United States - Literacy Rate, Adult Total for Other Small States was 86.25975 % of People Ages 15 and Above in January of 2023, according to the United States Federal Reserve. Historically, United States - Literacy Rate, Adult Total for Other Small States reached a record high of 86.25975 in January of 2023 and a record low of 71.82288 in January of 1983. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Literacy Rate, Adult Total for Other Small States - last updated from the United States Federal Reserve on August of 2025.

In the past five decades, the global literacy rate among adults has grown from 67 percent in 1976 to 87.36 percent in 2023. In 1976, males had a literacy rate of 76 percent, compared to a rate of 58 percent among females. This difference of over 17 percent in 1976 has fallen to just seven percent in 2020. Although gaps in literacy rates have fallen across all regions in recent decades, significant disparities remain across much of South Asia and Africa, while the difference is below one percent in Europe and the Americas. Reasons for these differences are rooted in economic and cultural differences across the globe. In poorer societies, families with limited means are often more likely to invest in their sons' education, while their daughters take up a more domestic role. Varieties do exist on national levels, however, and female literacy levels can sometimes exceed the male rate even in impoverished nations, such as Lesotho (where the difference was over 17 percent in 2014); nonetheless, these are exceptions to the norm.

Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2023, the degree of literacy in India was about 77 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

National Assessment of Adult Literacy, 2003 (NAAL:2003), is a study that is part of the National Assessment of Adult Literacy program. NAAL:2003 (https://nces.ed.gov/naal/) is a cross-sectional assessment that collected information about English literacy among American adults age 16 and older. The study was conducted using direct assessment from 19,000 adults 16 or older, in their homes and some in prisons from the 50 states and District of Columbia. Households and prison inmates were sampled in 2003. The weighted response rate was 62.1 percent for households and 88.3 percent for prison inmates. Key statistics produced from NAAL:2003 include reading skills, general literacy, relationships, demographics, and background characteristics.

Literacy Rate: Tamil Nadu data was reported at 80.100 % in 12-01-2011. This records an increase from the previous number of 73.450 % for 12-01-2001. Literacy Rate: Tamil Nadu data is updated decadal, averaging 58.525 % from Dec 1961 (Median) to 12-01-2011, with 6 observations. The data reached an all-time high of 80.100 % in 12-01-2011 and a record low of 36.390 % in 12-01-1961. Literacy Rate: Tamil Nadu data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Education Sector – Table IN.EDA001: Literacy Rate.

Context

There are more than 26.8 million people or 2.2% of the population currently who have disabilities in India (Census 2011) which itself is said to be a very conservative estimate. There is a lot of stigma associated with the disabled community and a very high inequality in terms of social as well as monetary status between the disabled community and the entire population.

Content

The data in the csv file gives us the statewise values of the following:

1.State 2.number_disabled : It gives the total number of people in the region that are disabled. 3.total_population: It gives the total number of people in the region. 4.percent_disabled: It gives the total percentage of the people disabled in the given region. 5.literacy_rate_disabled : It represents the literacy rate of the disabled community in the region. 6.literacy_rate_general : It shows the total literacy rate of the population in the state. 7.workforce_rate_disabled : It tells us the total percent of all the disabled people that are part of the workforce in the given region.(inclusive all ages). 8.workforce_rate_general : It shows the total percent of all the people that are part of the workforce in the given region(inclusive of all ages).

