In 2023, it was estimated that social forum and news aggregator Reddit saw over 26.5 million U.S. dollars in revenues from global paying users with an annual subscription. A premium Reddit subscription comes with an ad-free environment, as well as the possibility to join premium subreddits such as r/lounge. In 2022, Reddit counted approximately 530 thousand paying users. By 2026, Reddit annual subscription revenues are estimated to bring in 36.5 million U.S. dollars in revenues.

According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
Data Annotation category is the fastest growing segment of the AI Training Dataset Market

Market Dynamics of AI Training Dataset Market

Key Drivers for AI Training Dataset Market

Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

Restraint Factor for the AI Training Dataset Market

Data Privacy Regulations Impeding AI Training Dataset Market Growth

Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

              TikTok interactions: is there a magic formula for content success?

              In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
              The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
              It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.

              What’s trending on TikTok Shop?

              Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
              TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
              accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.

Abstract

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

       The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
       - Sample Size by Country, Area and Consumption Segment (Number of Households)
       - Population 2010 by Country, Area and Consumption Segment
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
       - Population 2010 by Country, Age Group, Sex and Consumption Segment
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
       - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
       - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

Geographic coverage notes

For all countries, estimates are provided at the national level and at the urban/rural levels. For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1): - Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins - India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal - South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape

Kind of data

Data derived from survey microdata

The SDG Indicator 7.1.1: Access to Electricity, 2023 Release data set, part of the Sustainable Development Goal Indicators (SDGI) collection, measures the proportion of the population with access to electricity for a given statistical area. UN SDG 7 is "ensure access to affordable, reliable, sustainable and modern energy for all". Tracking SDG 7: The Energy Progress Report estimated that in 2019, 759 million people around the world lacked access to electricity. Moreover, due to current policies and the detrimental effects of the COVID-19 crisis, it is predicted that by 2030, 660 million people will still not have access to electricity, with a majority of these people residing in Sub-Saharan Africa. As one measure of progress towards SDG 7, the UN agreed upon SDG indicator 7.1.1. The indicator was computed as the proportion of WorldPop gridded population located within illuminated areas defined by annual VIIRS Nighttime Lights Version 2 (VNL V2) data. The SDG indicator 7.1.1 data set provides estimates for the proportion of population with access to electricity for 206 countries and 45,979 level 2 subnational Units. The data set is available at both national and level 2 subnational resolutions.

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

Total external debt is debt owed to non residents repayable in foreign currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private non-guaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in million current U.S. dollars. This Data set uses 0 = no value, however the original data source uses -9999 as its original value. Data was found online at http://geodata.grid.unep.ch

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given and data on retracted papers (based on Retraction Watch database) as well as citations to/from retracted papers have been added in the most recent iteration. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2023 and single recent year data pertain to citations received during calendar year 2023. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (7) is based on the August 1, 2024 snapshot from Scopus, updated to end of citation year 2023. This work uses Scopus data. Calculations were performed using all Scopus author profiles as of August 1, 2024. If an author is not on the list it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work. PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases. The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, see attached file on FREQUENTLY ASKED QUESTIONS. Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

In 2015, the United Nations established 17 Sustainable Development Goals (SDGs), with Goal 7 focusing on ensuring access to affordable, reliable, and sustainable modern energy for all by 2030. By 2022, approximately 760 million people, or 1 in 11 globally still lacked electricity access according to Tracking SDG7 :The Energy Progress Report 2022, posing significant challenges to achieving this goal. Traditional survey methods for estimating the proportion of people with electricity access are often costly, infrequently updated, and hindered by the need for interpolation of historical data.

To address these challenges, this dataset employs a nighttime light remote sensing estimation framework that integrates DMSP-CCNL and NPP/VIIRS data with GlobPOP population data. This approach produces a global 0.1-degree grid and national-scale electricity access index (EAI) maps from 1992 to 2022.

The framework results' correlation coefficient (R) with World Bank survey data from 1992 to 2022 is 0.87, and the RMSE is 15.4, demonstrating its reliability at the national level. By effectively capturing geospatial changes, this dataset supports SDG 7.1.1 monitoring and offers valuable insights for policymakers to address electricity access disparities and promote sustainable energy transitions.

Data Description

1. This dataset consists of 0.1-degree grid Electricity Access Index (EAI) data in GeoTIFF format, where each pixel value represents the proportion of the population with access to electricity within that area.

