About this dataset

Context

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

Key observations

Largest age group (population): Male # 80-84 years (12) | Female # 75-79 years (17). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Lincoln township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lincoln township is shown in the following column.
• Population (Female): The female population in the Lincoln township 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 Lincoln township 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 Lincoln township Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Black Earth town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Black Earth town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Black Earth town was 495, a 1.59% decrease year-by-year from 2021. Previously, in 2021, Black Earth town population was 503, a decline of 0.79% compared to a population of 507 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Black Earth town increased by 12. In this period, the peak population was 539 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Black Earth town is shown in this column.
• Year on Year Change: This column displays the change in Black Earth town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Black Earth town Population by Year. You can refer the same here

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

Abstract copyright UK Data Service and data collection copyright owner.Gender and Adolescence: Global Evidence (GAGE) is a ten-year (2015-2025) research programme, funded by UK Aid from the UK Foreign, Commonwealth and Development Office (FCDO), that seeks to combine longitudinal data collection and a mixed-methods approach to understand the lives of adolescents in particularly marginalized regions of the Global South, and to uncover 'what works' to support the development of their capabilities over the course of the second decade of life, when many of these individuals will go through key transitions such as finishing their education, starting to work, getting married and starting to have children.GAGE undertakes longitudinal research in seven countries in Africa (Ethiopia, Rwanda), Asia (Bangladesh, Nepal) and the Middle East (Jordan, Lebanon, Palestine). Sampling adolescent girls and boys aged between 10‐19‐year olds, the quantitative survey follows a global total of 18,000 adolescent girls and boys, and their caregivers and explores the effects that programme have on their lives. This is substantiated by in‐depth qualitative and participatory research with adolescents and their peers. Its policy and legal analysis work stream studies the processes of policy change that influence the investment in and effectiveness of adolescent programming.Further information, including publications, can be found on the Overseas Development Institute GAGE website. In Jordan, GAGE recruited a sample of 4,101 adolescent girls and boys in two separate cohorts (younger adolescents aged 10-12 years and older adolescents age 15-18 years at baseline). GAGE surveyed the adolescents, as well as their adult female caregivers and, for those enrolled in formal schooling, conducted surveys at their schools. This sample includes Syrian refugees living in refugee camps, informal tented settlements (ITS) and host communities, as well as Palestinian refugees living in refugee camps and host communities, vulnerable Jordanian adolescents living in communities hosting refugees, and a small group of adolescents of other nationalities (Egyptian, Iraqi, and others) living in Jordan. The research sample was recruited during 2018 and 2019. Additional information about the sample and the baseline Jordan data are available in the GAGE Jordan Baseline Sample Overview and Data Use Manual (2021) available from UK Data Archive SN 8866. Gender and Adolescence: Global Evidence: Jordan Baseline School Survey, 2019-2020 contains data collected at baseline from an additional survey conducted in adolescents' communities, which focused on formal primary and secondary schools. Specific schools where Core Respondents attended were identified and linked based on the details collected from the Core Respondent baseline survey. Where schools consented to participate, questionnaires were administered to a key school informant, such as the principal or head teacher, in September 2019 through January 2020. Main Topics: Youth; adolescence; gender; longitudinal impact evaluation of youth programming Purposive selection/case studies Convenience sample Face-to-face interview: Computer-assisted (CAPI/CAMI)

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meter Source Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary Sphere Extent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer? This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro. In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend. To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth. Different Classifications Available to Map Five processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display. Using Time By default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year. In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change. Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009. This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover. Land Cover Processing To provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015. Source data The datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.php CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) 50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

Abstract

The Tajik Living Standards Survey (TLSS) was conducted jointly by the State Statistical Agency and the Center for Strategic Studies under the Office of the President in collaboration with the sponsors, the United Nations Development Programme (UNDP) and the World Bank (WB). International technical assistance was provided by a team from the London School of Economics (LSE). The purpose of the survey is to provide quantitative data at the individual, household and community level that will facilitate purposeful policy design on issues of welfare and living standards of the population of the Republic of Tajikistan in 1999.

Geographic coverage

National coverage. The TLSS sample was designed to represent the population of the country as a whole as well as the strata. The sample was stratified by oblast and by urban and rural areas.