Vietnam VN: Literacy Rate: Adult Male: % of Males Aged 15 and Above data was reported at 95.785 % in 2009. This records an increase from the previous number of 93.933 % for 2000. Vietnam VN: Literacy Rate: Adult Male: % of Males Aged 15 and Above data is updated yearly, averaging 93.918 % from Dec 1979 (Median) to 2009, with 5 observations. The data reached an all-time high of 95.785 % in 2009 and a record low of 90.381 % in 1979. Vietnam VN: Literacy Rate: Adult Male: % of Males Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data was reported at 94.368 % in 2015. This records an increase from the previous number of 94.140 % for 2014. South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data is updated yearly, averaging 92.895 % from Dec 1980 (Median) to 2015, with 9 observations. The data reached an all-time high of 94.368 % in 2015 and a record low of 76.200 % in 1980. South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Time series data for the statistic Literacy_Rate_Adult_Total and country Uganda. Indicator Definition:Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.The statistic "Literacy Rate Adult Total" stands at 80.59 percent as of 12/31/2022, the highest value at least since 12/31/1992, the period currently displayed. Regarding the Ten-Year-Change of the series, the current value constitutes an increase of 10.59 percentage points compared to the value ten years prior.The 10 year change in percentage points is 10.59.The Serie's long term average value is 69.67 percent. It's latest available value, on 12/31/2022, is 10.92 percentage points higher, compared to it's long term average value.The Serie's change in percentage points from it's minimum value, on 12/31/1991, to it's latest available value, on 12/31/2022, is +24.59.The Serie's change in percentage points from it's maximum value, on 12/31/2022, to it's latest available value, on 12/31/2022, is 0.0.

The National Reporting System (NRS) for Adult Education, 2017-18 (NRS 2017-18) is a performance accountability system for the national adult education program that is authorized under the Adult Education and Family Literacy Act (AEFLA), title II of the Workforce Innovation and Opportunity Act (WIOA) of 2014. More information about the program is available at . NRS 2017-18 is a cross-sectional data collection that is designed to monitor performance accountability for the federally funded, state-administered adult education program. States are required to submit their progress in adult education and literacy activities by reporting data on the WIOA primary indicators of performance for all AEFLA program participants who receive 12 or more hours of service, as well as state expenditures on the adult education program. States may also report on additional, optional secondary measures that include outcomes related to employment, family, and community. The data collection is conducted using a web-based reporting system. NRS 2017-18 is a universe data collection activity, and all states are required to submit performance data. Key statistics that are produced from the data collection include student demographics, receipt of secondary school diploma or a high school equivalency (HSE) credential, placement in postsecondary education or training, measurable skill gain, and employment outcomes.

Context

This datasets contains data from RBI which is published annually and this data has different features such as

Column names and legends

2000-01-INC = Income of each state for the year 2001 2011-12-INC = Income of each state for the year 2011

2001 - LIT = Literacy rate of each state for the year 2001 2011- LIT = Literacy rate of each state for the year 2011

2001 - POP = Total population of each state for the year 2001 2011- POP = Total population of each state for the year 2011

2001 -SEX_Ratio = Sex_Ratio of the each state for the year 2001 2011 -SEX_Ratio = Sex_Ratio of the each state for the year 2011

2001 -UNEMP = Unemployment rate of the each state for the year 2001 2011 -UNEMP = Unemployment rate of the each state for the year 2011

2001 -Poverty = Poverty rate of the each state for the year 2001 2011 -Poverty = Poverty rate of the each state for the year 2001

Unemployment Rate - for a month is calculated using the following formula: The monthly estimations for India are calculated as a ratio of the total estimated unemployed persons in India to the total estimated labor force for a month

Poverty rate = A common method used to estimate poverty in India is based on the income or consumption levels and if the income or consumption falls below a given minimum level, then the household is said to be Below the Poverty Line

state's Income measured using state domestic product - is the total value of goods and services produced during any financial year within the geographical boundaries of a state

Literacy rate - Total number of literate persons in a given age group, expressed as a percentage of the total population in that age group. The adult literacy rate measures literacy among persons aged 15 years and above, and the youth literacy rate measures literacy among persons aged 15 to 24 years

Acknowledgements

I wouldn't be here without the help of my friends and people who read this post. I owe you thanks for this research.

Inspiration

here are pretty basic question but I would high appreciate the data scientist community for any deep insight of the data in plots Cheers!!

Objective of the study:

-Is state's income is based on the education of the state -Does literacy rate contribute any changes to poverty rate

Source of the Datasets

datasets link

if this found useful kindly up-vote cheers!!