Example Filename: EAI_0dot1_Deg_WGS84_F32_1992

• Field 1: EAI (Proportion of people with access to electricity)
• Field 2&3: Spatial resolution is 0.1 degree
• Field 4: Coordinate system is WGS84
• Field 5: Data type is F32 (Float32)
• Field 6: Year "1992"

2. Aggregated EAI data at the national scale is provided in both Shapefile and CSV formats:

• Table Filename: EAI_Level_0_1992_2022.csv

  • Fields include:

    • SOC (Country code)
    • Name (Country name)
    • National EAI data from 1992 to 2022
• Shape Filename: EAI_Level_0_1992_2022.shp
  • • Boundary data sourced from GADM (Database of Global Administrative Areas)

3. The pixel-level (30 arc-seconds) Electricity Accessed Population Density is provided in GeoTIFF format, as identified through nighttime light (NTL) data.

Example Filename: Elec_PopDen_WGS84_30arc_F32_1992

• Field 1 & 2: Population Density with access to electricity (per km^2)
• Field 3: Coordinate system is WGS84
• Field 4: Spatial resolution is 30 arc-seconds
• Field 5: Data type is F32 (Float32)
• Field 6: Year "1992"

If you encounter any issues, please contact us via email at liu.luling.k2@s.mail.nagoya-u.ac.jp.

More Information

The source codes are publicly available at GitHub: https://github.com/lulingliu/EAI.

This Hotel Dataset: Rates, Reviews & Amenities(6k+) dataset includes hotel rates, guest reviews, and available amenities from two popular travel websites, TripAdvisor and Booking.com. The dataset can be used to analyze trends and insights in the hospitality industry, and inform decisions related to pricing, marketing, and customer service. Booking.com: Founded in 1996 in Amsterdam, Booking.com has grown from a small Dutch start-up to one of the world’s leading digital travel companies. Part of Booking Holdings Inc. (NASDAQ: BKNG), Booking.com’s mission is to make it easier for everyone to experience the world.

By investing in technology that takes the friction out of travel, Booking.com seamlessly connects millions of travelers to memorable experiences, a variety of transportation options, and incredible places to stay – from homes to hotels, and much more. As one of the world’s largest travel marketplaces for both established brands and entrepreneurs of all sizes, Booking.com enables properties around the world to reach a global audience and grow their businesses.

Booking.com is available in 43 languages and offers more than 28 million reported accommodation listings, including over 6.6 million homes, apartments, and other unique places to stay. Wherever you want to go and whatever you want to do, Booking.com makes it easy and supports you with 24/7 customer support. Tripadvisor, the world's largest travel guidance platform*, helps hundreds of millions of people each month** become better travelers, from planning to booking to taking a trip. Travelers across the globe use the Tripadvisor site and app to discover where to stay, what to do and where to eat based on guidance from those who have been there before. With more than 1 billion reviews and opinions of nearly 8 million businesses, travelers turn to Tripadvisor to find deals on accommodations, book experiences, reserve tables at delicious restaurants and discover great places nearby. As a travel guidance company available in 43 markets and 22 languages, Tripadvisor makes planning easy no matter the trip type. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), own and operate a portfolio of travel media brands and businesses, operating under various websites and apps.

This harmonized set of soil parameter estimates for Tunisia. It has been derived from the 1:1 million scale Soil and Terrain Database for the country (SOTER_TN, ver. 1.0) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of Tunisia, covering some 164,150 km2, has been characterized in SOTER_TN using 250 unique SOTER units. Each map unit consists of up to four different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts (see Dijkshoorn et al. 2008). Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SOTER_TN have been characterized using 100 profiles of which 44 are synthetic. The latter represent some 59 per cent of the territory. Comprehensive sets of measured attribute data are not available for most of the measured profiles (56) collated in SOTER_TN, as these were not considered in the source materials. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil property estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database ― the current taxotransfer procedure only considers profiles in WISE that: (a) have FAO soil unit names identical to those mapped for Tunisia in SOTER, and (b) originate from regions having similar Köppen climate zones (n= 3566). Property estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and volumetric water content (-33 kPa to -1.5 MPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and studies of global environmental change. The soil property estimates can be linked to the spatial data (map), using GIS, through the unique SOTER-unit code; database applications should consider the full map unit composition and depth range. The derived data presented here may be used for exploratory assessments at national scale or broader (greater than 1:1 000 000). They should be seen as best estimates based on the current, still limited, selection of soil profiles in SOTER_TN and data clustering procedure ― the type of taxotransfer rules used to fill gaps in the measured data has been flagged to provide an indication of confidence in the derived data

