The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

Context

In demographics, the world population is the total number of humans currently living, and was estimated to have reached 7,800,000,000 people as of March 2020. It took over 2 million years of human history for the world's population to reach 1 billion, and only 200 years more to reach 7 billion. The world population has experienced continuous growth following the Great Famine of 1315–1317 and the end of the Black Death in 1350, when it was near 370 million. The highest global population growth rates, with increases of over 1.8% per year, occurred between 1955 and 1975 – peaking to 2.1% between 1965 and 1970.[7] The growth rate declined to 1.2% between 2010 and 2015 and is projected to decline further in the course of the 21st century. However, the global population is still increasing[8] and is projected to reach about 10 billion in 2050 and more than 11 billion in 2100.

Content

Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. Annual population growth rate. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.

Statistical Concept and Methodology

Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time. The growth rate is computed using the exponential growth formula: r = ln(pn/p0)/n, where r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods. For information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).

Acknowledgements

Derived from total population. Population source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision, ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme.

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

The total population in the United Kingdom was estimated at 69.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - United Kingdom Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dominican Republic DO: Population in Urban Agglomerations of More Than 1 Million data was reported at 3,094,465.000 Person in 2017. This records an increase from the previous number of 3,018,681.000 Person for 2016. Dominican Republic DO: Population in Urban Agglomerations of More Than 1 Million data is updated yearly, averaging 1,483,522.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3,094,465.000 Person in 2017 and a record low of 367,328.000 Person in 1960. Dominican Republic DO: Population in Urban Agglomerations of More Than 1 Million data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank.WDI: Population and Urbanization Statistics. Population in urban agglomerations of more than one million is the country's population living in metropolitan areas that in 2018 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; ;

The social assistance explorer contains a harmonised panel dataset of social assistance indicators spanning 2000-2015. It has been developed to support comparative research on emerging welfare institutions. Comparative analysis of social protection institutions in low and middle income countries is scarce. Yet social assistance accounts for most of the recent expansion of welfare institutions. The project collected data on programme design and objectives, institutionalisation, reach, and financial resources. Key indicators can be aggregated at country and region levels.Since the turn of the century low and middle income countries have introduced or expanded programmes providing direct transfers to families in poverty or extreme poverty as a means of strengthening their capacity to exit poverty. The rationale underpinning these programmes is that stabilising and enhancing family income through transfers in cash and in kind will enable programme participants to improve their nutrition, ensure investment in children's schooling and health, and help overcome economic and social exclusion. The expansion of antipoverty transfer programmes has accelerated. Estimates suggest that around 1 billion people in developing countries reside with someone in receipt of a transfer. As would be expected, the spread of social assistance has been slower and more tentative in low income countries due to implementation and finance constraints and limited elite political support. Antipoverty transfer programmes in developing countries show large variation in design, effectiveness, scale, and objectives. In most countries, there are several interventions running alongside one another with diverse priorities and designs, and often targeting different groups. In many countries social public assistance programmes work alongside social insurance programmes for formal sector workers and humanitarian or emergency assistance. Social assistance focuses on groups in poverty, provides medium term support, and is budget-financed. The spread of social assistance in developing countries has revealed significant gaps in the knowledge, for example as regards their effectiveness, reach, and sustainability. Comparative analysis is essential to fill in these gaps and improve national, regional and global policy. For example, achieving a zero target for extreme poverty, as has been suggested in the context of the post-2015 international development agenda, would require effective and permanent institutions ensuring the benefits from economic growth reach the poorest. Social assistance is essential to achieving this goal. This research project focuses on improving research infrastructure on social assistance, in terms of concepts, indicators and data. This is urgently needed to support comparative analysis of emerging social assistance institutions. The project will identify indicators to assess social assistance programmes and will collect information on these for 2000 to 2015 for all developing countries. The database will be made available online to researchers and policy makers globally. As part of the project, the database will be analysed to examine patterns or configurations in social assistance programmes and institutions. Our interest is in identifying ideal types, broad features of social assistance programmes or institutions which enable reducing the large diversity of programmes and interventions to their core characteristics. These ideal types are social assistance regimes. Further analysis will test for potential combinations of political, demographic, economic and social factors linked to specific social assistance regimes. This analysis will allow us to examine what conditions can help explain the expansion of social assistance in developing countries; what factors influence the specific configuration of social assistance institutions in different countries and regions; and what conditions are needed for their effectiveness and sustainability. This research will throw light on the contribution of social assistance to the reduction of poverty and vulnerability and to economic and social development. The data collection included all countries defined as low and middle income in the 2016 version of the World Bank Country Classification. An inventory of potential social assistance programmes was developed for each country. The definition described above was then applied to identify social assistance programmes. For some countries with a large number of small or localised programmes, the data collection focused on nationwide, large-scale, and/or leading programmes. For example, some states in India have localised programmes. These were excluded from the data collection. In sub-Saharan Africa some programmes are very small in scale but they are significant in leading the expansion of social assistance. They were included. Where programmes consolidate pre-existing programmes, for example Brazil's Bolsa Família, the dataset includes Bolsa Família as well as its component programmes. Data were collected from a variety of sources: global and regional datasets (ASPIRE, ODI, CEPAL, ADB's SPI, IPC-PG); national government websites; programme agency reports; research papers; evaluation reports; policy documents; IFIs project documentation and reports; personal communication with programme agencies. The collection of the data was organised around a codebook, describing each of the variables and the specific coding of the information. The codebook was constructed after extensive consultation with specialist researchers. The codebook is available from the data webpage in the website. Specialist consultants supported data collection in had-to-reach areas. The data collected were checked against alternative sources of information where available.