The National Center for Education Statistics surveyed 12,330 U.S. adults ages 16 to 74 living in households from 2012 to 2017 for the Program for the International Assessment of Adult Competencies (PIAAC), an international study involving over 35 countries. Using small area estimation models (SAE), indirect estimates of literacy and numeracy proficiency have been produced for all U.S. states and counties. By using PIAAC survey data in conjunction with data from the American Community Survey, the Skills Map data provides reliable estimates of adult literacy and numeracy skills in all 50 states, all 3,141 counties, and the District of Columbia.

SAE is a model-dependent approach that produces indirect estimates for areas where survey data is inadequate for direct estimation. SAE models assume that counties with similar demographics would have similar estimates of skills. An estimate for a county then “borrows strength” across related small areas through auxiliary information to produce reliable indirect estimates for small areas. The models rely on covariates available at the small areas, and PIAAC survey data. In the absence of any other proficiency assessment data for individual states and counties, the estimates provide a general picture of proficiency for all states and counties. In addition to the indirect estimates, this website provides precision estimates and facilitates statistical comparisons among states and counties. For technical details on the SAE approach applied to PIAAC, see section 5 of the State and County Estimation Methodology Report.

The U.S. county indirect estimates reported in this data are not directly comparable with the direct estimates for PIAAC countries that are reported by the Organization for Economic Cooperation and Development (OECD). Specifically, the U.S. county indirect estimates (1) represent modeled estimates for adults ages 16-74 whereas the OECD’s direct estimates for participating countries represent estimates for adults ages 16-65, (2) include data for “literacy-related nonresponse” (i.e., adults whose English language skills were too low to participate in the study) whereas the OECD’s direct estimates for countries exclude these data, and (3) are based on three combined data collections (2012/2014/2017) whereas OECD’s direct estimates are based on a single data collection.Please visit the Skills Map to learn more about this data.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Diverse learning theories have been constructed to understand learners' internal states through various tangible predictors. We focus on self-regulatory actions that are subconscious and habitual actions triggered by behavior agents' 'awareness' of their attention loss. We hypothesize that self-regulatory behaviors (i.e., attention regulation behaviors) also occur in e-reading as 'regulators' as found in other behavior models (Ekman, P., & Friesen, W. V., 1969). In this work, we try to define the types and frequencies of attention regulation behaviors in e-reading. We collected various cues that reflect learners' moment-to-moment and page-to-page cognitive states to understand the learners' attention in e-reading.

The text 'How to make the most of your day at Disneyland Resort Paris' has been implemented on a screen-based e-reader, which we developed in a pdf-reader format. An informative, entertaining text was adopted to capture learners' attentional shifts during knowledge acquisition. The text has 2685 words, distributed over ten pages, with one subtopic on each page. A built-in webcam on Mac Pro and a mouse have been used for the data collection, aiming for real-world implementation only with essential computational devices. A height-adjustable laptop stand has been used to compensate for participants' eye levels.

Thirty learners in higher education have been invited for a screen-based e-reading task (M=16.2, SD=5.2 minutes). A pre-test questionnaire with ten multiple-choice questions was given before the reading to check their prior knowledge level about the topic. There was no specific time limit to finish the questionnaire. We collected cues that reflect learners' moment-to-moment and page-to-page cognitive states to understand the learners' attention in e-reading. Learners were asked to report their distractions on two levels during the reading: 1) In-text distraction (e.g., still reading the text with low attentiveness) or 2) out-of-text distraction (e.g., thinking of something else while not reading the text anymore). We implemented two noticeably-designed buttons on the right-hand side of the screen interface to minimize possible distraction from the reporting task. After triggering a new page, we implemented blur stimuli on the text in the random range of 20 seconds. It ensures that the blur stimuli occur at least once on each page. Participants were asked to click the de-blur button on the text area of the screen to proceed with the reading. The button has been implemented in the whole text area, so participants can minimize the effort to find and click the button. Reaction time for de-blur has been measured, too, to grasp the arousal of learners during the reading. We asked participants to answer pre-test and post-test questionnaires about the reading material. Participants were given ten multiple-choice questions before the session, while the same set of questions was given after the reading session (i.e., formative questions) with added subtopic summarization questions (i.e., summative questions). It can provide insights into the quantitative and qualitative knowledge gained through the session and different learning outcomes based on individual differences. A video dataset of 931,440 frames has been annotated with the attention regulator behaviors using an annotation tool that plays the long sequence clip by clip, which contains 30 frames. Two annotators (doctoral students) have done two stages of labeling. In the first stage, the annotators were trained on the labeling criteria and annotated the attention regulator behaviors separately based on their judgments. The labels were summarized and cross-checked in the second round to address the inconsistent cases, resulting in five attention regulation behaviors and one neutral state. See WEDAR_readme.csv for detailed descriptions of features.