Abstract

The COVID-19 pandemic is significantly having short term and long term impact on Burkinabe households’ welfare, impacting households through at least three broad channels: (i) the income/employment channel, which includes both labor and non-labor income, (ii) the price channel, and (iii) the long-term human capital channel. Most of these impacts are related to the direct health effect, but also to the containment measures that systematically altered socio-economic activities, resulting in a reduction of income across the board. Due to the urgent need for timely data and the limits on face-to-face surveys, the World Bank and the National Institute of Statistics and Demography (INSD) decided to implement a high-frequency phone survey of national households (HFPS) (https://microdata.worldbank.org/index.php/catalog/3768) to monitor the effects of COVID-19 on households, leveraging the available household phone number in the 2018/19 Enquete Harmonisée sur les Conditions de Vie des Ménages (EHCVM). In Burkina Faso, the forcibly displaced persons (FDP) include both refugees and internal displaced population. For security related issues, FDPs are predominantly internal displaced people (IDPs). According to recent studies, the number of internally displaced people soared from 87,000 in January 2019 to over 1 million in August 2020, an increase of more than 1000 per cent (Conseil National de Secours d'Urgence et de Réhabilitation – CONASUR, 2020). The unprecedented levels of displacement occurred as the coronavirus pandemic worsens an already critical humanitarian crisis in the violence-stricken country. This critical situation calls for the need of timely data and analysis especially during a pandemic for this vulnerable group in order to better inform policy and targeting programs. Given the mutual interest of the INSD, WB-UNHCR Joint Data Center on Forced Displacement (JDC), UNHCR, and World Bank, decision was made to further expand the sample of the high-frequency phone survey of national households (HFPS) to include IDPs for a total of three consecutive rounds. The core survey questionnaire of the Burkina Faso High Frequency Phone Survey on IDPs (BFA HFPS-IDP) is designed to cover important and relevant topics like employment, access to basic services and items, and non-labor sources of income. The core questionnaire is complemented by questions on selected topics that rotate each month, including knowledge of Covid-19 spread, social distancing and behavior, coping mechanisms to shocks, fragility, conflict and violence. Selected topics may be investigated more in detail in specific rounds.

The BFA HFPS-IDP is fielded alongside the Burkina Faso Covid-19 High Frequency Phone Survey of national households. Rounds 1, 2 and 3 of data collection for the HFPS-IDP occur simultaneously with round 9, 10 and 11 of the national HFPS operation, respectively.

Geographic coverage

The survey covers households from 9 of the 13 regions of Burkina Faso. These regions are: Boucle de Mouhoun, Cascades, Centre-Est, Centre-Nord, Est, Hauts-Bassins, Nord, Plateau Central, and Sahel.

Analysis unit

• Households
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The IDP sample is drawn from an IDP database named CONASUR database which serves as the sampling frame. The CONASUR has been developed and supported by the government of Burkina Faso with the technical and financial support of development partners, including UNHCR, IOM and OCHA. The CONASUR database is updated regularly, and has exhaustive list of refugees and IDPs, along with few socio-demographic characteristics, as well as information on the phone numbers of households. The sample is drawn from the 9 regions (out of 12) where the presence of IDPs is more relevant: Boucle du Mouhoun, Cascades, Centre-Est, Centre-Nord, Est, Hauts-Bassins, Nord, Plateau Central, Sahel. It is important to note that the BFA HFPS-IDPs is representative of households that have access to phones. Taken that into consideration, a key concern is the bias introduced by sampling households with at least a phone number, as phone penetration in some regions/areas might be limited. However, according to data from the CONASUR database, the percentage of households with at least one phone number is very high, accounting for above the 74% in all the sampled regions. To account for non-response and attrition, 1500 households were selected in baseline round of the HFS. 1,166 households were fully interviewed during the first round of interviews. The final successful sample have been contacted in subsequent rounds of the survey.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

ROUND 1: Household Respondent’s information; Access to Basic Services; Employment and revenues; Food Security and Other revenues. ROUND 2: Household Respondent’s information; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Covid-19 Testing and Vaccination; Access to Basic Services; Credit; Employment and revenue (with a focus on livestock activities); Food Security; Other revenues; Shocks; Concerns regarding the impact of COVID-19 on personal health and financial wealth of the household; Fragility, Conflict and Violence. ROUND 3: Household Respondent’s information; Early Child Development; Access to Basic Services; Employment and revenue (with a focus on agricultural activities); Food Security; Other revenues; Concerns regarding the current situation; Social Safety Nets. All the interview materials were translated in French for the INSD. The questionnaire was administered in local languages with about varying length (about 25 minutes).

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the INSD with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

BASELINE (ROUND 1): All 1500 households were called in the baseline round of the phone survey. 73.75 percent of sampled households were successfully contacted. Of those contacted, 1,156 households were fully interviewed. These 1,156 households constitute the final successful sample and will be contacted in subsequent rounds of the survey.

ROUND 2: Interviewers attempted to contact and interview all 1,156 households that were successfully interviewed in the Round 1 of the BFA COVID-19 HFPS. 1,114 households (96.3% of the 1,156 attempted) were contacted and 1,112 (96.1%) were successfully interviewed in the second round. Of those contacted, 2 households did not answer due to a language barrier.