This is the dataset for the article "A Predictive Method to Improve the Effectiveness of Twitter Communication in a Cultural Heritage Scenario".

Abstract:

Museums are embracing social technologies in the attempt to broaden their audience and to engage people. Although social communication seems an easy task, media managers know how hard it is to reach millions of people with a simple message. Indeed, millions of posts are competing every day to get visibility in terms of likes and shares and very little research focused on museums communication to identify best practices. In this paper, we focus on Twitter and we propose a novel method that exploits interpretable machine learning techniques to: (a) predict whether a tweet will likely be appreciated by Twitter users or not; (b) present simple suggestions that will help enhancing the message and increasing the probability of its success. Using a real-world dataset of around 40,000 tweets written by 23 world famous museums, we show that our proposed method allows identifying tweet features that are more likely to influence the tweet success.

Code to run a selection of experiments is available at https://github.com/rmartoglia/predict-twitter-ch

Dataset structure

The dataset contains the dataset used in the experiments of the above research paper. Only the extracted features for the museum tweet threads (and not the message full text) are provided and needed for the analyses.

We selected 23 well known world spread art museums and grouped them into five groups: G1 (museums with at least three million of followers); G2 (museums with more than one million of followers); G3 (museums with more than 400,000 followers); G4 (museums with more that 200,000 followers); G5 (Italian museums). From these museums, we analyzed ca. 40,000 tweets, with a number varying from 5k ca. to 11k ca. for each museum group, depending on the number of museums in each group.

Content features: these are the features that can be drawn form the content of the tweet itself. We further divide such features in the following two categories:

– Countable: these features have a value ranging into different intervals. We take into consideration: the number of hashtags (i.e., words preceded by #) in the tweet, the number of URLs (i.e., links to external resources), the number of images (e.g., photos and graphical emoticons), the number of mentions (i.e., twitter accounts preceded by @), the length of the tweet;

– On-Off : these features have binary values in {0, 1}. We observe whether the tweet has exclamation marks, question marks, person names, place names, organization names, other names. Moreover, we also take into consideration the tweet topic density: assuming that the involved topics correspond to the hashtags mentioned in the text, we define a tweet as dense of topics if the number of hashtags it contains is greater than a given threshold, set to 5. Finally, we observe the tweet sentiment that might be present (positive or negative) or not (neutral).

Context features: these features are not drawn form the content of the tweet itself and might give a larger picture of the context in which the tweet was sent. Namely, we take into consideration the part of the day in which the tweet was sent (morning, afternoon, evening and night respectively from 5:00am to 11:59am, from 12:00pm to 5:59pm, from 6:00pm to 10:59pm and from 11pm to 4:59am), and a boolean feature indicating whether the tweet is a retweet or not.

User features: these features are proper of the user that sent the tweet, and are the same for all the tweets of this user. Namely we consider the name of the museum and the number of followers of the user.

