The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

This dataset provides values for RESEARCHERS IN R D PER MILLION PEOPLE WB DATA.HTML; reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

About this dataset

Context

The dataset tabulates the population of Blue Earth City township by race. It includes the population of Blue Earth City township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Blue Earth City township across relevant racial categories.

Key observations

The percent distribution of Blue Earth City township population by race (across all racial categories recognized by the U.S. Census Bureau): 95.80% are white, 0.19% are Asian and 4.01% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Blue Earth City township
• Population: The population of the racial category (excluding ethnicity) in the Blue Earth City township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Blue Earth City township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blue Earth City township Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Blue Earth City township Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Blue Earth City township, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Blue Earth City township.

Key observations

Among the Hispanic population in Blue Earth City township, regardless of the race, the largest group is of Mexican origin, with a population of 26 (100% of the total Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Blue Earth City township
• Population: The population of the specific origin for Hispanic or Latino population in the Blue Earth City township is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Blue Earth City township total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blue Earth City township Population by Race & Ethnicity. You can refer the same here

This dataset provides values for PERSONAL COMPUTERS PER 100 PEOPLE WB reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

By 2025, forecasts suggest that there will be more than 75 billion Internet of Things (IoT) connected devices in use. This would be a nearly threefold increase from the IoT installed base in 2019.

What is the Internet of Things?

The IoT refers to a network of devices that are connected to the internet and can “communicate” with each other. Such devices include daily tech gadgets such as the smartphones and the wearables, smart home devices such as smart meters, as well as industrial devices like smart machines. These smart connected devices are able to gather, share, and analyze information and create actions accordingly. By 2023, global spending on IoT will reach 1.1 trillion U.S. dollars.

How does Internet of Things work?

IoT devices make use of sensors and processors to collect and analyze data acquired from their environments. The data collected from the sensors will be shared by being sent to a gateway or to other IoT devices. It will then be either sent to and analyzed in the cloud or analyzed locally. By 2025, the data volume created by IoT connections is projected to reach a massive total of 79.4 zettabytes.

Privacy and security concerns 

Given the amount of data generated by IoT devices, it is no wonder that data privacy and security are among the major concerns with regard to IoT adoption. Once devices are connected to the Internet, they become vulnerable to possible security breaches in the form of hacking, phishing, etc. Frequent data leaks from social media raise earnest concerns about information security standards in today’s world; were the IoT to become the next new reality, serious efforts to create strict security stands need to be prioritized.

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

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

Market Summary of Database Security Market:

• The Global Database Security market size in 2023 was XX Million. The Database Security Industry's compound annual growth rate (CAGR) will be XX% from 2024 to 2031. • The database security industry is growing faster and is expected to expand at a faster rate due to these strict regulatory frameworks. Also, the increase in advanced technology for better protection of data is driving the growth of the Database security market. • The dominating segment is the software. It includes encryption, auditing, tokenization, data masking, and access control management. • Due to the increase in internet users, remote working demand, and risk of data breaches, the COVID-19 pandemic has had a beneficial effect on the market for data security solutions. • The database security market is dominated by North America in terms of both revenue and market share. This can be attributed to the region's concentration of significant industry participants and increasing technical advancements in their product line.

Market Dynamics of Database Security Market:

Key Drivers of Database Security Market:

An increase in advanced technology for better protection of data is driving the growth of the Database security market

Retail, banking, healthcare, and government are just a few of the industries where a strong data security plan could help companies stay compliant and lower their exposure to threats. When data is used by the principles of availability, confidentiality, and integrity, it becomes the most precious resource that aids in decision-making, strategic endeavor execution, and the development of closer relationships between companies and their clients. For Instance, Records from thousands of people assembled and reindexed leaks, breaches, and privately sold databases are part of a supermassive Mother of all Breaches or MOAB. The huge release includes information from multiple earlier breaches, totaling an incredible 12 gigabytes of data covering an incredible 26 billion records. The leak is most likely the biggest to be found to date and includes user data from Tencent, Weibo, LinkedIn, Twitter, and other networks.(Source: https://cybernews.com/security/billions-passwords-credentials-leaked-mother-of-all-breaches/) Hence, the protection of data is of utmost importance in almost all sectors. Hardware-based security, data backup and resilience, data erasure, data masking, encryption, firewalls, and authentication and authorization are examples of data security technologies. It is essential to corporate development, operations, and financing. Companies can better comply with regulatory standards and avoid data breaches and reputational harm by securing their data. Data is locked up by modern encryption methods with a single key, making it only accessible to the key holder. AES-compliant standards are used by many databases to encrypt data. These remedies are the most robust against hardware loss, possibly due to theft. The data is protected even if the encryption key is incorrect. For Instance, An innovative method for protecting personal information for use with generative artificial intelligence has been released, according to security company Baffle. Assuring that their regulated data is compliant and cryptographically safe, Baffle Data Protection for AI interacts with current data pipelines to help businesses expedite generative AI initiatives. According to Baffle, the method encrypts sensitive data using the advanced encryption standard (AES) algorithm so that outside parties cannot view private information in plaintext. (Source: https://baffle.io/news/baffle-releases-encryption-solution-to-secure-data-for-generative-ai/) Hence, technology is playing an important role in reducing data breaches and protecting data, which is eventually increasing the market for database security as many companies require data protection.