The dataset has been uploaded 1) raw data, which has formed as we collected, and 2) preprocessed, that we extracted useful features for further learning analytics based on real-time and post-hoc data.

Reference

Ekman, P., & Friesen, W. V. (1969). The repertoire of nonverbal behavior: Categories, origins, usage, and coding. semiotica, 1(1), 49-98.

Water Quality Dataset

The basis of this dataset is taken from WaterBase water quality data shared on EAA. After most of the columns there were dropped, new data was created with the help of Worldbank, OSM, Foursquare, SEDAC. After removing the country and city information from the available location information, socioeconomic features of that country were added. However, the distance of certain road types close to those coordinates was also added with OSM. It is thought that such information plays an important role in the pollution of waters.

Features:

parameterWaterBodyCategory: Water body category code, as defined in the codelist. (Taken from EAA) observedPropertyDeterminandCode: Unique code of the determinand monitored, as defined in the codelist. (Taken from EAA) procedureAnalysedFraction: Specification of which fraction of the sample was analysed. (Taken from EAA) procedureAnalysedMedia: Type of media monitored. (Taken from EAA) resultUom: Unit of measure for the reported values. (Taken from EAA) phenomenonTimeReferenceYear: Year during which the data were sampled. (Taken from EAA) parameterSamplingPeriod: The period of the year during which the data used for the aggregation were sampled. (Taken from EAA) resultMeanValue: Mean value of the data used for aggregation. (Taken from EAA) waterBodyIdentifier: Unique international identifier of the water body in which the data were obtained. (Taken from EAA) Country: Country info generated by using coordinates. PopulationDensity: Population density of Country TerraMarineProtected_2016_2018: Mean of protected Terra Marine areas of Country Between 2016-2018 TouristMean_1990_2020: Mean of Tourist count of Country between 1990-2020 VenueCount: Venue count in near of given coordinates. netMigration_2011_2018: Mean of migration of given Country between 2011-2018 literacyRate_2010_2018: Literacy rate of Country between 2010-2018 combustibleRenewables_2009_2014: Compustible Renewable count in Country between 2009-2014 droughts_floods_temperature: gdp composition_food_organic_waste_percent composition_glass_percent composition_metal_percent composition_other_percent composition_paper_cardboard_percent composition_plastic_percent composition_rubber_leather_percent composition_wood_percent composition_yard_garden_green_waste_percent waste_treatment_recycling_percent

Sources: https://www.eea.europa.eu/data-and-maps/data/waterbase-water-quality-2 https://datacatalog.worldbank.org/dataset/what-waste-global-database

Abstract

The National Family Health Survey 2019-21 (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India, each state/union territory (UT), and for 707 districts.

The primary objective of the 2019-21 round of National Family Health Surveys is to provide essential data on health and family welfare, as well as data on emerging issues in these areas, such as levels of fertility, infant and child mortality, maternal and child health, and other health and family welfare indicators by background characteristics at the national and state levels. Similar to NFHS-4, NFHS-5 also provides information on several emerging issues including perinatal mortality, high-risk sexual behaviour, safe injections, tuberculosis, noncommunicable diseases, and the use of emergency contraception.

The information collected through NFHS-5 is intended to assist policymakers and programme managers in setting benchmarks and examining progress over time in India’s health sector. Besides providing evidence on the effectiveness of ongoing programmes, NFHS-5 data will help to identify the need for new programmes in specific health areas.