The country is divided into 4 oblasts, or regions; Leninabad in the northwest of the country, Khatlon in the southwest, Rayons of Republican Subordination (RRS) in the middle and to the west of the country, and Gorno-Badakhshan Autonomous Oblast (GBAO) in the east. The capital, Dushanbe, in the RRS oblast, is a separately administrated area. Oblasts are divided into rayons (districts). Rayons are further subdivided into Mahallas (committees) in urban areas, and Jamoats (villages) in rural areas.

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

The TLSS sample was designed to represent the population of the country as a whole as well as the strata. The sample was stratified by oblast and by urban and rural areas.

In common with standard LSMS practice a two-stage sample was used. In the first stage 125 primary sample units (PSU) were selected with the probability of selection within strata being proportional to size. At the second stage, 16 households were selected within each PSU, with each household in the area having the same probability of being chosen. [Note: In addition to the main sample, the TLSS also included a secondary sample of 15 extra PSU (containing 400 households) in Dangara and Varzob. Data in the oversampled areas were collected for the sole purpose of providing baseline data for the World Bank Health Project in these areas. The sampling for these additional units was carried out separately after the main sampling procedure in order to allow for their exclusion in nationally representative analysis.] The twostage procedure has the advantage that it provides a self-weighted sample. It also simplified the fieldwork operation as a one-field team could be assigned to cover a number of PSU.

A critical problem in the sample selection with Tajikistan was the absence of an up to date national sample frame from which to select the PSU. As a result lists of the towns, rayons and jamoats (villages) within rayons were prepared manually. Current data on population size according to village and town registers was then supplied to the regional offices of Goskomstat and conveyed to the center. This allowed the construction of a sample frame of enumeration units by sample size from which to draw the PSU.

This procedure worked well in establishing a sample frame for the rural population. However administrative units in some of the larger towns and in the cities of Dushanbe, Khojand and Kurgan-Tubbe were too large and had to be sub-divided into smaller enumeration units. Fortuitously the survey team was able to make use of information available as a result of the mapping exercise carried out earlier in the year as preparation for the 2000 Census in order to subdivide these larger areas into enumeration units of roughly similar size.

The survey team was also able to use the household listings prepared for the Census for the second stage of the sampling in urban areas. In rural areas the selection of households was made using the village registers – a complete listing of all households in the village which is (purported to be) regularly updated by the local administration. When selecting the target households a few extra households (4 in addition to the 16) were also randomly selected and were to be used if replacements were needed. In actuality non-response and refusals from households were very rare and use of replacement households was low. There was never the case that the refusal rate was so high that there were not enough households on the reserve list and this enabled a full sample of 2000 randomly selected households to be interviewed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was based on the standard LSMS for the CIS countries, and adapted and abridged for Tajikistan. In particular the health section was extended to allow for more in depth information to be collected and a section on food security was also added. The employment section was reduced and excludes information on searching for employment.

The questionnaires were translated into Tajik, Russian and Uzbek.

The TLSS consists of three parts: a household questionnaire, a community level questionnaire and a price questionnaire.

Household questionnaire: the Household questionnaire is comprised of 10 sections covering both household and individual aspects.

Community/Population point Questionnaire: the Community level or Population Point Questionnaire consists of 8 sections. The community level questionnaire provides information on differences in demographic and economic infrastructure. Open-ended questions in the questionnaire were not coded and hence information on the responses to these qualitative questions is not provided in the data sets.

Summary of Section contents

The brief descriptions below provide a summary of the information found in each section. The descriptions are by no means exhaustive of the information covered by the survey and users of the survey need to refer to each particular section of the questionnaire for a complete picture of the information gathered.

Household information/roster This includes individual level information of all individuals in the household. It establishes who belongs to the household at the time of the interview. Information on gender, age, relation to household head and marital status are included. In the question relating to family status, question 7, “Nekared” means married where nekar is the Islamic (arabic) term for marriage contract. Under Islamic law a man may marry more than once (up-to four wives at any one time). Although during the Soviet period it was illegal to be married to more than one woman this practice did go on. There may be households where the household head is not present but the wife is married or nekared, or in the same household a respondent may answer married and another nekared to the household head.