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.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development. Indicators: Access to electricity, Agricultural irrigated land, Agricultural land, Agriculture, Annual freshwater withdrawals, Arable land, Average precipitation in depth, CO2 emissions, CO2 emissions from gaseous fuel consumption, CO2 emissions from liquid fuel consumption, CO2 emissions from solid fuel consumption, CO2 intensity, CPIA public sector management and institutions cluster average, Cereal yield, Community health workers, Disaster risk reduction progress score, Droughts, Ease of doing business index, Electric power consumption, Electricity production from coal sources, Electricity production from hydroelectric sources, Electricity production from natural gas sources, Electricity production from nuclear sources, Electricity production from oil sources, Electricity production from renewable sources, Energy use, Foreign direct investment, Forest area, GHG net emissions/removals by LUCF, HFC gas emissions, Land area where elevation is below 5 meters, Marine protected areas, Methane emissions, Mortality rate, Nitrous oxide emissions, Other greenhouse gas emissions, PFC gas emissions, Population, Population growth, Population in urban agglomerations of more than 1 million, Population living in areas where elevation is below 5 meters, Poverty headcount ratio at $1.90 a day, Prevalence of underweight, Primary completion rate, Renewable electricity output, Renewable energy consumption, Rural land area where elevation is below 5 meters, Rural population living in areas where elevation is below 5 meters, SF6 gas emissions, School enrollment, Terrestrial and marine protected areas, Terrestrial protected areas, Total greenhouse gas emissions, Urban land area where elevation is below 5 meters, Urban population, Urban population growth, Urban population living in areas where elevation is below 5 meters

The urban–rural continuum classifies the global population, allocating rural populations around differently-sized cities. The classification is based on four dimensions: population distribution, population density, urban center location, and travel time to urban centers, all of which can be mapped globally and consistently and then aggregated as administrative unit statistics.Using spatial data, we matched all rural locations to their urban center of reference based on the time needed to reach these urban centers. A hierarchy of urban centers by population size (largest to smallest) is used to determine which center is the point of “reference” for a given rural location: proximity to a larger center “dominates” over a smaller one in the same travel time category. This was done for 7 urban categories and then aggregated, for presentation purposes, into “large cities” (over 1 million people), “intermediate cities” (250,000 –1 million), and “small cities and towns” (20,000–250,000).Finally, to reflect the diversity of population density across the urban–rural continuum, we distinguished between high-density rural areas with over 1,500 inhabitants per km2 and lower density areas. Unlike traditional functional area approaches, our approach does not define urban catchment areas by using thresholds, such as proportion of people commuting; instead, these emerge endogenously from our urban hierarchy and by calculating the shortest travel time.Urban-Rural Catchment Areas (URCA).tif is a raster dataset of the 30 urban–rural continuum categories for the urban–rural continuum showing the catchment areas around cities and towns of different sizes. Each rural pixel is assigned to one defined travel time category: less than one hour, one to two hours, and two to three hours travel time to one of seven urban agglomeration sizes. The agglomerations range from large cities with i) populations greater than 5 million and ii) between 1 to 5 million; intermediate cities with iii) 500,000 to 1 million and iv) 250,000 to 500,000 inhabitants; small cities with populations v) between 100,000 and 250,000 and vi) between 50,000 and 100,000; and vii) towns of between 20,000 and 50,000 people. The remaining pixels that are more than 3 hours away from any urban agglomeration of at least 20,000 people are considered as either hinterland or dispersed towns being that they are not gravitating around any urban agglomeration. The raster also allows for visualizing a simplified continuum created by grouping the seven urban agglomerations into 4 categories.Urban-Rural Catchment Areas (URCA).tif is in GeoTIFF format, band interleaved with LZW compression, suitable for use in Geographic Information Systems and statistical packages. The data type is byte, with pixel values ranging from 1 to 30. The no data value is 128. It has a spatial resolution of 30 arc seconds, which is approximately 1km at the equator. The spatial reference system (projection) is EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long). The geographic extent is 83.6N - 60S / 180E - 180W. The same tif file is also available as an ESRI ArcMap MapPackage Urban-Rural Catchment Areas.mpkFurther details are in the ReadMe_data_description.docx

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 modeFace-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: PaperWeb-based interviewIn 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) with a printed (PAPI) or electronic (CAPI) questionnaire. Several countries employed self-administered interview or mixed-mode approach to data collection: Australia (CAWI; postal survey); Canada (CAWI); Great Britain (CAPI; CAWI; postal survey; web-based interview (Video interviewing); Hong Kong SAR (PAPI; CAWI); Malaysia (CAWI; PAPI); Netherlands (CAWI); Northern Ireland (CAPI; CAWI; postal survey; web-based interview (Video interviewing); 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); Czechia (CZ); Chile (CL); China (CN); Cyprus (CY); Ecuador (EC); Egypt (EG); Ethiopia (ET); Germany (DE); Great Britain (GB-GBN); 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); Northern Ireland (GB-NIR); Pakistan (PK); Peru (PE); Philippines (PH); Puerto Rico (PR); Romania (RO); Russia (RU); Serbia (RS); Singapore (SG); Slovakia (SK); South Korea (KR); Taiwan ROC (TW); Tajikistan (TJ); Thailand (TH); Tunisia (TN); Turkey (TR); Ukraine (UA); United States (US); Uruguay (UY); Venezuela (VE); Vietnam (VN); Zimbabwe (ZW). The sampling procedures differ from country to country; probability sample: Multistage Sample, Probability Sample, Simple Random Sample Nation-wide 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 2017-2022. 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. China, USA, Russia, Brazil etc.) are requirred to reach an effective sample of N=1500 or larger. Only 3 countries (Argentina, Chile, Uruguay) 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.

