This dataset provides values for LITERACY RATE ADULT TOTAL PERCENT OF PEOPLE AGES 15 AND ABOVE WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset offers a detailed comparison of key global players like USA, Russia, China, India, Canada, Australia, and others across various economic, social, and environmental metrics. By comparing countries on indicators such as GDP, population, healthcare access, education levels, internet penetration, military spending, and much more, this dataset provides valuable insights for researchers, policymakers, and analysts.

🔍 Key Comparisons:

Economic Indicators: GDP, inflation rates, unemployment rates, etc. Social Indicators: Literacy rates, healthcare quality, life expectancy, etc. Environmental Indicators: CO2 emissions, renewable energy usage, protected areas, etc. Technological Advancements: Internet users, mobile subscriptions, tech exports, etc. Military Spending: Defense budgets, military personnel numbers, etc. This dataset is perfect for those who want to compare countries in terms of development, growth, and global standing. It can be used for data analysis, policy planning, research, and even education.

✨ Key Features:

Comprehensive Coverage: Includes multiple countries with key metrics. Multiple Domains: Economic, social, environmental, technological, and military data. Up-to-date Information: Covers data from the last decade to provide recent insights. Research Ready: Suitable for academic research, visualizations, and analysis.

Context

The dataset is published for helping Kiva to make their descions.

Content

These datasets contain the percentage of youth, unemployment and literacy in different countries.

Acknowledgements

This dataset is collected from Wikipedia.

Inspiration

1. Which country needs more help from Kiva?

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

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).

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

Objective: The PIAAC 2012 study was the first fully computer-based large scale assessment in education. During the assessment, user interactions were logged automatically. This means that most of the users’ actions within the assessment tool were recorded and stored with time stamps in separate files called log files. The log files contain paradata for each participant in the domains literacy, numeracy, and problem solving in technology-rich environments. The availability of these log files offers new opportunities to researchers, for instance to reproduce test-taking behavior of individuals and to better understand test-taking behavior.

Method: PIAAC 2012 was conducted August 2011-November 2012 among a representative international sample of around 166000 adults within 24 different countries. The following dataset includes the log files from 17 countries. Each country was allowed to choose their own sampling technique as long as the technique applies full selection probability methods to select a representative sample from the PIAAC target population. The countries were able to oversample particular subgroups of the target population. Persons aged 55-65 and recent immigrants were oversampled in Denmark and persons aged 19-26 were oversampled in Poland. The administration of the background questionnaires was conducted face-to-face using computer assisted personal interviewing (CAPI). After the questionnaire, the respondent completed a computer-based or paper-based cognitive under the supervision of the interviewer in one or two of the following competence domains: literacy, numeracy and problem solving in technology-rich environments.

Variables: With the help of the PIAAC LogDataAnalyzer you can generate a data set. The Log Data Extraction software is a self-contained system that manages activities like data extraction, data cleaning, and visualization of OECD-PIAAC 2012 assessment log data files. It serves as a basis for data related analysis tasks using the tool itself or by exporting the cleaned data to external tools like statistics packages. You can generate the following Variables: Number of Using Cancel Button, Number of Using Help Menu, Time on Task, Time Till the First Interaction, Final Response, Number of Switching Environment, Sequence of Switching Environment, Number of Highlight Events, Time Since Last Answer Interaction, Number of Created Emails, Sequence of Viewed Emails, Number of Different Email Views, Number of Revisited Emails, Number of Email Views, Sequence of Visited Webpages, Time-Sequence of Spent Time on Webpages, Number of Different Page Visits, Number of Page Visits, Number of Page Revisits.

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10. For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

The dataset of global Skills-in-Literacy Adjusted Mean Years of Schooling (SLAMYS) provides the indicator for 185 countries, by gender and three broad age groups (20-64; 20-39; 40-64) presented in five-year steps from 1970 to 2025. This dataset is an extention and update of Lutz et al. (2021) which included estimates until 2020 and for working age population (age 20-64) only. This new dataset allows for more nuanced analyses of gender-specific trends and generational shifts in skill formation, with particular attention to younger adult populations. The dataset is based on more up to date survey data, including the most recent OECD’s Programme for the International Assessment of Adult Competencies Cycle 2 data (PIAAC, 2023), most recent the Demographic and Health Survey (DHS), and Multiple Indicator Cluster Surveys (MICS). It also uses more recent mean years of schooling (MYS) which are sourced from the most recent Wittgenstein Centre Human Capital Data Explorer, version 3 (K. C. et al., 2024; , https://dataexplorer.wittgensteincentre.org/wcde-v3). Estimates for 2020 and 2025 correspond to the medium scenario (SSP2) of the 2023 update (v15) of the Wittgenstein Centre’s Human Capital Projections (K.C. et al 2024). MYS values for the period 1970–2015 are based on a historical reconstruction (KC et al. 2025) that is fully consistent with the SSP2 scenario. Additionally, estimates of educational attainment distributions by sex and age for all 185 countries—used as covariates in the prediction models—are drawn from the same sources and are fully aligned with the MYS values. See the attached technical documentation for more details.

