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.

Dataset Card: deepseek_geopolitical_bias_dataset

  Dataset Summary

The deepseek_geopolitical_bias_dataset is a collection of geopolitical questions and model responses. It focuses on historical incidents spanning multiple regions (e.g., China, India, Pakistan, Russia, Taiwan, and the USA) and provides an in-depth look at how different Large Language Models (LLMs), including DeepSeek, respond to these sensitive topics. The dataset aims to support research in bias detection… See the full description on the dataset page: https://huggingface.co/datasets/enkryptai/deepseek-geopolitical-bias-dataset.

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

As positive mental health (PMH) has a significant impact on general and mental health, it is an important target for interventions. Cut points are a useful basis for identifying participants with a greater need for such interventions. Representative (n = 9,440) and student (n = 22,833) samples from Germany, Russia, the US, and China were reanalyzed. Two different anchors were used to determine optimal cut points for the PMH-scale: (1) a combined measure of PMH-related questionnaires and (2) the Global Assessment of Functioning Scale (GAF). A kernel-based method to determine optimal cut points and bootstrapping to identify potential cross-cultural differences were used. Acceptable to excellent levels of classification accuracy were found regarding the combined measure (AUCs between .75 and .87) across all samples. Using the GAF resulted in poor discriminatory power (AUC = .69). Optimal cut points varied systematically between countries and samples. Country and sample-specific cut points for the PMH scale should be used to identify individuals with high versus low levels of PMH. Specifically, we suggest using cut points of 21, 22, and 24 in Germany, Russia, and the US, respectively. For student samples, we recommend cut points of 18, 19, and 20 in Germany, Russia, and China, respectively.

Dataset Details

  Dataset Description

This dataset measures soft censorship (selective omission of information) in large language models (LLMs). It contains responses from 14 state-of-the-art LLMs from different regions (Western countries, China, and Russia) when prompted about political figures in all six official UN languages. The dataset is designed to provide insights into how and when LLMs refuse to provide information or selectively omit details when discussing… See the full description on the dataset page: https://huggingface.co/datasets/aida-ugent/llm-censorship.

Context The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion in 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growing more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc.

This study conducts a comparative analysis of how geopolitical risk (GPR) and innovation impact stock returns in the defense industry based on data from 75 defense companies across 17 countries and 4 continents. With daily datasets spanning from January 1, 2014 to March 29, 2024, wavelet coherence and wavelet phase differences were used to conduct the analysis. The results revealed that innovation had a greater and more pronounced impact during the entire analysis period compared with the influence of GPR events. GPRs exerted an uneven and heterogeneous impact on global defense stocks and had a concentrated impact during events that generated uncertainty. Overall, we found significant time-varying dependence across a large number of companies at different time frequencies. The COVID-19 pandemic did not have a major impact on companies in the defense industry. Further, GPR events led to increased volatility during the Russia–Ukraine war, leading to increased uncertainty. In addition to the dominant role they play in the world defense market, US companies served as a robust hedge, especially from 2021 to 2022. Defense companies in the UK are more sensitive to both GPR events and innovation, followed by companies in Germany and France. Comparative analysis of the scalograms of China reveals a greater influence of innovation compared with GPR events. Thus, diversification opportunities have been extended from the defense industry in China, offering investors a promising way to capitalize on refuge opportunities during periods of disruption. To mitigate the global rearmament trend, we suggest alternative investment opportunities for different time horizons.

The Soil and Physiographic Database for Northern and Central Eurasia (1:5 Million Scale) CD-ROM contains reports, databases, and digital maps for North and Central Eurasia region [covering China (including the Taiwan Province of China), Mongolia, and all countries of the former Soviet Union (CIS and Baltic States)]. The database was prepared by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) in co-operation with the Dokuchaiev Institute of Soil Science, Moscow, Russia and the Institute of Soil Science, Academia Sinica, Nanjing, China. The preparation of this database is part of a larger FAO programme that aims to produce a systematic update of the soil and terrain database for the whole world. This effort is supported by a number of international institutes and organizations such as the United Nations Environment Programme (UNEP), the International Soil Resources Information Centre (ISRIC), and the European Soil Bureau (ESB), and is only possible through contribution of national soil institutes worldwide.