ROUND 3: Interviewers attempted to contact and interview all 1,112 households that were successfully interviewed in the Round 2 of the BFA COVID-19 HFPS. 1,051 households (94.53% of the 1,112 attempted) were contacted and 1,048 (94.24%) were successfully interviewed in the third round. Of those contacted, 1 household refused the interview and 2 were only partially interviewed.

RESPONDENTS: Each round of the Burkina Faso COVID-19 HFPS has ONE RESPONDENT per household. The respondent was the household head or a knowledgeable adult household member. The respondent must be a member of the household. Unlike many other household surveys, interviewers were not expected to seek out other household members to provide their own information. The respondent may still consult with other household members as needed to respond to the questions, including to provide all the necessary information on each household member.

Interviewers were instructed to make every effort to reach the same respondent in subsequent rounds of the survey, in order to maintain the consistency of the information collected. However, in cases where the previous respondent was not available, interviewers would identify another knowledgeable adult household member to interview.

One in every 100 children dies before completing one year of life. Around 68 percent of infant mortality is attributed to deaths of children before completing 1 month. 15,000 children die every day – Child mortality is an everyday tragedy of enormous scale that rarely makes the headlines Child mortality rates have declined in all world regions, but the world is not on track to reach the Sustainable Development Goal for child mortality Before the Modern Revolution child mortality was very high in all societies that we have knowledge of – a quarter of all children died in the first year of life, almost half died before reaching the end of puberty Over the last two centuries all countries in the world have made very rapid progress against child mortality. From 1800 to 1950 global mortality has halved from around 43% to 22.5%. Since 1950 the mortality rate has declined five-fold to 4.5% in 2015. All countries in the world have benefitted from this progress In the past it was very common for parents to see children die, because both, child mortality rates and fertility rates were very high. In Europe in the mid 18th century parents lost on average between 3 and 4 of their children Based on this overview we are asking where the world is today – where are children dying and what are they dying from?

5.4 million children died in 2017 – Where did these children die? Pneumonia is the most common cause of death, preterm births and neonatal disorders is second, and diarrheal diseases are third – What are children today dying from? This is the basis for answering the question what can we do to make further progress against child mortality? We will extend this entry over the course of 2020.

@article{owidchildmortality, author = {Max Roser, Hannah Ritchie and Bernadeta Dadonaite}, title = {Child and Infant Mortality}, journal = {Our World in Data}, year = {2013}, note = {https://ourworldindata.org/child-mortality} }

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset displays the Real GDP by metropolitan area for the years 2001-2005. For each of the posted metropolitan areas Millions of chained dollars and the percentage change from the previous year is posted. This data was geocoded according to city and state locations. During the geocoding process 233/363 records from the original dataset were successfully geocoded. The reason for this is that during the process is that the dataset often groups cities together into one metropolitan area, which were unable to be properly coded. This data was collected from the Bureau of Economic analysis at their web page at: http://www.bea.gov/newsreleases/regional/gdp_metro/gdp_metro_newsrelease.htm Access Date: October 29, 2007

This dataset displays the energy prices and expenditures for each of the 50 United States, plus the District of Columbia. Included in the dataset are figures on the prices on a scale with nominal dollars per million Btu. Expenditures in millions of nominal dollars. Expenditures per person in nominal dollars. Hawaii pays the highest in prices, with Texas paying the most in expenditures.

Accumulation rates of 1-2 mm/million years are reported for manganese crusts on deep-sea rocks. These rates are the same as most reported accretion rates for manganese nodules suggesting a similar mechanism for this formation. Manganese crusts on large deep-sea rocks provide convincing evidence that net sediment accumulation in these areas has been zero for many millions of years. From 1983 until 1989 NOAA-NCEI compiled the NOAA-MMS Marine Minerals Geochemical Database from journal articles, technical reports and unpublished sources from other institutions. At the time it was the most extended data compilation on ferromanganese deposits world wide. Initially published in a proprietary format incompatible with present day standards it was jointly decided by AWI and NOAA to transcribe this legacy data into PANGAEA. This transfer is augmented by a careful checking of the original sources when available and the encoding of ancillary information (sample description, method of analysis...) not present in the NOAA-MMS database.

There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.

The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.

The underlying data and code of this version are the same as the previous one, but provide additional data format and selected data summaries.

Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.

SPAM2010:

Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.

GAEZv4:

FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021

The dataset of Ricciardi et al.'s:

Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.

The global dominant field size dataset:

Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.

GLC-Share:

Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.

CAAS-IFPRI cropland extent map:

Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.

The Gross Domestic Product (GDP) in Russia was worth 2173.84 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Russia represents 2.05 percent of the world economy. This dataset provides the latest reported value for - Russia GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.