Dwelling This section includes information covering the type of dwelling, availability of utilities and water supply as well as questions pertaining to dwelling expenses, rents, and the payment of utilities and other household expenses. Information is at the household level.

Education This section includes all individuals aged 7 years and older and looks at educational attainment of individuals and reasons for not continuing education for those who are not currently studying. Questions related to educational expenditures at the household level are also covered. Schooling in Tajikistan is compulsory for grades (classes) 1-9. Primary level education refers to grades 1 - 4 for children aged 7 to 11 years old. General secondary level education refers to grades 5-9, corresponding to the age group 12-16 year olds. Post-compulsory schooling can be divided into three types of school: - Upper secondary education covers the grades 10 and 11. - Vocational and Technical schools can start after grade 9 and last around 4 years. These schools can also start after grade 11 and then last only two years. Technical institutions provide medical and technical (e.g. engineering) education as well as in the field of the arts while vocational schools provide training for employment in specialized occupation. - Tertiary or University education can be entered after completing all 11 grades. - Kindergarten schools offer pre-compulsory education for children aged 3 – 6 years old and information on this type of schooling is not covered in this section.

Health This section examines individual health status and the nature of any illness over the recent months. Additional questions relate to more detailed information on the use of health care services and hospitals, including expenses incurred due to ill health. Section 4B includes a few terms, abbreviations and acronyms that need further clarification. A feldscher is an assistant to a physician. Mediniski dom or FAPs are clinics staffed by physical assistants and/or midwifes and a SUB is a local clinic. CRH is a local hospital while an oblast hospital is a regional hospital based in the oblast administrative centre, and the Repub. Hospital is a national hospital based in the capital, Dushanbe. The latter two are both public hospitals.

Employment This section covers individuals aged 11 years and over. The first part of this section looks at the different activities in which individuals are involved in order to determine if a person is engaged in an income generating activity. Those who are engaged in such activities are required to answer questions in Part B. This part relates to the nature of the work and the organization the individual is attached to as well as questions relating to income, cash income and in-kind payments. There are also a few questions relating to additional income generating activities in addition to the main activity. Part C examines employment

Those who experienced sexual harassment in the previous 12 months by whether they ever disclosed a harassment experience (% of persons aged 18 years and over who experienced sexual harassment in the previous 12 months)

The Everyday Childhoods collection is a qualitative longitudinal dataset that was collected by researchers from the Universities of Sussex and Brighton and the Open University during 2013-15. The initial project, called ‘Face 2 Face: Tracing the real and the mediated in children’s cultural worlds’ (F2F) was funded by an NCRM Methodology Innovation award. The primary aim of the project was to explore how technologies documented and mediated the everyday in children's daily lives. The F2F project generated the majority of the data contained in this collection and the dataset comprises data from two research panels: firstly, a younger panel (the 'extensive' panel) of children aged 7-8 years (n=6) who had previously been involved with their families in an ESRC funded study of new motherhood ('The Making of Modern Motherhoods: Memories, Representations, Practices'). Their geographical location ranged across the South, South East and South West of England. Secondly, an older panel (the 'intensive' panel) of children aged 10-15 years (n=7) were recruited for the first time in this study. Their geographical location was focused in the South East of England. This latter sample were recruited to illustrate a diversity of youth experiences and identities, including along intersectional lines of ethnicity, religion, dis/ability, urban/rural locality, and economic background. Over the course of 12 months, both groups of children took part in a series of regular research activities aimed at capturing their everyday lives.Face 2 Face: Tracing the Real and the Mediated in Children’s Cultural Worlds (2013-14) was a 12-month methodological innovation project funded by the ESRC’s National Centre for Research Methods. The study documented thirteen children and young people’s everyday lives over a 12-month period, focusing on how new media technologies were infused in their everyday lives and relationships. The research team worked with two panels: a group of 8 year olds who were part of an established longitudinal study of new motherhood and had been followed since before birth, and a newly established panel of 11-15 year olds. Using a combination of biographical, ethnographic and digital/material methods, the research team worked closely with participants and their families to document their everyday lives. One of the key outputs of the research was a set of public multimedia documents that experimented with using data from the study to depict the children's lives. These multi-modal documents used the digital sound recordings, photographs, ethnographic descriptions and other data captured during the different phases of the study. Ethical practice around the documentation and curation of data about children's lives was a key strand of the study, and were further developed through an AHRC funded follow-up study: 'Curating Childhoods'. This study enabled the to follow up questions about the use of research data in digital age into the domains of archiving and data sharing. As part of this follow up project, participants from the Face 2 Face study were invited to take part in a one-day workshop at the Mass Observation Archive to discuss the future archiving and re-use of their data. (1) ‘Favourite Things’ interviews – Carried out with each participant at the beginning of the study, during which children were invited to share ‘favourite’ possessions in their homes with a focus on objects that connected to their past and objects that connected to their future. The interviews were audio recorded and transcribed and the children’s objects were photographed. (2) Family interviews - To gain a sense of the children’s everyday routines, some of the children’s families were interviewed about a typical day in their household. These interviews typically included the child and at least one parent, and sometimes siblings and extended family. The family interviews were audio recorded and transcribed (3) ‘Day in a life’ observations – Each child was ethnographically observed by a researcher over a single day. These included school days, holidays and weekend days – and were normally chosen by the child in conjunction with their parent. The researchers drew on multimodal practices of ethnographic observation – collecting visual and audio data alongside traditional field notes. (4) Recursive interviews – At the conclusion of the 12 months of fieldwork, each child took part in a final interview to look back on their participation in the study, and to look at the data collected as part of the project. All younger children, and some older children, were interviewed with their parents. Data was presented back to the participants in curated multimedia documents which were intended to be shared publicly on the project’s website with the permission of children and parents.