The soil information in this database has been derived from several sources, in particular the 1:2.5 Million Soil Map of the Former Soviet Union prepared by Friedland in the Dokuchaiev Institute, Moscow, and the Soil Map of China at 1:4 million scale prepared by the Institute of Soil Science of the Academia Sinica in Nanjing. All soil information has been correlated with the Revised Legend of the Soil Map of the World.

For Mongolia and the countries of the CIS and Baltic States, the physiographic coverage was prepared by the Dokuchaiev Institute according to the principles developed for soil and terrain databases (SOTER) by ISRIC, FAO and UNEP. For China, the physiographic layer was prepared as part of a larger project assessing land degradation in Asia according to the same principles.

For the Russian Federation and China, an integration of the soil and physiographic layer has been carried out; for the other areas, both layers have been prepared separately and no correlation has been attempted.

Apart from selected examples in the report on soils of China, the database does not contain any soil profile description nor soil analytical results. Documentation on the soils of the Russian Federation, the physiography of the CIS and Baltic States, and on soils of China is included on the CD-ROM.

The results presented on the CD-ROM are in part provisional, as efforts are under way to produce a full SOTER database for Eastern Europe at 1:2.5 million scale. This will improve the information available for the European part of the Russian Federation, Ukraine, and Belarus (Byelarus). In addition, more recent and more detailed regional soil information exists on the Russian Federation and on China.

Soil and Physiographic Database for North and Central Eurasia at 1:5 Million Scale is provided on CD-ROM by the FAO, Land and Water Digital Media Series (Number 7). The CD-ROM can be purchased (Price: US$40) from FAO, Sales and Marketing Group, Viale delle Terme di Caracalla 0100 Rome, Italy (Fax: +39-06-5705-3360 E-mail: publications-sales@fao.org).

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

Abstract

Gallup Worldwide Research continually surveys residents in more than 150 countries, representing more than 98% of the world's adult population, using randomly selected, nationally representative samples. Gallup typically surveys 1,000 individuals in each country, using a standard set of core questions that has been translated into the major languages of the respective country. In some regions, supplemental questions are asked in addition to core questions. Face-to-face interviews are approximately 1 hour, while telephone interviews are about 30 minutes. In many countries, the survey is conducted once per year, and fieldwork is generally completed in two to four weeks. The Country Dataset Details spreadsheet displays each country's sample size, month/year of the data collection, mode of interviewing, languages employed, design effect, margin of error, and details about sample coverage.

Gallup is entirely responsible for the management, design, and control of Gallup Worldwide Research. For the past 70 years, Gallup has been committed to the principle that accurately collecting and disseminating the opinions and aspirations of people around the globe is vital to understanding our world. Gallup's mission is to provide information in an objective, reliable, and scientifically grounded manner. Gallup is not associated with any political orientation, party, or advocacy group and does not accept partisan entities as clients. Any individual, institution, or governmental agency may access the Gallup Worldwide Research regardless of nationality. The identities of clients and all surveyed respondents will remain confidential.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING AND DATA COLLECTION METHODOLOGY With some exceptions, all samples are probability based and nationally representative of the resident population aged 15 and older. The coverage area is the entire country including rural areas, and the sampling frame represents the entire civilian, non-institutionalized, aged 15 and older population of the entire country. Exceptions include areas where the safety of interviewing staff is threatened, scarcely populated islands in some countries, and areas that interviewers can reach only by foot, animal, or small boat.

Telephone surveys are used in countries where telephone coverage represents at least 80% of the population or is the customary survey methodology (see the Country Dataset Details for detailed information for each country). In Central and Eastern Europe, as well as in the developing world, including much of Latin America, the former Soviet Union countries, nearly all of Asia, the Middle East, and Africa, an area frame design is used for face-to-face interviewing.

The typical Gallup Worldwide Research survey includes at least 1,000 surveys of individuals. In some countries, oversamples are collected in major cities or areas of special interest. Additionally, in some large countries, such as China and Russia, sample sizes of at least 2,000 are collected. Although rare, in some instances the sample size is between 500 and 1,000. See the Country Dataset Details for detailed information for each country.

FACE-TO-FACE SURVEY DESIGN

FIRST STAGE In countries where face-to-face surveys are conducted, the first stage of sampling is the identification of 100 to 135 ultimate clusters (Sampling Units), consisting of clusters of households. Sampling units are stratified by population size and or geography and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size, otherwise simple random sampling is used. Samples are drawn independent of any samples drawn for surveys conducted in previous years.