The dataset contains output data files including technical variables (MYS, SAFs) and a technical documentation. The documentation describes calculation steps, data structures, and includes illustrative examples to guide the interpretation of the main output variables.

This dataset consists of the following files:

Dataset: SLAMYS_2025_v1.csv

The csv file includes the following variables:

country_code (3-numeric ISO code, UN standard)

country_name

year (year in five year steps, 1970-2025)

age_group (20-64, 20-39, 40-64)

gender (Female, Male, Both)

mys (mean years of schooling)

saf (skill adjustment factor)

slamys (skills-in-literacy adjusted mean years of schooling)

calculation (predicted values == 1, empirical values == 0)

source (survey name for empirical values, NA for predicted values)

Data sources: SLAMYS_data-source-documentation_v1.csv

Documentation and methodology: L4S_deliverable D2.4 Skills adjusted global human capital dataset_2025-08-22_final.pdf

R codes: https://github.com/clreiter/Skills-in-Literacy-Adjusted-Human-Capital-Dataset

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

With almost 40 million inhabitants and a diverse geography that encompasses the Andes mountains, glacial lakes, and the Pampas grasslands, Argentina is the second largest country (by area) and has one of the largest economies in South America. It is politically organized as a federation of 23 provinces and an autonomous city, Buenos Aires.

Content

We will analyze ten economic and social indicators collected for each province. Because these indicators are highly correlated, we will use principal component analysis (PCA) to reduce redundancies and highlight patterns that are not apparent in the raw data. After visualizing the patterns, we will use k-means clustering to partition the provinces into groups with similar development levels.

These results can be used to plan public policy by helping allocate resources to develop infrastructure, education, and welfare programs.

Acknowledgements

DataCamp

This first volume of PISA 2012 results summarises the performance of students in PISA 2012. It describes how performance is defined, measured and reported, and then provides results from the assessment, showing what students are able to do in mathematics. After a summary of mathematics performance, it examines the ways in which this performance varies on subscales representing different aspects of mathematics literacy. Given that any comparison of the outcomes of education systems needs to take into consideration countries’ social and economic circumstances, and the resources they devote to education, the volume also presents the results within countries’ economic and social contexts. In addition, the volume examines the relationship between the frequency and intensity of students’ exposure to subject content in school, what is known as “opportunity to learn”, and student performance. The volume concludes with a description of student results in reading and science. Trends in student performance in mathematics between 2003 and 2012, in reading between 2000 and 2012, and in science between 2006 and 2012 are examined when comparable data are available. Throughout the volume, case studies examine in greater detail the policy reforms adopted by countries that have improved in PISA.

The Trends in International Mathematics and Science Study, 2015 (TIMSS 2015) is a data collection that is part of the Trends in International Mathematics and Science Study (TIMSS) program; program data are available since 1999 at . TIMSS 2015 (https://nces.ed.gov/timss/) is a cross-sectional study that provides international comparative information of the mathematics and science literacy of fourth-, eighth-, and twelfth-grade students and examines factors that may be associated with the acquisition of math and science literacy in students. The study was conducted using direct assessments of students and questionnaires for students, teachers, and school administrators. Fourth-, eighth-, and twelfth-graders in the 2014-15 school year were sampled. Key statistics produced from TIMSS 2015 provide reliable and timely data on the mathematics and science achievement of U.S. students compared to that of students in other countries. Data are expected to be released in 2018.

Individual and community-level factors associated with women’s accessing healthcare among women in LMICs.

This repository contains the raw data used for a research study that examined university students' music listening habits while studying. There are two experiments in this research study. Experiment 1 is a retrospective survey, and Experiment 2 is a mobile experience sampling research study. This repository contains five Microsoft Excel files with data obtained from both experiments. The files are as follows:

onlineSurvey_raw_data.xlsx esm_raw_data.xlsx esm_music_features_analysis.xlsx esm_demographics.xlsx index.xlsx Files Description File: onlineSurvey_raw_data.xlsx This file contains the raw data from Experiment 1, including the (anonymised) demographic information of the sample. The sample characteristics recorded are:

studentship area of study country of study type of accommodation a participant was living in age self-identified gender language ability (mono- or bi-/multilingual) (various) personality traits (various) musicianship (various) everyday music uses (various) music capacity The file also contains raw data of responses to the questions about participants' music listening habits while studying in real life. These pieces of data are:

likelihood of listening to specific (rated across 23) music genres while studying and during everyday listening. likelihood of listening to music with specific acoustic features (e.g., with/without lyrics, loud/soft, fast/slow) music genres while studying and during everyday listening. general likelihood of listening to music while studying in real life. (verbatim) responses to participants' written responses to the open-ended questions about their real-life music listening habits while studying. File: esm_raw_data.xlsx This file contains the raw data from Experiment 2, including the following variables:

information of the music tracks (track name, artist name, and if available, Spotify ID of those tracks) each participant was listening to during each music episode (both while studying and during everyday-listening) level of arousal at the onset of music playing and the end of the 30-minute study period level of valence at the onset of music playing and the end of the 30-minute study period specific mood at the onset of music playing and the end of the 30-minute study period whether participants were studying their location at that moment (if studying) whether they were studying alone (if studying) the types of study tasks (if studying) the perceived level of difficulty of the study task whether participants were planning to listen to music while studying (various) reasons for music listening (various) perceived positive and negative impacts of studying with music Each row represents the data for a single participant. Rows with a record of a participant ID but no associated data indicate that the participant did not respond to the questionnaire (i.e., missing data). File: esm_music_features_analysis.xlsx This file presents the music features of each recorded music track during both the study-episodes and the everyday-episodes (retrieved from Spotify's "Get Track's Audio Features" API). These features are:

energy level loudness valence tempo mode The contextual details of the moments each track was being played are also presented here, which include:

whether the participant was studying their location (e.g., at home, cafe, university) whether they were studying alone the type of study tasks they were engaging with (e.g., reading, writing) the perceived difficulty level of the task File: esm_demographics.xlsx This file contains the demographics of the sample in Experiment 2 (N = 10), which are the same as in Experiment 1 (see above). Each row represents the data for a single participant. Rows with a record of a participant ID but no associated demographic data indicate that the participant did not respond to the questionnaire (i.e., missing data). File: index.xlsx Finally, this file contains all the abbreviations used in each document as well as their explanations.

EG: Gender Parity Index (GPI): Literacy Rate: Youth Aged 15-24 data was reported at 0.965 Ratio in 2013. This records an increase from the previous number of 0.932 Ratio for 2012. EG: Gender Parity Index (GPI): Literacy Rate: Youth Aged 15-24 data is updated yearly, averaging 0.903 Ratio from Dec 1976 (Median) to 2013, with 8 observations. The data reached an all-time high of 0.965 Ratio in 2013 and a record low of 0.605 Ratio in 1976. EG: Gender Parity Index (GPI): Literacy Rate: Youth Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank: Education Statistics. Gender parity index for youth literacy rate is the ratio of females to males ages 15-24 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).

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.

Literacy Rate: Kerala data was reported at 94.000 % in 12-01-2011. This records an increase from the previous number of 90.860 % for 12-01-2001. Literacy Rate: Kerala data is updated decadal, averaging 78.850 % from Dec 1951 (Median) to 12-01-2011, with 7 observations. The data reached an all-time high of 94.000 % in 12-01-2011 and a record low of 47.180 % in 12-01-1951. Literacy Rate: Kerala 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.

In recent years, ocean literacy has become a global movement that connects the human dimension to the ocean and intends to be an incentive for positive change in people’s behavior. As multiple initiatives on ocean literacy have arisen, a comprehensive understanding of this topic is required to better engage the broader society. In the present study, we applied a combination of bibliometric analysis and science mapping to a dataset of scientific publications on ocean literacy between 2005 and 2019, obtained from Web of Science and Scopus databases. In order to represent the development of the field, analyze the level of collaborations and uncover its thematic areas, we first used bibliometric analyses to describe the field’s main features, including indicators of growth and research collaboration. We then used science mapping techniques to build collaboration networks among countries and institutions, and to identify research communities. Lastly, we performed co-word analysis to reveal the underlying thematic areas and their evolution. Our results reveal a slow-growing number of publications and a promising trend for collaboration among authors, countries and institutions. Education and science were identified as the two major thematic areas on ocean literacy showing that, over time, issues related to these themes have gained more attention among researchers. These findings confirm that ocean literacy is gaining more acknowledgment within the scientific community but still faces considerable limitations to its dissemination in sectors like the blue economy and in regions such as Latin America and Africa. Promoting cross-institutional and cross-disciplinary cooperation among research institutions, marine education networks and the industry is critical to support this purposeful movement and represents an urgent challenge.