Abstract

This public dataset contains key variables on energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix and other relevant metrics, made available by Our World in Data. Curated by Carnegie Mellon University Libraries.

Methodology

Additional data sources used by Our World in Data include:

• Energy Institute, https://www.energyinst.org/statistical-review
• U.S. Energy Information Administration, https://www.eia.gov/opendata/index.php#bulk-downloads
• The Shift Dataportal, https://www.theshiftdataportal.org/energy
• Ember, https://ember-climate.org/data-catalogue/yearly-electricity-data/
• HYDE version 3.2, https://www.pbl.nl/en/image/links/hyde
• Gapminder's Population version 7, https://www.gapminder.org/data/documentation/gd003/
• UN World Population Prospect, https://population.un.org/wpp/Download/Standard/Population/
• Gapminder's Systema Globalis, https://github.com/open-numbers/ddf--gapminder--systema_globalis
• World Bank's data on income groups
• Bolt, J., & van Zanden, J. L. (2020). Maddison Project Database, version 2020. https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020

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Usage

License:

• Creative Commons BY
• See data sources (specified in Methodology) for additional license terms

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Codebook:

Please refer to the codebook for variable metadata (see the table named "codebook").

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Data from literature search systematically conducted using two widely-used academic databases: Web of Science (WoS) and Scopus . Data include the annual amount of KM publication in China and across the world, in WoS, the total amount of knowledge management (KM) publication during the searched years for each country (top 20), in Scopus, the total amount of KM publication during the searched years for each country (top 20), information about the retained KM publication for environmental management in China. The data were generated during the NERC grant 'The transmissive critical zone: understanding the karst hydrology-biogeochemical interface for sustainable management' reference NE/N007425/1 undertaken as part of the NERC Using Critical Zone Science to Understand Sustaining the Ecosystem Service of Soil & Water (CZO) programme.

Context

Data is compiled by Our World in Data based on two sources: – BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html – Ember: https://ember-climate.org/data/

Generation in THh between 2000 and 2019

Content

World in Data rely on electricity mix data from BP as it's primary source for two key reasons: BP also provides total energy (not just electricity) consumption data, meaning energy and electricity data is consistent from the same source; and it provides a longer time-series. However, BP does not provide data for all countries, but these were removed from this datasets.

Acknowledgements

Ember compiles electricity mix data from numerous international and national sources, but relies on the Energy Information Administration (EIA) as its primary source.

The average for 2023 based on 193 countries was -0.07 points. The highest value was in Liechtenstein: 1.61 points and the lowest value was in Syria: -2.75 points. The indicator is available from 1996 to 2023. Below is a chart for all countries where data are available.

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says 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, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

The average for 2023 based on 153 countries was 94.91 percent. The highest value was in Luxembourg: 386.03 percent and the lowest value was in Sudan: 6.77 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

As of February 2025, 5.56 billion individuals worldwide were internet users, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 20254. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide – over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

Ranking of Selected Economies in the World Merchandise Trade - Table 410-51041 : Ranking of Selected Economies in the World Merchandise Trade

1) Data Introduction • The Hotels from Around the World Dataset provides over 1,000 hotel data (including ratings, reviews, and room rates) provided by Booking.com .

2) Data Utilization (1) Hotels from Around the World Dataset has characteristics that: • This dataset is a list of over 10 major city hotels worldwide. This includes ratings, city, country, and number of customer reviews. • This dataset was extracted on February 18, 2025 and is based on a one-night reservation from March 18-19, 2025. (2) Hotels from Around the World Dataset can be used to: • Analysis of hotel ratings and reviews : Using hotel-specific ratings and review data, it can be used for text mining and emotional analysis studies such as customer satisfaction analysis, hotel service quality assessment, and classification of positive and negative reviews. • Tourism and Location Strategy Research : It can be used for research on the tourism industry and real estate market, including comparing characteristics by popular area, location strategy, and hotel rating by analyzing various characteristics such as hotel location, rating, convenience facilities, and number of reviews.

The ranking of countries by air pollution levels, based on data from the World Health Organization (WHO), shows the annual average concentrations of fine particulate matter (PM2.5) in micrograms per cubic meter.

Context

This dataset is a subset of Yelp's businesses, reviews, and user data. It was originally put together for the Yelp Dataset Challenge which is a chance for students to conduct research or analysis on Yelp's data and share their discoveries. In the most recent dataset you'll find information about businesses across 8 metropolitan areas in the USA and Canada.

Content

This dataset contains five JSON files and the user agreement. More information about those files can be found here.