There are two methods for sample stratification:

METHOD 1: The sample is stratified into 100 to 125 ultimate clusters drawn proportional to the national population, using the following strata: 1) Areas with population of at least 1 million 2) Areas 500,000-999,999 3) Areas 100,000-499,999 4) Areas 50,000-99,999 5) Areas 10,000-49,999 6) Areas with less than 10,000

The strata could include additional stratum to reflect populations that exceed 1 million as well as areas with populations less than 10,000. Worldwide Research Methodology and Codebook Copyright © 2008-2012 Gallup, Inc. All rights reserved. 8

METHOD 2:

A multi-stage design is used. The country is first stratified by large geographic units, and then by smaller units within geography. A minimum of 33 Primary Sampling Units (PSUs), which are first stage sampling units, are selected. The sample design results in 100 to 125 ultimate clusters.

SECOND STAGE

Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day, and where possible, on different days. If an interviewer cannot obtain an interview at the initial sampled household, he or she uses a simple substitution method. Refer to Appendix C for a more in-depth description of random route procedures.

THIRD STAGE

Respondents are randomly selected within the selected households. Interviewers list all eligible household members and their ages or birthdays. The respondent is selected by means of the Kish grid (refer to Appendix C) in countries where face-to-face interviewing is used. The interview does not inform the person who answers the door of the selection criteria until after the respondent has been identified. In a few Middle East and Asian countries where cultural restrictions dictate gender matching, respondents are randomly selected using the Kish grid from among all eligible adults of the matching gender.

TELEPHONE SURVEY DESIGN

In countries where telephone interviewing is employed, random-digit-dial (RDD) or a nationally representative list of phone numbers is used. In select countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day. Appointments for callbacks that fall within the survey data collection period are made.

PANEL SURVEY DESIGN

Prior to 2009, United States data were collected using The Gallup Panel. The Gallup Panel is a probability-based, nationally representative panel, for which all members are recruited via random-digit-dial methodology and is only used in the United States. Participants who elect to join the panel are committing to the completion of two to three surveys per month, with the typical survey lasting 10 to 15 minutes. The Gallup Worldwide Research panel survey is conducted over the telephone and takes approximately 30 minutes. No incentives are given to panel participants. Worldwide Research Methodology and Codebook Copyright © 2008-2012 Gallup, Inc. All rights reserved. 9

Research instrument

QUESTION DESIGN

Many of the Worldwide Research questions are items that Gallup has used for years. When developing additional questions, Gallup employed its worldwide network of research and political scientists1 to better understand key issues with regard to question development and construction and data gathering. Hundreds of items were developed, tested, piloted, and finalized. The best questions were retained for the core questionnaire and organized into indexes. Most items have a simple dichotomous ("yes or no") response set to minimize contamination of data because of cultural differences in response styles and to facilitate cross-cultural comparisons.

The Gallup Worldwide Research measures key indicators such as Law and Order, Food and Shelter, Job Creation, Migration, Financial Wellbeing, Personal Health, Civic Engagement, and Evaluative Wellbeing and demonstrates their correlations with world development indicators such as GDP and Brain Gain. These indicators assist leaders in understanding the broad context of national interests and establishing organization-specific correlations between leading indexes and lagging economic outcomes.

Gallup organizes its core group of indicators into the Gallup World Path. The Path is an organizational conceptualization of the seven indexes and is not to be construed as a causal model. The individual indexes have many properties of a strong theoretical framework. A more in-depth description of the questions and Gallup indexes is included in the indexes section of this document. In addition to World Path indexes, Gallup Worldwide Research questions also measure opinions about national institutions, corruption, youth development, community basics, diversity, optimism, communications, religiosity, and numerous other topics. For many regions of the world, additional questions that are specific to that region or country are included in surveys. Region-specific questions have been developed for predominantly Muslim nations, former Soviet Union countries, the Balkans, sub-Saharan Africa, Latin America, China and India, South Asia, and Israel and the Palestinian Territories.

The questionnaire is translated into the major conversational languages of each country. The translation process starts with an English, French, or Spanish version, depending on the region. One of two translation methods may be used.

METHOD 1: Two independent translations are completed. An independent third party, with some knowledge of survey research methods, adjudicates the differences. A professional translator translates the final version back into the source language.

METHOD 2: A translator

Nexdata has off-the-shelf 35,000 hours Multilingual Language Data of 16kHz conversational speech, covering 100+ countries including English, German, French, Spanish, Italian, Portuguese, Korean, Japanese, Hindi, Russia and etc.