The clinical, anthropometric, and biochemical (CAB) component of NFHS-5 is designed to provide vital estimates of the prevalence of malnutrition, anaemia, hypertension, high blood glucose levels, and waist and hip circumference, Vitamin D3, HbA1c, and malaria parasites through a series of biomarker tests and measurements.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-54, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A uniform sample design, which is representative at the national, state/union territory, and district level, was adopted in each round of the survey. Each district is stratified into urban and rural areas. Each rural stratum is sub-stratified into smaller substrata which are created considering the village population and the percentage of the population belonging to scheduled castes and scheduled tribes (SC/ST). Within each explicit rural sampling stratum, a sample of villages was selected as Primary Sampling Units (PSUs); before the PSU selection, PSUs were sorted according to the literacy rate of women age 6+ years. Within each urban sampling stratum, a sample of Census Enumeration Blocks (CEBs) was selected as PSUs. Before the PSU selection, PSUs were sorted according to the percentage of SC/ST population. In the second stage of selection, a fixed number of 22 households per cluster was selected with an equal probability systematic selection from a newly created list of households in the selected PSUs. The list of households was created as a result of the mapping and household listing operation conducted in each selected PSU before the household selection in the second stage. In all, 30,456 Primary Sampling Units (PSUs) were selected across the country in NFHS-5 drawn from 707 districts as on March 31st 2017, of which fieldwork was completed in 30,198 PSUs.

For further details on sample design, see Section 1.2 of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four survey schedules/questionnaires: Household, Woman, Man, and Biomarker were canvassed in 18 local languages using Computer Assisted Personal Interviewing (CAPI).

Cleaning operations

Electronic data collected in the 2019-21 National Family Health Survey were received on a daily basis via the SyncCloud system at the International Institute for Population Sciences, where the data were stored on a password-protected computer. Secondary editing of the data, which required resolution of computer-identified inconsistencies and coding of open-ended questions, was conducted in the field by the Field Agencies and at the Field Agencies central office, and IIPS checked the secondary edits before the dataset was finalized.

Field-check tables were produced by IIPS and the Field Agencies on a regular basis to identify certain types of errors that might have occurred in eliciting information and recording question responses. Information from the field-check tables on the performance of each fieldwork team and individual investigator was promptly shared with the Field Agencies during the fieldwork so that the performance of the teams could be improved, if required.

Response rate

A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98 percent.

In the interviewed households, 747,176 eligible women age 15-49 were identified for individual women’s interviews. Interviews were completed with 724,115 women, for a response rate of 97 percent. In all, there were 111,179 eligible men age 15-54 in households selected for the state module. Interviews were completed with 101,839 men, for a response rate of 92 percent.