Abstract

Young Lives: An International Study of Childhood Poverty is a collaborative project investigating the changing nature of childhood poverty in selected developing countries. The UK’s Department for International Development (DFID) is funding the first three-year phase of the project.

Young Lives involves collaboration between Non Governmental Organisations (NGOs) and the academic sector. In the UK, the project is being run by Save the Children-UK together with an academic consortium that comprises the University of Reading, London School of Hygiene and Tropical Medicine, South Bank University, the Institute of Development Studies at Sussex University and the South African Medical Research Council.

The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.

Objectives of the study The Young Lives study has three broad objectives: • producing good quality panel data about the changing nature of the lives of children in poverty. • trace linkages between key policy changes and child poverty • informing and responding to the needs of policy makers, planners and other stakeholders There will also be a strong education and media element, both in the countries where the project takes place, and in the UK.

The study takes a broad approach to child poverty, exploring not only household economic indicators such as assets and wealth, but also child centred poverty measures such as the child’s physical and mental health, growth, development and education. These child centred measures are age specific so the information collected by the study will change as the children get older.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

Geographic coverage

Young Lives is an international study of childhood poverty, involving 12,000 children in 4 countries. - Ethiopia (20 communities in Addis Ababa, Amhara, Oromia, and Southern National, Nationalities and People's Regions) - India (20 sites across Andhra Pradesh and Telangana) - Peru (74 communities across Peru) - Vietnam (20 communities in the communes of Lao Cai in the north-west, Hung Yen province in the Red River Delta, the city of Danang on the coast, Phu Yen province from the South Central Coast and Ben Tre province on the Mekong River Delta)

Analysis unit

Individuals; Families/households

Universe

Cross-national; Subnational

Children aged approximately 5 years old and their households, and children aged 12 years old and their households, in Ethiopia, India (Andhra Pradesh), Peru and Vietnam, in 2006-2007. These children were originally interviewed in Round 1 of the study. See documentation for details of the exact regions covered in each country.

Kind of data

Sample survey data [ssd]

Sampling procedure

Purposive selection/case studies

A key need for the study's objectives was to obtain data at different levels - the children, their households, the community in which they resided, as well as at regional and national levels. This need thus determined that children should be selected in geographic clusters rather than randomly selected across the country. There was, however, a much more important reason for recruiting children in clusters - the sites are also intended to provide suitable settings for a range of complementary thematic studies. For example, one or a few sites may be used for a qualitative study designed to achieve a deeper level of understanding of some social issues, either because they are important in that particular place, or because the sites are appropriate locales to investigate a more general concern. The quantitative panel study is seen as the foundation upon which a coherent and interesting range of linked studies can be set up.