1. Summary

Global estimates of reach-level bankfull river width generated in the article by Peirong Lin, Ming Pan, George H. Allen, Renato Frasson, Zhenzhong Zeng, Dai Yamazaki, Eric F. Wood entitled "Global reach-level bankfull river width leveraging big-data geospatial analysis", Geophysical Research Letters (accepted).

2. File Description

Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin et al. (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki et al., 2017, 2019), under a channelization threshold of 25 km². Only rivers wider than 30 m are shown here; these locations were determined by jointly using the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and the MERIT Hydro width estimates (Yamazaki et al., 2019).

3. Attribute Description

• COMID: identification number of the river reach, same as that used in global river modeling by Lin et al., (2019);
• Order: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km²;
• Area: Upstream drainage basin area in km²;
• Sin: Sinuosity of the river segment (unitless);
• Slp: mean slope of the river segment (unitless);
• Elev: mean elevation of the river segment;
• K: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• P: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• AI: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019);
• LAI: mean leaf area index of the unit catchment; data extracted from Zhu et al. (2013);
• SND: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• CLY: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• SLT: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• Urb: mean urban fraction of the unit catchment; data extracted from Liu et al. (2018);
• WTD: mean water table depth (m below surface) of the unit catchment; data extracted from Fan et al. (2013);
• HW: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada et al. (2016)
• DOR: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill et al. (2019)
• QMEAN: mean annual discharge (m³/s) for the river segment; the multi-year averaged were calculated from Lin et al. (2019);
• Q2: 2-year return period flood discharge (m³/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin et al. (2019);
• Width_m: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates;
• Width_DHG: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study

4. References

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636

Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940–943. https://doi.org/10.1126/science.1229881

Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). Mapping the world’s free-flowing rivers. Nature, 569(7755), 215. https://doi.org/10.1038/s41586-019-1111-9

Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748

Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860

Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 0(0). https://doi.org/10.1029/2019WR025287

Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055

Trabucco, A., & Zomer, R. (2019, January 18). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. https://doi.org/10.6084/m9.figshare.7504448.v3

Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). High-resolution modeling of human and climate impacts on global water resources. Journal of Advances in Modeling Earth Systems, 8(2), 735–763. https://doi.org/10.1002/2015MS000618

Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resources Research. https://doi.org/10.1029/2019WR024873

Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing, 5(2), 927–948. https://doi.org/10.3390/rs5020927

The Gross Domestic Product (GDP) in China was worth 18743.80 billion US dollars in 2024, according to official data from the World Bank. The GDP value of China represents 17.65 percent of the world economy. This dataset provides - China GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The United States recorded a trade deficit of 60.18 USD Billion in June of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Database Automation Systems Market Outlook

The global database automation systems market size was valued at approximately USD 7.8 billion in 2023 and is expected to reach USD 23.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.2% during the forecast period. This growth is primarily driven by the increasing need for efficient database management and the rapid adoption of cloud-based solutions across various industries. The surge in data generation and the increasing complexity of database environments necessitate automation to enhance operational efficiency and reduce human error.

One of the primary growth factors for the database automation systems market is the escalating demand for cost efficiency and resource optimization in database management. Enterprises are increasingly leveraging automation to minimize manual intervention, thereby reducing operational costs and improving productivity. Additionally, the growing complexity of database environments, owing to the proliferation of data, necessitates sophisticated automation tools to manage and maintain databases effectively. These tools not only facilitate seamless database operations but also enhance performance and reliability, further driving market growth.

The surge in cloud adoption is another significant driver for the market. With an increasing number of enterprises migrating their workloads to the cloud, there is a heightened demand for cloud-based database automation solutions. These solutions offer scalability, flexibility, and cost-effectiveness, which are critical for businesses aiming to stay competitive in today’s dynamic environment. Moreover, the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) with database automation systems is revolutionizing the market by enabling predictive analytics and proactive maintenance, thereby enhancing database performance and security.

Moreover, regulatory compliance and data security concerns are propelling the demand for database automation systems. Businesses across various industries are subject to stringent regulatory requirements regarding data handling and storage. Automated database systems provide robust security features and ensure compliance with international standards, thereby mitigating the risk of data breaches and non-compliance penalties. This aspect is particularly crucial for industries such as BFSI, healthcare, and retail, where data integrity and security are paramount.

