 167 mm, resolution = 0.8 mm isotropic, acquisition time = 4 min and 32s. The acquisition parameters for echo planar T2-weighted imaging (EPI) were as follows: 60 oblique axial slices, TR = 2000 ms, TE = 22 ms, FA= 80°, FOV = 204 × 204 × 165 mm, 2.5 mm isotropic, and acceleration factor 3. E-Prime 2.0 (Psychology Software Tools) was used to present the stimuli.

EEG data.

A gel-based 64-channel Neuroscan system on a 10-20 electrode template was used for data acquisition, sampling at a rate of 1000 Hz. To mark the onset of each sentence, triggers were set at the beginning of each sentence. STIM2 software (Compumedics Neuroscan) was used for stimulus presentation.

Stimuli

The experimental stimuli utilized in both the EEG and fMRI consisted of approximately 20 minutes of the story The Little Prince in Cantonese audiobook. It was translated and narrated in Cantonese by a native male speaker. The stimuli consist of a total of 4,473 words and 535 sentences. To facilitate data analysis and participant engagement, the stimuli were further segmented into four distinct sections, each spanning nearly five minutes. To assess listening comprehension, participants were presented with five yes/no questions after completing each section, resulting in a total of 20 questions throughout the experiment. To make sure the speed of story narration was normal for the participants, we asked a few people who were different from the participants in this study to judge the speed and comprehensibility. They all reported the speed was normal, neither so slow nor so fast.

Annotation

We present audio and text annotations, including time-aligned speech segmentation and prosodic information, as well as word-by-word predictors derived from natural language processing (NLP) tools. These predictors include aspects of lexical semantic information, such as part-of-speech (POS) tagging and word frequency.

Prosodic information.

We extracted the root mean square intensity and the fundamental frequency (f0) from every 10 ms interval of the audio segments by utilizing the Voicebox toolbox (http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html). Peak RMS intensity and peak f0 for each word in the naturalistic stimuli were used to represent the intensity and pitch information for each word.

Word frequency.

Word segmentation was performed manually by two native Cantonese speakers. The log-transformed frequency of each word was also estimated using PyCantonese20, Version 3.4.0 (https://pycantonese.org/). The built-in corpus in PyCantonese is the Hong Kong Cantonese Corpus21 (HKCancor), collected

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

Number and percentage of live births, by age group of mother, 1991 to most recent year.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of Pinterest users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+3.14 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 9.88 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Annual data on death registrations by single year of age for the UK (1974 onwards) and England and Wales (1963 onwards).

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.