Thus the design was decided, in each country, comprising 20 geographic clusters with 100 children sampled in each cluster.

Sampling deviation

Ethiopia: 1,912 (5-year-olds), 980 (12-year-olds); India: 1,950 (5-year-olds), 994 (12-year-olds); Peru: 1,963 (5-year-olds), 685 (12-year-olds); Vietnam: 1,970 (5-year-olds), 990 (12-year-olds)

Mode of data collection

Face-to-face interview

Research instrument

Every questionnaire used in the study consists of a 'core' element and a country-specific element, which focuses on issues important for that country.

The core element of the questionnaires consists of the following sections: Core 5 & 12 year old household questionnaire • Section 1: Parental background • Section 2: Household education • Section 3: Livelihoods and asset framework • Section 3a: Land & crops • Section 3b: Time allocation • Section 3c: Productive assets • Section 3d: Non-agricultural earnings • Section 3e: Transfers • Section 4: Consumption/Expenditure • Section 4a: Food consumption/expenditure • Section 4b: Non-food consumption/expenditure • Section 5: Social capital • Section 5a: Support networks • Section 5b: Family, group and political capital • Section 5c: Collective action and exclusion • Section 5d: Information networks • Section 6: Economic changes and recent life history • Section 7: Socio-economic status • Section 8: Child care, education & activities (blank in 12yr old household) • Section 9: Child health • Section 10: Child development (blank in 12yr old household) • Section 11: Anthropometry • Section 12: Caregiver perceptions & attitudes

Core 12 year old child questionnaire • Section 1: School and activities • Section 2: Child health • Section 3: Social networks, social skills and social support • Section 4: Feelings and attitudes • Section 5: Parents and household issues • Section 6: Perceptions of household wealth and future • Section 7: Child Development

The community questionnaire used in Ethiopia consists of the following sections: - MODULE 1 General Module • Section 1 General Community Characteristics • Section 2 Social Environment • Section 3 Access to Services • Section 4 Economy • Section 5 Local Prices - MODULE 2 Child-Specific Modules • Section 1 Educational Service (General) • Section 2 NOT INCLUDED IN ETHIOPIA CONTEXT INSTRUMENT • Section 3 Educational Services (Preschool, Primary, Secondary) • Section 4 Health Services • Section 5 Child Protection Services - MODULE 3 Country specific community level questions • Section 1 Conversion factors • Section 2 Migration • Section 3 Social protection program • Section 4 Equity and budget management in education and health