Representative single stage or multi-stage sampling of the adult population of the country 18 years old and older was used for the EVS 2017. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. 8 countries out of 16 deviated from the guidelines and planned with an effective sample size below the set threshold. Germany, Netherlands, Iceland and Switzerland, due to the mixed mode design, allocated only part (50% or more) of the effective sample size to the interviewer-administered mode. Sample design and other relevant information about sampling were reviewed by the EVS-Methodology Group (EVS-MG) and approved prior to contracting of fieldwork agency or starting of data collection. In case of on-field sampling EVS-MG proposed necessary protocols for documentation of the probabilities of selection of each respondent. The sampling was documented using the Sampling Design Form (SDF) delivered by the national teams (see the EVS2017 Methodological Guidelines, Sampling). The SDF includes the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it includes the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights. WVS 7: The sampling procedures differ from country to country: Probability Sample: Multistage Sample Probability Sample: Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2020. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are required to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines and planned with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. World Values Survey Interview Mode of collection: mixed mode. Face-to-face interview: CAPI (Computer Assisted Personal Interview). Face-to-face interview: PAPI (Paper and Pencil Interview). Telephone interview: CATI (Computer Assisted Telephone Interview). Self-administered questionnaire: CAWI (Computer-Assisted Web Interview). Self-administered questionnaire: Paper. In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS scientific advisory committee and WVSA secretariat. The main data collection mode in WVS 2017-2022 is face to face (interviewer-administered). Several countries employed self-administered interview or mixed-mode approach to data collection: Australia (CAWI & postal survey); Canada (CAWI); Hong Kong SAR (PAPI & CAWI); Malaysia (CAWI & PAPI); Netherlands (CAWI); USA (CAWI & CATI). The WVS Master Questionnaire was provided in English, Arabic, Russian and Spanish. Each national survey team had to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. WVSA Secretariat and Data archive monitored the translation process; every translation is subject to multi-stage validation procedure before the fieldwork can be started. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country/ territory] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). Research area: Andorra (AD); Argentina (AR); Armenia (AM); Australia (AU); Bangladesh (BD); Bolivia (BO); Brazil (BR); Canada (CA); Colombia (CO); Chile (CL); China (CN); Cyprus (CY); Ecuador (EC); Egypt (EG); Ethiopia (ET); Germany (DE); Greece (GR); Guatemala (GT); Hong Kong SAR PRC (HK); Indonesia (ID); Iran (IR); Iraq (IQ); Japan (JP); Jordan (JO); Kazakhstan (KZ); Kenya (KE); Kyrgyzstan (KG); Lebanon (LB); Libya (LY); Macao SAR PRC (MO); Malaysia (MY); Maldives (MV); Mexico (MX); Mongolia (MN); Morocco (MA); Myanmar (MM); Netherlands (NL); New Zealand (NZ); Nicaragua (NI); Nigeria (NG); Pakistan (PK); Peru (PE); Philippines (PH); Puerto Rico (PR); Romania (RO); Russia (RU); Serbia (RS); Singapore (SG); South Korea (KR); Taiwan ROC (TW); Tajikistan (TJ); Thailand (TH); Tunisia (TN); Turkey (TR); Ukraine (UA); United States (US); Venezuela (VE); Vietnam (VN); Zimbabwe (ZW). The sampling procedures differ from country to country; probability sample: Multistage Sample, Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2021. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are requirred to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

Permutable AI’s BRICS datasets deliver structured quantitative analytics across Brazil, Russia, India, China, and South Africa. Using proprietary large language models, our platform transforms multilingual news into real-time sentiment scores for macroeconomic data releases, fiscal policy changes, and political developments. Systematic data feeds capture GDP announcements, central bank decisions, and budgetary measures with five-minute refresh rates. Geopolitical modules quantify elections, sanctions, and trade policy coordination, while natural disaster tracking provides supply chain disruption scoring across critical commodities. With a decade of historical datasets for backtesting emerging market cycles, our Co-Pilot API enables seamless integration with quantitative trading strategies through millisecond-latency sentiment classifications.

Please find my Tableau viz for this dataset here: https://public.tableau.com/app/profile/jamie.collins5558/viz/CentralBankReserves/Dashboard1 Feel free to copy, or use as a template/inspiration for your own visualisations.