Code snippet to read the files

in Python, you can read the JSON files like this (using the json and pandas libraries):

import json
import pandas as pd
data_file = open("yelp_academic_dataset_checkin.json")
data = []
for line in data_file:
 data.append(json.loads(line))
checkin_df = pd.DataFrame(data)
data_file.close()

Latvia LV: Ease of Doing Business Index: 1=Most Business-friendly Regulations data was reported at 19.000 NA in 2017. Latvia LV: Ease of Doing Business Index: 1=Most Business-friendly Regulations data is updated yearly, averaging 19.000 NA from Dec 2017 (Median) to 2017, with 1 observations. Latvia LV: Ease of Doing Business Index: 1=Most Business-friendly Regulations data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank.WDI: Business Environment. Ease of doing business ranks economies from 1 to 190, with first place being the best. A high ranking (a low numerical rank) means that the regulatory environment is conducive to business operation. The index averages the country's percentile rankings on 10 topics covered in the World Bank's Doing Business. The ranking on each topic is the simple average of the percentile rankings on its component indicators.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; ; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.

Two shapefiles mapping the locations of ancient and modern passive margin boundaries are presented. These data are a digital recreation of the work originally published by Bradley (2008). The ancient passive margin data were used as an evidential layer to map prospectivity for sediment-hosted Pb-Zn mineral systems (Lawley and others, 2022). The ancient passive margins dataset includes additional attributes related to the boundary's orogenic setting and history, the length of the boundary, its estimated lifespan, and its modern-day country location. Although only ancient passive margin boundaries were analyzed for the United States, Canada, and Australia for this study, boundaries for the world are included in the shapefile. The modern passive margin dataset includes an identifier for the margin segment, a margin name, the associated ocean, and age ranges of basin initiation, mean age and length of the respective passive margin segment. The modern passive margin data were not used in prospectivity modeling for ancient deposits. The passive margin boundaries in both files are mapped as line segments in geographic coordinates using a WGS84 datum. References Bradley, D.C., 2008, Passive margins through earth history: Earth-Science Reviews, v. 91, no. 1-4, p. 1-26, https://doi.org/10.1016/j.earscirev.2008.08.001. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635.

This dataset contains the digitized treatments in Plazi based on the original journal article Li, Xin-Yu, Pape, Thomas, Zhang, Dong (2019): Taxonomic review of Gasterophilus (Oestridae, Gasterophilinae) of the world, with updated nomenclature, keys, biological notes, and distributions. ZooKeys 891: 119-156, DOI: http://dx.doi.org/10.3897/zookeys.891.38560, URL: http://dx.doi.org/10.3897/zookeys.891.38560

Altosight | AI-Powered Amazon Data, eBay Data & More | Global Marketplace Insights

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🔹 Analyze which retailers sell particular brands and products, helping businesses identify new partnerships or distribution opportunities.

― Data Collection & Quality ―

✔ Publicly Sourced Data: Altosight collects Amazon Data, Amazon Reviews Data, eBay Data, Alibaba Data, and AliExpress Data from publicly available sources. This includes product information, transaction data, reviews, and other valuable data points that are essential for making informed business decisions.

✔ AI-Powered Scraping: Our AI-driven technology handles CAPTCHAs, dynamic content, and JavaScript-heavy websites to ensure continuous and accurate data collection. We extract and structure Amazon Reviews Data, Digital Shelf Data, and other relevant marketplace data for easy integration into your existing systems.

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This is the dataset for 2021 world biased/unbiased per capita GNI including ranking and classification by the World Bank. The original data (country, code, population, GNI, classification) was downloaded from the World Bank with date 12/22/2022 (notice that some countries have no data at the time of download).

On June 1, 2020, a little more than two months after the World Health Organization's pandemic declaration, we assumed leadership of the American Political Science Review (APSR), making it difficult to isolate the pandemic's effect on new submissions and review processes. In this research note, we describe submission and review patterns in the two and half years before and after the pandemic's beginning and editorial transition. We offer some tentative conclusions. The timing of the editorial transition and our public commitments to broaden the reach of the journal may help explain why new submissions to the APSR increased during the the pandemic. At the APSR, our commitment to substantive diversity may have also contributed to greater representational diversity among submitting authors. In our experience, reviewers were less likely to complete reviews during the first years of the pandemic, but by inviting more reviewers per manuscript, our team was able to improve review times overall. This strategy may not work as well for smaller journals that already struggle to secure reviews.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Zimbabwe ZW: Ease of Doing Business Index: 1=Most Business-friendly Regulations data was reported at 159.000 NA in 2017. Zimbabwe ZW: Ease of Doing Business Index: 1=Most Business-friendly Regulations data is updated yearly, averaging 159.000 NA from Dec 2017 (Median) to 2017, with 1 observations. Zimbabwe ZW: Ease of Doing Business Index: 1=Most Business-friendly Regulations data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Business Environment. Ease of doing business ranks economies from 1 to 190, with first place being the best. A high ranking (a low numerical rank) means that the regulatory environment is conducive to business operation. The index averages the country's percentile rankings on 10 topics covered in the World Bank's Doing Business. The ranking on each topic is the simple average of the percentile rankings on its component indicators.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; ; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.