The community questionnaire used in India consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5; Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Peru consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Vietnam consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices • Section 6: Poverty Alleviation and Infrastructure Initiatives - MODULE 2 Child-Specific Module • Section 1: Educational Services (General and Country Specific) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood. The Young Lives study aims to track the lives of 12,000 children over a 15-year period, surveyed once every 3-4 years. Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, and Round 4 surveyed them at 12 and 19 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves. The survey consists of three main elements: a child questionnaire, a household questionnaire and a community questionnaire. The household data gathered is similar to other cross-sectional datasets (such as the World Bank's Living Standards Measurement Study). It covers a range of topics such as household composition, livelihood and assets, household expenditure, child health and access to basic services, and education. This is supplemented with additional questions that cover caregiver perceptions, attitudes, and aspirations for their child and the family. Young Lives also collects detailed time-use data for all family members, information about the child's weight and height (and that of caregivers), and tests the children for school outcomes (language comprehension and mathematics). An important element of the survey asks the children about their daily activities, their experiences and attitudes to work and school, their likes and dislikes, how they feel they are treated by other people, and their hopes and aspirations for the future. The community questionnaire provides background information about the social, economic and environmental context of each community. It covers topics such as ethnicity, religion, economic activity and employment, infrastructure and services, political representation and community networks, crime and environmental changes. The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country. Further information about the survey, including publications, can be downloaded from the Young Lives website. School surveys were introduced into Young Lives in 2010 in order to capture detailed information about children's experiences of schooling, and to improve our understanding of: the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groups. school effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systems. equity issues (including gender) in relation to learning outcomes and the evolution of inequalities within education The survey allows us to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives. Findings are all available on our Education theme pages and our publications page. Further information is available from the Young Lives School Survey webpages.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Release of the hourly database containing 55 cruises spanning over 31 years, including the historical data from 12 cruises done in the 1990's (Fairall et al., 2003) and 9 cruises of the PACS/EPIC dataset. Data collected from these cruises are critical for supporting the study of physical oceanography, air-sea interaction, tropical meteorology, as well as global weather and climate variability and predictability. This includes improvement to our fundamental understanding of these processes in the ocean and their influence around the globe including the Continental United States. The data will also support improvement and validation of prediction models including parameterizations. Sensible heat flux was computed from vertical velocity - sonic temperature covariance. The humidity contribution to sonic temperature was removed using the bulk latent heat flux. acknowledgement=NOAA Global Ocean Monitoring and Observing (GOMO) program cdm_data_type=Trajectory cdm_trajectory_variables=cruise_name comment=Corrections and Data Quality Notes not contained in global or variable attributes: Unavailable data, bad data, and data within restricted Exclusive Economic Zones were assigned _FillValue = -9999. Please use the variables named flag_bad_ship and flag_bad_bulk to further mask out questionable or non-ideal data points depending on the application for state variables and bulk fluxes respectively. comment2=Sensible heat flux was computed from vertical velocity - sonic temperature covariance. The humidity contribution to sonic temperature was removed using the bulk latent heat flux. comment3=A correction to account for biases in gas concentration measurements has been applied on the covariance and ID latent heat fluxes. See Fratini et al. 2014 for more details. comment4=Data from the 2004 New England Air Quality Study (NEAQS-04) are included in this dataset but it has to be noted that during that project we found significant suppression of the transfer coefficients for momentum, sensible heat, and latent heat; mainly because our measurements at 18-m height did not realize the full surface flux in these shallower boundary layer conditions. (Fairall et al., 2006). Conventions=CF-1.6, ACCD-1.3, COARDS, ACDD-1.3 coverage_content_type=physicalMeasurement, qualityInformation, modelResult, coordinate date_metadata_modified=2023-04-18T13:10:40Z Easternmost_Easting=179.73351 featureType=Trajectory geospatial_lat_bounds=POLYGON [-179.833, 179.734, -53.754, 69.934] geospatial_lat_max=69.933717 geospatial_lat_min=-53.753807 geospatial_lat_units=degrees_north geospatial_lon_max=179.73351 geospatial_lon_min=-179.83283 geospatial_lon_units=degrees_east geospatial_vertical_max=0.014330280134111055 geospatial_vertical_min=1.7080496969024725E-4 geospatial_vertical_positive=down geospatial_vertical_units=m history=v0: original data, v1: first release id=doi = not yet assigned infoUrl=https://psl.noaa.gov/boundary-layer/ institution=(1) NOAA Physical Sciences Lab (PSL); (2) CIRES Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder in partnership with NOAA PSL instrument_vocabulary=GCMD Version 12.3 keywords_library=GCMD Version 12.3 keywords_vocabulary=GCMD Science Keywords licence=Please acknowledge data according to global attribute info: acknowledgement, creator_name, creator_institution. These data may be redistributed and used without restriction. naming_authority=gov.noaa.ncei Northernmost_Northing=69.933717 platform=refer to platform_name variable that contains names of the different platforms from which the datasets were collected platform_vocabulary=GCMD Version 12.3 processing_level=processed and quality controlled program=Funding agencies: NOAA Global Ocean Monitoring and Observing (GOMO) program project=refer to cruise_name variable for project names of various datasets references=Fairall et al. 1996a JGR https://doi.org/10.1029/95JC03190 ...Fairall et al. 1996b JGR https://doi.org/10.1029/95JC03205 .... Fairall et al. 2003 JClim https://doi.org/10.1175/1520-0442(2003)016%3C0571:BPOASF%3E2.0.CO;2 ... Edson et al. 2013 JPO with corrigendum: the value should be m = 0.0017, and not m = 0.017 as originally appeared https://doi.org/10.1175/JPO-D-12-0173.1 ... Fratini et al. 2014https://doi.org/10.5194/bg-11-1037-2014 ... Fairall et al. 2006 https://doi.org/10.1029/2006JD007597 sea_name=Northwest, Equatorial and SouthEast Pacific Ocean; North Atlantic Ocean; Davis Strait; Labrador Sea; South Atlantic Ocean; Bay of Bengal; Indian Ocean; Tasman Sea source=observations from NOAA PSL sensors (no subscript, most accurate) and the ship permanent sensors (_ship subscript, less accurate), derivations from those observations using eddy covariance and inertial dissipation methods of estimating fluxes, model results from COARE 3.6 bulk air-sea flux algorithm. Wave parameters were not used as input to COARE since they were either unavailable or not consistently available on all projects. Also True water-relative wind speed was used as input to COARE when available. Otherwise when not available the true wind speed was used instead. sourceUrl=(local files) Southernmost_Northing=-53.753807 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_duration=31 years time_coverage_end=2021-08-31T23:00:00Z time_coverage_resolution=PT60.M time_coverage_start=1991-11-22T11:41:00Z Westernmost_Easting=-179.83283