The Sudan Demographic and Health Survey (SDHS) was conducted in two phases between November 15, 1989 and May 21, 1990 by the Department of Statistics of the Ministry of Economic and National Planning. The survey collected information on fertility levels, marriage patterns, reproductive intentions, knowledge and use of contraception, maternal and child health, maternal mortality, and female circumcision. The survey findings provide the National Population Committee and the Ministry of Health with valuable information for use in evaluating population policy and planning public health programmes. A total of 5860 ever-married women age 15-49 were interviewed in six regions in northern Sudan; three regions in southern Sudan could not be included in the survey because of civil unrest in that part of the country. The SDHS provides data on fertility and mortality comparable to the 1978-79 Sudan Fertility Survey (SFS) and complements the information collected in the 1983 census. The primary objective of the SDHS was to provide data on fertility, nuptiality, family planning, fertility preferences, childhood mortality, indicators of maternal health care, and utilization of child health services. Additional information was coUected on educational level, literacy, source of household water, and other housing conditions. The SDHS is intended to serve as a source of demographic data for comparison with the 1983 census and the Sudan Fertility Survey (SFS) 1978-79, and to provide population and health data for policymakers and researchers. The objectives of the survey are to: assess the overall demographic situation in Sudan, assist in the evaluation of population and health programmes, assist the Department of Statistics in strengthening and improving its technical skills for conducting demographic and health surveys, enable the National Population Committee (NPC) to develop a population policy for the country, and measure changes in fertility and contraceptive prevalence, and study the factors which affect these changes, and examine the basic indicators of maternal and child health in Sudan. MAIN RESULTS Fertility levels and trends Fertility has declined sharply in Sudan, from an average of six children per women in the Sudan Fertility Survey (TFR 6.0) to five children in the Sudan DHS survey flTR 5.0). Women living in urban areas have lower fertility (TFR 4.1) than those in rural areas (5.6), and fertility is lower in the Khartoum and Northern regions than in other regions. The difference in fertility by education is particularly striking; at current rates, women who have attained secondary school education will have an average of 3.3 children compared with 5.9 children for women with no education, a difference of almost three children. Although fertility in Sudan is low compared with most sub-Saharan countries, the desire for children is strong. One in three currently married women wants to have another child within two years and the same proportion want another child in two or more years; only one in four married women wants to stop childbearing. The proportion of women who want no more children increases with family size and age. The average ideal family size, 5.9 children, exceeds the total fertility rate (5.0) by approximately one child. Older women are more likely to want large families than younger women, and women just beginning their families say they want to have about five children. Marriage Almost all Sudanese women marry during their lifetime. At the time of the survey, 55 percent of women 15-49 were currently married and 5 percent were widowed or divorced. Nearly one in five currently married women lives in a polygynous union (i.e., is married to a man who has more than one wife). The prevalence of polygyny is about the same in the SDHS as it was in the Sudan Fertility Survey. Marriage occurs at a fairly young age, although there is a trend toward later marriage among younger women (especially those with junior secondary or higher level of schooling). The proportion of women 15-49 who have never married is 12 percentage points higher in the SDHS than in the Sudan Fertiliy Survey. There has been a substantial increase in the average age at first marriage in Sudan. Among SDHS. Since age at first marriage is closely associated with fertility, it is likely that fertility will decrease in the future. With marriages occurring later, women am having their first birth at a later age. While one in three women age 45-49 had her first birth before age 18, only one in six women age 20-24 began childbearing prior to age 18. The women most likely to postpone marriage and childbearing are those who live in urban areas ur in the Khartoum and Northern regions, and women with pest-primary education. Breastfeeding and postpartum abstinence Breastfeeding and postpartum abstinence provide substantial protection from pregnancy after the birth uf a child. In addition to the health benefits to the child, breastfeeding prolongs the length of postpartum amenorrhea. In Sudan, almost all women breastfeed their children; 93 percent of children are still being breastfed 10-11 months after birth, and 41 percent continue breastfeeding for 20-21 months. Postpartum abstinence is traditional in Sudan and in the first two months following the birth of a child 90 percent of women were abstaining; this decreases to 32 percent after two months, and to 5 percent at~er one year. The survey results indicate that the combined effects of breastfeeding and postpartum abstinence protect women from pregnancy for an average of 15 months after the birth of a child. Knowledge and use of contraception Most currently married women (71 percent) know at least one method of family planning, and 59 percent know a source for a method. The pill (70 percent) is the most widely known method, followed by injection, female sterilisation, and the IUD. Only 39 percent of women knew a traditional method of family planning. Despite widespread knowledge of family planning, only about one-fourth of ever-married women have ever used a contraceptive method, and among currently married women, only 9 percent were using a method at the time of the survey (6 percent modem methods and 3 percent traditional methods). The level of contraceptive use while still low, has increased from less than 5 percent reported in the Sudan Fertility Survey. Use of family planning varies by age, residence, and level of education. Current use is less than 4 percent among women 15-19, increases to 10 percent for women 30-44, then decreases to 6 percent for women 45-49. Seventeen percent of urban women practice family planning compared with only 4 percent of rural women; and women with senior secondary education are more likely to practice family planning (26 percent) than women with no education (3 percent). There is widespread approval of family planning in Sudan. Almost two-thirds of currently married women who know a family planning method approve of the use of contraception. Husbands generally share their wives's views on family planning. Three-fourths of married women who were not using a contraceptive method at the time of the survey said they did not intend to use a method in the future. Communication between husbands and wives is important for successful family planning. Less than half of currently married women who know a contraceptive method said they had talked about family planning with their husbands in the year before the survey; one in four women discussed it once or twice; and one in five discussed it more than twice. Younger women and older women were less likely to discuss family planning than those age 20 to 39. Mortality among children The neonatal mortality rate in Sudan remained virtually unchanged in the decade between the SDHS and the SFS (44 deaths per 1000 births), but under-five mortality decreased by 14 percent (from 143 deaths per 1000 births to 123 per thousand). Under-five mortality is 19 percent lower in urban areas (117 per 1000 births) than in rural areas (144 per 10(30 births). The level of mother's education and the length of the preceding birth interval play important roles in child survival. Children of mothers with no education experience nearly twice the level of under-five mortality as children whose mother had attained senior secondary or nigher education. Mortality among children under five is 2.7 times higher among children born after an interval of less than 24 months than among children born after interval of 48 months or more. Maternal mortality The maternal mortality rate (maternal deaths per 1000 women years of exposure) has remained nearly constant over the twenty years preceding the survey, while the maternal mortality ratio (number of maternal deaths per 100,000 births), has increased (despite declining fertility). Using the direct method of estimation, the maternal mortality ratio is 352 maternal deaths per 100,000 births for the period 1976-82, and 552 per 100,000 births for the period 1983-89. The indirect estimate for the maternal mortality ratio is 537. The latter estimate is an average of women's experience over an extended period before the survey centred on 1977. Maternal health care The health care mothers receive during pregnancy and delivery is important to the survival and well-being of both children and mothers. The SDHS results indicate that most women in Sudan made at least one antenatal visit to a doctor or trained health worker/midwife. Eighty-seven percent of births benefitted from professional antenatal care in urban areas compared with 62 percent in rural areas. Although the proportion of pregnant mothers seen by trained health workers/midwives are similar in urban and rural areas, doctors provided antenatal care for 42 percent and 19 percent of births in urban and rural areas, respectively. Neonatal tetanus, a major cause of infant deaths in developing countries, can be prevented if mothers receive tetanus toxoid vaccinations.