This dataset provides a comprehensive snapshot of central bank reserves, including foreign exchange (FX) reserves, total reserves, and gold holdings, for 165 countries. It includes detailed metrics such as gold reserves in tonnes and millions (USD), the percentage of total reserves held in gold, and the 20-year change in gold holdings. The dataset also categorises countries by region and economic grouping (e.g., high income, upper middle income, lower middle income, low income), offering a valuable resource for analysing global financial trends, reserve management strategies, and the role of gold in national economies.

• Country: The name of the country (e.g., Afghanistan, United States of America).
• Region: The geographical region of the country (e.g., Central Asia, Western Europe, Latin America & Caribbean).
• Economic grouping: The World Bank income classification of the country (e.g., High income, Upper middle income, - - -- Lower middle income, Low income).
• FX Reserves: Foreign exchange reserves in millions of USD (e.g., 68448.33 for Algeria). Some values are marked as "AWAITED" where data is unavailable.
• Total Reserves: Total reserves (including FX and gold) in millions of USD (e.g., 83007.11 for Algeria). Some values are marked as "AWAITED."
• Gold Reserves Tonnes: Gold reserves held by the central bank in metric tonnes (e.g., 173.56 for Algeria). Some values are marked as "AWAITED."
• Gold Reserves Millions: The value of gold reserves in millions of USD (e.g., 14558.78 for Algeria). Some values are marked as "AWAITED."
• Holdings %: The percentage of total reserves held in gold (e.g., 17.54 for Algeria). Some values are marked as "AWAITED."
• 20yr change: The change in gold holdings (in tonnes) over the past 20 years (e.g., -0.09 for Algeria). Positive values indicate an increase, while negative values indicate a decrease.

Key Statistics Countries Covered: 165 - Regions Represented: Includes Central Asia, Western Europe, Latin America & Caribbean, Middle East & North Africa, Sub-Saharan Africa, South East Asia, East Asia, South Asia, Australasia / Oceania, and North America. - Economic Groupings: High income (e.g., United States, Japan), Upper middle income (e.g., Brazil, China), Lower middle income (e.g., India, Egypt), and Low income (e.g., Afghanistan, Haiti). - Largest Gold Reserves: The United States holds the largest gold reserves at 8,133.46 tonnes, valued at $682,276.85 million, accounting for 74.97% of its total reserves. - Highest Gold Holdings %: Bolivia has the highest percentage of reserves in gold at 95.59%, despite holding only 22.53 tonnes. - Largest 20-Year Increase in Gold: The Russian Federation increased its gold holdings by 1,945.79 tonnes over 20 years, followed by China with a 1,684.55-tonne increase. Potential Use Cases

This dataset is ideal for a variety of analytical and research purposes, including:

• Economic Analysis: Investigate the relationship between a country’s economic grouping and its reserve composition, particularly the reliance on gold versus foreign exchange.
• Financial Stability Studies: Analyse how countries with higher gold holdings percentages (e.g., Bolivia, Uzbekistan) manage financial stability compared to those with lower percentages (e.g., Chile, South Korea).
• Historical Trends: Use the 20-year change in gold holdings to study trends in reserve management strategies, such as China and Russia’s significant increases in gold reserves.
• Geopolitical Insights: Explore how regions like Central Asia (e.g., Kazakhstan, Uzbekistan) or Middle East & North Africa (e.g., Qatar, Saudi Arabia) differ in their reserve strategies, potentially reflecting geopolitical priorities.
• Data Visualisation: Create maps, bar charts, or scatter plots to visualise global gold reserves, regional differences, or the correlation between income levels and gold holdings. Notes for Users
• Missing Data: Some countries have "AWAITED" in place of numerical values for FX reserves, total reserves, gold reserves, and holdings percentages. Users may need to handle these missing values (e.g., by excluding them, imputing values, or sourcing additional data).
• Gold Valuation: The "Gold Reserves Millions" column reflects the value of gold reserves in USD, based on the gold price as of 2024. Users should note that gold prices fluctuate, and historical comparisons may require adjustment for price changes.
• 20-Year Change: The "20yr change" column provides the change in gold holdings in tonnes from 2005 to 2025. Negative values indicate a reduction in gold reserves (e.g., Switzerland reduced by 314.35 tonnes), while positive values indicate an increase (e.g., India increased by 521.31 tonnes).