From a regional perspective, North America has emerged as a dominant player in the database automation systems market. The region’s strong technological infrastructure, coupled with the presence of major market players, drives significant adoption of these systems. Additionally, the high adoption rate of cloud services and advanced technologies like AI and ML in North America further propels market growth. Conversely, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digitization across emerging economies, increasing investments in IT infrastructure, and a burgeoning number of SMEs adopting database automation solutions.

Component Analysis

Database automation systems can be broadly segmented into software and services. The software segment encompasses various automation tools and platforms that enable enterprises to automate database management tasks. This includes tools for database provisioning, backup and recovery, performance management, and security and compliance. The robust growth of this segment is driven by the need for advanced solutions that can handle the increasing complexity and volume of data. Enterprises are investing heavily in software solutions to streamline operations, reduce human errors, and enhance database performance.

The services segment, on the other hand, involves professional services such as consulting, implementation, training, and support. This segment plays a crucial role in ensuring the successful deployment and operation of database automation systems. As enterprises increasingly adopt automation solutions, the demand for specialized services to guide the implementation process and provide ongoing support is surging. Service providers offer expertise and strategic insights that help organizations maximize the benefits of their automation investments, thereby driving the growth of this segment.

Moreover, the integration of AI and ML technologies in database automation software is a game-changer. These technologies enable predictive analytics and proactiv

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).

GH Pro conducted an endline evaluation of USAID’s Maternal Child Survival Program (MCSP-MNCH)1 to assess if it had achieved its objectives and planned outputs, as stated in its program description, in Nigeria’s Ebonyi and Kogi states. Five questions evaluated increases in access and utilization of reproductive, maternal, newborn, and child health interventions; gender-transformative strategies; sustainability; the program’s learning agenda vis-à-vis the Nigerian government’s learning needs; and use of program data. The evaluation team used a retrospective analytic and a cross-sectional design to address the five questions, and mixed methods were used for data collection, including reviews of the national District Health Information System (DHIS) 2, MCSP-MNCH datasets, and 51 program documents. Apparent improvements were noted in the utilization of six interventions: oxytocin, partograph, Chlorhexidine 4% gel, newborn resuscitation, essential newborn care, and integrated Community Case Management, particularly with referral of danger signs. MCSP-MNCH baseline data was not available nor calculable for magnesium sulphate or Kangaroo Mother Care. Data was also not available for postpartum family planning for first-time parents and Bubble Continuous Positive Airway Pressure for newborn resuscitation, as a study was undergoing analysis and data was not ready. Furthermore, the dataset MCSP-MNCH provided to the evaluation team was incomplete, imprecise, and contained errors, raising concerns about noted improvements. The program’s work in male engagement and Mothers Savings and Loans Clubs hold promise for transforming gender norms but reached too few people. Most of the program’s reproductive health and MNCH interventions are likely to be included in budgets in Ebonyi and Kogi through the World Bank’s Saving One Million Lives project, but without specific commitment from the states’ governors, funding release may be jeopardized. The learning agenda helped inform implementation, but the government did not help shape the research. Last, MCSP-MNCH project created a new DHIS database instance for its project data only, including new indicators that it introduced (like application of Chlorhexidine 4% gel for newborn cord care), as well as indicators that were already available in the national DHIS 2 database; it is housed within the same server as the national DHIS 2.

Success.ai’s B2B Contact Data for Human Resources Professionals Worldwide empowers businesses to connect with HR leaders across the globe. With access to over 170 million verified professional profiles, this dataset includes critical contact information for key HR decision-makers in various industries. Whether you’re targeting HR directors, talent acquisition specialists, or employee relations managers, Success.ai ensures accurate and effective outreach.

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Data Highlights: - 170M+ Verified Professional Profiles: Includes HR professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.

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• HR Decision-Maker Profiles: Identify and connect with HR professionals at all levels, including C-suite HR leaders.
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APIs for Enhanced Functionality

1. Data Enrichment API: Enrich existing records with verified HR contact data.
2. Lead Generation API: Automate lead generation for HR-specific campaigns and initiatives.

Leverage B2B Contact Data for Human Resources Professionals Worldwide to connect with HR leaders and decision-makers in your target market. Success.ai offers verified work emails, phone numbers, and continuously updated profiles to ensure effective outreach and impactful communication.

With AI-validated accuracy and a Best Price Guarantee, Success.ai provides the ultimate solution for accessing and engaging global HR professionals. Contact us now to elevate your business strategy with precise and reliable data!

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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.