0. Sections

1. Project
2. Dataset
3. Terms of Use
4. Contents

5. Method and Processing

1. PROJECT

Title: Brain-Computer Music Interface for Monitoring and Inducing Affective States (BCMI-MIdAS) Dates: 2012-2017 Funding organisation: Engineering and Physical Sciences Research Council (EPSRC) Grant no.: EP/J003077/1 and EP/J002135/1.

2. DATASET

EEG data from an affective Music Brain-Computer Interface: system calibration.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2015) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to calibrate an affective brain-computer interface system to induce specific affective states by real-time online modification of synthetic music.

For this purpose, 20 healthy adult volunteers listened to music clips (40 s) targeting two affective states, as defined by valence and arousal (the first 20-s targeted state 1, while the remaining 20-s targeted state 2). Data were recorded over 1 session with 5 runs of 18 music trials each. The music clips were generated using a synthetic music generator.

The dataset contains the electroencephalogram (EEG), galvanic skin response (GSR) and electrocardiogram (ECG) data from 19 healthy adult participants while listening to the music clips, together with the reported affective state (valence and arousal values) and auxiliary variables.

This dataset is connected to 2 additional datasets:

1. EEG data from an affective Music Brain-Computer Interface: offline training to induce target emotional states. doi:
2. EEG data from an affective Music Brain-Computer Interface: online real-time control. doi: Please note that the number of participants varies between datasets; however, participant codes are the same across all three datasets.

Publication Year: 2018

Creators: Nicoletta Nicolaou, Ian Daly

Contributors: Isil Poyraz Bilgin, James Weaver, Asad Malik, Alexis Kirke, Duncan Williams.

Principal Investigator: Slawomir Nasuto(EP/J003077/1).