Data Resource Preview - Those who experienced stalking with fear of sexual violence by whether they ever disclosed a stalking experience (% of persons aged 18 years and over who experienced stalking with intent of sexual violence in the previous 12 months) @--> Disclosed a stalking…Disclosed a stalking…Disclosed a stalking…All persons aged 18…050100Those who experienced stalking with fear of sexual violence by whether they ever disclosed a stalking experience (% of persons aged 18 years and over who experienced stalking with intent of sexual violence in the previous 12 months)FiltersBoth sexesMaleFemale SexRows & ColumnsStatisticSex ↔ YearSex

This layer is a subset of Global Landcover 1992- 2020 Layer. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

The 2012 Program for International Student Assessment (PISA:12) is a study that is part of the Program for International Student Assessment (PISA) program; program data is available since 2000 at . PISA:12 (https://nces.ed.gov/surveys/pisa/) is a cross-sectional study that measures the yield of education systems, or what skills and competencies students have acquired and can apply in reading, mathematics, and science to real-world contexts by age 15. For PISA:12, mathematics literacy was the subject area assessed in-depth. The study was conducted using questionnaires and direct assessments of 15-year-old students. 15-year-old students in April to May of 2012 were sampled. Key statistics produced from PISA:12 are 15-year-olds' capabilities in reading, mathematics, and science literacy, problem solving, and financial literacy.

A global land-based gridded dataset of daily precipitation at 1 degree X 1 degree resolution from 1950 to 2013. Two datasets are available under the REGEN moniker. This dataset interpolates all daily precipitation stations available regardless of completeness of station timeseries.

A second related dataset interpolates only the long-term stations (stations with at least 40 complete years of data). Besides the grid cell average precipitation amount per day (mm/day), the Yamamoto standard deviation per grid cell (mm/day), the kriging error per grid cell (%) and number of stations per grid cell variables are also included.

Currently, there are two major data archives of global in situ daily rainfall data: 1. The Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and 2. The Deutscher Wetterdienst (DWD) Global Precipitation Climatology Centre (GPCC). These two data archives are combined along with additional station data acquired from other researchers. The merged archive is quality controlled and the flagged stations are removed to create a long term high quality archive of raw station data, which is then interpolated using ordinary block kriging by this dataset. The REGEN long-term dataset instead only interpolates a long-term station subset of this high quality merged archive.

The output consists of a separate CF-compliant netcdf file for each year, each containing the four aforementioned variables. These values are available for global land areas with the exception of Antarctica. The time dimension of the netcdf ranges from 1950-01-01 to 2013-12-31. Each dataset (All station based and long-term station based) is in classic netcdf format and occupies around 350MB of disk space for each year with the combined total of all years being around 26GB. Besides these two netcdfs, two additional netcdfs containing a mask indicating the high data quality grid cells of each dataset (all station and long-term) are also available. These netcdf files have the same grid descriptions as the original data but contain only one timestep for the entire period.

This dataset was produced by Steefan Contractor of the ARC Centre of Excellence for Climate Systems Sciences, as part of a the research program "The role of land surface forcing and feedbacks for regional climate".