The Crops Dataset contains nineteen variables which represent different crops sown in China. For each crop (variable) the number of hectares of that crop sown are given. The following crops are represented: Cereal Grains, Corn, Cotton, Double Season Rice, Green Manure, Potatoes, Rapeseed, Rice and Rapeseed, Single Season Rice, Spring Wheat, Sorghum, Soybeans, Sugarbeets, Sugarcane, Tobacco, Vegetables, Winter Wheat, Winter Wheat and Corn, Winter Wheat and Rice.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

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Data Catalog Market Size 2025-2029

The data catalog market size is valued to increase USD 5.03 billion, at a CAGR of 29.5% from 2024 to 2029. Rising demand for self-service analytics will drive the data catalog market.

Major Market Trends & Insights

North America dominated the market and accounted for a 39% growth during the forecast period.
By Component - Solutions segment was valued at USD 822.80 billion in 2023
By Deployment - Cloud segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 554.30 million
Market Future Opportunities: USD 5031.50 million
CAGR : 29.5%
North America: Largest market in 2023

Market Summary

The market is a dynamic and evolving landscape, driven by the increasing demand for self-service analytics and the rise of data mesh architecture. Core technologies, such as metadata management and data discovery, play a crucial role in enabling organizations to effectively manage and utilize their data assets. Applications, including data governance and data integration, are also seeing significant growth as businesses seek to optimize their data management processes.
However, maintaining catalog accuracy over time poses a challenge, with concerns surrounding data lineage, data quality, and data security. According to recent estimates, the market is expected to account for over 30% of the overall data management market share by 2025, underscoring its growing importance in the digital transformation era.

What will be the Size of the Data Catalog Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Data Catalog Market Segmented and what are the key trends of market segmentation?

The data catalog industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Deployment

  Cloud
  On-premises


Type

  Technical metadata
  Business metadata
  Operational metadata


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

Data catalog solutions have gained significant traction in today's data-driven business landscape, addressing complexities in data discovery, governance, collaboration, and data lifecycle management. These solutions enable users to search and discover relevant datasets for analytical or reporting purposes, thereby reducing the time spent locating data, promoting data reuse, and ensuring the usage of appropriate datasets for specific tasks. Centralized metadata storage is a key feature of data catalog solutions, offering detailed information about datasets, including source, schema, data quality, lineage, and other essential attributes. This metadata-centric approach enhances understanding of data assets, supports data governance initiatives, and provides users with the necessary context for effective data utilization.

Data catalog solutions also facilitate semantic enrichment, data versioning, data security protocols, data access control, and data model design. Semantic enrichment adds meaning and context to data, making it easier to understand and use. Data versioning ensures that different versions of datasets are managed effectively, while data access control restricts access to sensitive data. Data model design helps create an accurate representation of data structures and relationships. Moreover, data catalog solutions offer data discovery tools, data lineage tracking, data governance policies, schema management, data lake management, ETL process optimization, and data quality monitoring. Data discovery tools help users locate relevant data quickly and efficiently.

Data lineage tracking enables users to trace the origin and movement of data throughout its lifecycle. Data governance policies ensure compliance with regulatory requirements and organizational standards. Schema management maintains the structure and consistency of data, while data lake management simplifies the management of large volumes of data. ETL process optimization improves the efficiency of data integration, and data quality monitoring ensures that data is accurate and reliable. Businesses across various sectors, including healthcare, finance, retail, and manufacturing, are increasingly adopting data catalog solutions to streamline their data management and analytics processes. According to recent studies, the adoption of data catalog solutions has grown by approximately 25%, with an estimated 30% of organizations planning to implement t

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

Nuclear energy – alongside hydropower – is one of our oldest low-carbon energy technologies.

Nuclear power generation has been around since the 1960s but saw massive growth globally in the 1970s, 80s, and 90s. In the interactive chart shown, we see how global nuclear generation has changed over the past half-century.

Following fast growth during the 1970s to 1990s, the global generation has slowed significantly. In addition, we see a sharp dip in nuclear output following the Fukushima tsunami in Japan in 2011 as countries took plants offline due to safety concerns.

But we also see that in recent years, production has once again increased.

The global trend in nuclear energy generation masks the significant differences in its role at the country level.

Some countries get no energy from nuclear – or are aiming to eliminate it – while others get the majority of their power from it.

This dataset shows the amount of nuclear energy generated by the country. France, the USA, China, Russia, and Canada all produce relatively large amounts of atomic power.