The Database Security Market is driven by the strict regulatory framework to address information security

Regulatory frameworks can establish standards that developers and users must follow to guarantee a secure database. The market is growing as a result of increasingly stringent regulations enforced globally to protect sensitive data by governments and other relevant authorities in numerous nations. ...

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

United States Employment: American Indian or Alaska Native data was reported at 1,980.000 Person th in Feb 2025. This records an increase from the previous number of 1,956.000 Person th for Jan 2025. United States Employment: American Indian or Alaska Native data is updated monthly, averaging 1,327.500 Person th from Jan 2000 (Median) to Feb 2025, with 302 observations. The data reached an all-time high of 1,980.000 Person th in Feb 2025 and a record low of 837.000 Person th in Oct 2003. United States Employment: American Indian or Alaska Native data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Employment.

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

The Comparative Political Economy Database (CPEDB) began at the Centre for Learning, Social Economy and Work (CLSEW) at the Ontario Institute for Studies in Education at the University of Toronto (OISE/UT) as part of the Changing Workplaces in a Knowledge Economy (CWKE) project. This data base was initially conceived and developed by Dr. Wally Seccombe (independent scholar) and Dr. D.W. Livingstone (Professor Emeritus at the University of Toronto). Seccombe has conducted internationally recognized historical research on evolving family structures of the labouring classes (A Millennium of Family Change: Feudalism to Capitalism in Northwestern Europe and Weathering the Storm: Working Class Families from the Industrial Revolution to the Fertility Decline). Livingstone has conducted decades of empirical research on class and labour relations. A major part of this research has used the Canadian Class Structure survey done at the Institute of Political Economy (IPE) at Carleton University in 1982 as a template for Canadian national surveys in 1998, 2004, 2010 and 2016, culminating in Tipping Point for Advanced Capitalism: Class, Class Consciousness and Activism in the ‘Knowledge Economy’ (https://fernwoodpublishing.ca/book/tipping-point-for-advanced-capitalism) and a publicly accessible data base including all five of these Canadian surveys (https://borealisdata.ca/dataverse/CanadaWorkLearningSurveys1998-2016). Seccombe and Livingstone have collaborated on a number of research studies that recognize the need to take account of expanded modes of production and reproduction. Both Seccombe and Livingstone are Research Associates of CLSEW at OISE/UT. The CPEDB Main File (an SPSS data file) covers the following areas (in order): demography, family/household, class/labour, government, electoral democracy, inequality (economic, political & gender), health, environment, internet, macro-economic and financial variables. In its present form, it contains annual data on 725 variables from 12 countries (alphabetically listed): Canada, Denmark, France, Germany, Greece, Italy, Japan, Norway, Spain, Sweden, United Kingdom and United States. A few of the variables date back to 1928, and the majority date from 1960 to 1990. Where these years are not covered in the source, a minority of variables begin with more recent years. All the variables end at the most recent available year (1999 to 2022). In the next version developed in 2025, the most recent years (2023 and 2024) will be added whenever they are present in the sources’ datasets. For researchers who are not using SPSS, refer to the Chart files for overviews, summaries and information on the dataset. For a current list of the variable names and their labels in the CPEDB data base, see the excel file: Outline of SPSS file Main CPEDB, Nov 6, 2023. At the end of each variable label in this file and the SPSS datafile, you will find the source of that variable in a bracket. If I have combined two variables from a given source, the bracket will begin with WS and then register the variables combined. In the 14 variables David created at the beginning of the Class Labour section, you will find DWL in these brackets with his description as to how it was derived. The CPEDB’s variables have been derived from many databases; the main ones are OECD (their Statistics and Family Databases), World Bank, ILO, IMF, WHO, WIID (World Income Inequality Database), OWID (Our World in Data), Parlgov (Parliaments and Governments Database), and V-Dem (Varieties of Democracy). The Institute for Political Economy at Carleton University is currently the main site for continuing refinement of the CPEDB. IPE Director Justin Paulson and other members are involved along with Seccombe and Livingstone in further development and safe storage of this updated database both at the IPE at Carleton and the UT dataverse. All those who explore the CPEDB are invited to share their perceptions of the entire database, or any of its sections, with Seccombe generally (wseccombe@sympatico.ca) and Livingstone for class/labour issues (davidlivingstone@utoronto.ca). They welcome any suggestions for additional variables together with their data sources. A new version CPEDB will be created in the spring of 2025 and installed as soon as the revision is completed. This revised version is intended to be a valuable resource for researchers in all of the included countries as well as Canada.

Chart and table of Malaysia population from 1950 to 2025. United Nations projections are also included through the year 2100.

Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

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

Germany DE: Labour Force Participation Rate: National Estimate: Male: % of Male Population Aged 15+ data was reported at 66.716 % in 2023. This records an increase from the previous number of 66.563 % for 2022. Germany DE: Labour Force Participation Rate: National Estimate: Male: % of Male Population Aged 15+ data is updated yearly, averaging 66.703 % from Dec 1983 (Median) to 2023, with 41 observations. The data reached an all-time high of 71.968 % in 1990 and a record low of 64.736 % in 2004. Germany DE: Labour Force Participation Rate: National Estimate: Male: % of Male Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.