The Gross Domestic Product (GDP) in the United States was worth 27720.71 billion US dollars in 2023, according to official data from the World Bank. The GDP value of the United States represents 26.29 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The 2016 Integrated Household Panel Survey (IHPS) was launched in April 2016 as part of the Malawi Fourth Integrated Household Survey fieldwork operation. The IHPS 2016 targeted 1,989 households that were interviewed in the IHPS 2013 and that could be traced back to half of the 204 enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11. The 2019 IHPS was launched in April 2019 as part of the Malawi Fifth Integrated Household Survey fieldwork operations targeting the 2,508 households that were interviewed in 2016. The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. Available as part of this project is the IHPS 2019 data, the IHPS 2016 data as well as the rereleased IHPS 2010 & 2013 data including only the subsample of 102 EAs with updated panel weights. Additionally, the IHPS 2016 was the first survey that received complementary financial and technical support from the Living Standards Measurement Study – Plus (LSMS+) initiative, which has been established with grants from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Living Standards Measurement Study (LSMS) team, in collaboration with the World Bank Gender Group and partner national statistical offices. The LSMS+ aims to improve the availability and quality of individual-disaggregated household survey data, and is, at start, a direct response to the World Bank IDA18 commitment to support 6 IDA countries in collecting intra-household, sex-disaggregated household survey data on 1) ownership of and rights to selected physical and financial assets, 2) work and employment, and 3) entrepreneurship – following international best practices in questionnaire design and minimizing the use of proxy respondents while collecting personal information. This dataset is included here.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

The IHPS 2016 and 2019 attempted to track all IHPS 2013 households stemming from 102 of the original 204 baseline panel enumeration areas as well as individuals that moved away from the 2013 dwellings between 2013 and 2016 as long as they were neither servants nor guests at the time of the IHPS 2013; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 2010 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS 2013) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban. The IHPS 2013 main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Given budget and resource constraints, for the IHPS 2016 the number of sample EAs in the panel was reduced to 102 out of the 204 EAs. As a result, the domains of analysis are limited to the national, urban and rural areas. Although the results of the IHPS 2016 cannot be tabulated by region, the stratification of the IHPS by region, urban and rural strata was maintained. The IHPS 2019 tracked all individuals 12 years or older from the 2016 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data Entry Platform To ensure data quality and timely availability of data, the IHPS 2019 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHPS 2019, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer that the NSO provided. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

Data Management The IHPS 2019 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHPS 2019 Interviews were mainly collected in “sample” mode (assignments generated from headquarters) and a few in “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample. This hybrid approach was necessary to aid the tracking operations whereby an enumerator could quickly create a tracking assignment considering that they were mostly working in areas with poor network connection and hence could not quickly receive tracking cases from Headquarters.

The range and consistency checks built into the application was informed by the LSMS-ISA experience with the IHS3 2010/11, IHPS 2013 and IHPS 2016. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (the NSO management) assigned work to the supervisors based on their regions of coverage. The supervisors then made assignments to the enumerators linked to their supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHPS 2019 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to Stata for other consistency checks, data cleaning, and analysis.

Data Cleaning The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field-based field teams utilizing error messages generated by the Survey Solutions application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent. The supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field, and this resulted from the additional error reports generated in Stata, which were in turn sent to the field teams via email or DropBox. The field supervisors collected reports for their assignments and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call-backs while the team was still operating in the EA when required. Corrections to the data were entered in the rejected questionnaires and sent back to headquarters.

The data cleaning process was done in several stages over the course of the fieldwork and through preliminary analyses. The first stage was during the interview itself. Because CAPI software was used, as enumerators asked the questions and recorded information, error messages were provided immediately when the information recorded did not match previously defined rules for that variable. For example, if the education level for a 12 year old respondent was given as post graduate. The second stage occurred during the review of the questionnaire by the Field Supervisor. The Survey Solutions software allows errors to remain in the data if the enumerator does not make a correction. The enumerator can write a comment to explain why the data appears to be incorrect. For example, if the previously mentioned 12 year old was, in fact, a genius who had completed graduate studies. The next stage occurred when the data were transferred to headquarters where the NSO staff would again review the data for errors and verify the comments from the