Co-Investigator: Eduardo Miranda (EP/J002135/1).

Organisation: University of Reading

Rights-holders: University of Reading

Source: The synthetic generator used to generate the music clips was presented in Williams et al., “Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System”, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005

3. TERMS OF USE

Copyright University of Reading, 2018. This dataset is licensed by the rights-holder(s) under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/.

4. CONTENTS

The dataset comprises of data from 19 subjects. The sampling rate is 1 kHz and the music listening task corresponding to a music clip is 40 s long (clip duration). The 40-s music clip is generated in real-time by the music generator, based on the target emotional state (defined by LOW/NEUTRAL/HIGH valence and LOW/NEUTRAL/HIGH arousal).

5. METHOD and PROCESSING

This information is available in the following publications:

[1] Daly, I., Nicolaou, N., Williams, D., Hwang, F., Kirke, A., Miranda, E., Nasuto, S.J., “Neural and physiological data from participants listening to affective music”, Scientific Data, 2018. [2] Daly, I., Williams, D., Hwang, F., Kirke, A., Malik, A., Roesch, E., Weaver, J., Miranda, E. R., Nasuto, S. J., “Identifying music-induced emotions from EEG for use in brain-computer music interfacing”, in Proc. 4th Workshop on Affective Brain-Computer Interfaces at the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015). Xi’an, China, 21-25 September 2015. If you use this dataset in your study please cite these references, as well as the following reference: [3] Williams, D., Kirke, A., Miranda, E.R., Daly, I., Hwang, F., Weaver, J., Nasuto, S.J., “Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System”, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005

Thank you for your interest in our work.

Context

The Programme for International Student Assessment (PISA) is a test given every three years to 15-year-old students from around the world to evaluate their performance in mathematics, reading, and science. This test provides a quantitative way to compare the performance of students from different parts of the world. In this homework assignment, we will predict the reading scores of students from the United States of America on the 2009 PISA exam.

The datasets pisa2009train.csv and pisa2009test.csv contain information about the demographics and schools for American students taking the exam, derived from 2009 PISA Public-Use Data Files distributed by the United States National Center for Education Statistics (NCES). While the datasets are not supposed to contain identifying information about students taking the test, by using the data you are bound by the NCES data use agreement, which prohibits any attempt to determine the identity of any student in the datasets.

Each row in the datasets pisa2009train.csv and pisa2009test.csv represents one student taking the exam. The datasets have the following variables:

Content

grade: The grade in school of the student (most 15-year-olds in America are in 10th grade)

male: Whether the student is male (1/0)

raceeth: The race/ethnicity composite of the student

preschool: Whether the student attended preschool (1/0)

expectBachelors: Whether the student expects to obtain a bachelor's degree (1/0)

motherHS: Whether the student's mother completed high school (1/0)

motherBachelors: Whether the student's mother obtained a bachelor's degree (1/0)

motherWork: Whether the student's mother has part-time or full-time work (1/0)

fatherHS: Whether the student's father completed high school (1/0)

fatherBachelors: Whether the student's father obtained a bachelor's degree (1/0)

fatherWork: Whether the student's father has part-time or full-time work (1/0)

selfBornUS: Whether the student was born in the United States of America (1/0)

motherBornUS: Whether the student's mother was born in the United States of America (1/0)

fatherBornUS: Whether the student's father was born in the United States of America (1/0)

englishAtHome: Whether the student speaks English at home (1/0)

computerForSchoolwork: Whether the student has access to a computer for schoolwork (1/0)

read30MinsADay: Whether the student reads for pleasure for 30 minutes/day (1/0)

minutesPerWeekEnglish: The number of minutes per week the student spend in English class

studentsInEnglish: The number of students in this student's English class at school

schoolHasLibrary: Whether this student's school has a library (1/0)

publicSchool: Whether this student attends a public school (1/0)

urban: Whether this student's school is in an urban area (1/0)

schoolSize: The number of students in this student's school

readingScore: The student's reading score, on a 1000-point scale

Acknowledgements

MITx ANALYTIX

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