In 2021, almost ** percent of respondents from the United States and United Kingdom stated managing between 1PB and * PB of data. Organizations are collecting and storing increasing amounts of data to use for different purposes. Most of the data collected is unstructured data.

In 2020, the banking sector led in terms of data-driven decision making within organizations, with ** percent of respondents indicating as such. Other noteworthy sectors for data-driven decision making within organizations are insurance and telecom.

BackgroundPhysical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle. Regular engagement in physical activity can produce immediate and long term health benefits. However, physical activity levels are not as high as might be expected. For example, according to the global World Health Organization (WHO) 2017 statistics, more than 80% of the world’s adolescents are insufficiently physically active. In response to this problem, physical activity programs have become popular, with step counts commonly used to measure program performance. Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance. This study reviews the statistical methods that are used to model and evaluate physical activity programs, using step counts.MethodsAdhering to PRISMA guidelines, this review systematically searched for relevant journal articles which were published between January 2000 and August 2017 in any of three databases (PubMed, PsycINFO and Web of Science). Only the journal articles which used a statistical model in analysing step counts for a healthy sample of participants, enrolled in an intervention involving physical exercise or a physical activity program, were included in this study. In these programs the activities considered were natural elements of everyday life rather than special activity interventions.ResultsThis systematic review was able to identify 78 unique articles describing statistical models for analysing step counts obtained through physical activity programs. General linear models and generalized linear models were the most popular methods used followed by multilevel models, while structural equation modeling was only used for measuring the personal and psychological factors related to step counts. Surprisingly no use was made of time series analysis for analysing step count data. The review also suggested several strategies for the personalisation of physical activity programs.ConclusionsOverall, it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial, demographic, weather and climatic factors. Statistical models can provide a better understanding of the impact of these factors, allowing for the provision of more personalised physical activity programs, which are expected to produce better immediate and long-term outcomes for participants. It is hoped that this review will identify the statistical methods which are most suitable for this purpose.

This statistic shows the respondents' perceptions of the importance of data for their organizations as of 2018. More than half of respondents stated that they "totally agree" that data is essential to their organization's strategy as of 2018.

WHOSIS, the WHO Statistical Information System, is an interactive database bringing together core health statistics for the 193 WHO Member States. It comprises more than 100 indicators, which can be accessed by way of a quick search, by major categories, or through user-defined tables. The data can be further filtered, tabulated, charted and downloaded. The data are also published annually in the World Health Statistics Report released in May. The WHO Statistical Information System is the guide to health and health-related epidemiological and statistical information available from the World Health Organization. Most WHO technical programs make statistical information available, and they will be linked from here. Sponsors: WHOSIS is supported by the World Health Organization. Note: The WHO Statistical Information System (WHOSIS) has been incorporated into the Global Health Observatory (GHO) to provide you with more data, more tools, more analysis and more reports.

To establish a government information disclosure system, facilitate the public sharing and fair use of government information, safeguard people's right to know, enhance public understanding, trust, and oversight of public affairs, the "Statistical Table of Subsidized Not-for-Profit Organizations and Individuals" from 2013 onwards has been opened for access, providing various data such as subsidy items, approval dates, subsidy recipients, and total subsidy amounts.

A table showing statistics on professional work regulation 2023

Provide statistical information on the settlement and declaration of organizations and their working groups from 2013.

Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. We show experimentally that, in agreement with our predictions, receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.

Annual extract of select Form 990 financial data items captured on the IRS Master File produced solely for release to the general public.

Armenia Construction Organizations Statistics: Number of Construction Organizations data was reported at 1,135.000 Unit in 2021. This records an increase from the previous number of 1,021.000 Unit for 2020. Armenia Construction Organizations Statistics: Number of Construction Organizations data is updated yearly, averaging 814.500 Unit from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 1,135.000 Unit in 2021 and a record low of 362.000 Unit in 2000. Armenia Construction Organizations Statistics: Number of Construction Organizations data remains active status in CEIC and is reported by Statistical Committee of the Republic of Armenia. The data is categorized under Global Database’s Armenia – Table AM.EA003: Construction Organizations Statistics.

In 2020, the respondents surveyed demonstrated a ** percent increase in implementing data-driven decision making within their global organizations when compared to 2018. Although there was a significant increase, only ** percent of respondents surveyed indicated that decision making in their organizations is data-driven, hence, which means that there is still a significant portion of companies without a focus in data-driven decision making.

App Data Statistics Tool Market Outlook

The global app data statistics tool market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach USD 11.9 billion by 2032, growing at a CAGR of 9.2% during the forecast period. Several growth factors, including the escalating demand for data-driven decision-making and the rise in mobile app usage, are driving this market. As organizations increasingly recognize the value of data analytics in enhancing user engagement and optimizing app performance, the adoption of app data statistics tools is expected to surge significantly.

The growth of the app data statistics tool market is primarily fueled by the exponential increase in mobile app usage worldwide. With billions of smartphone users generating vast amounts of data daily, companies are leveraging app data statistics tools to gain actionable insights. These tools help in understanding user behavior, tracking app performance, and identifying areas for improvement. Furthermore, the growing emphasis on personalized user experiences has led to an increased demand for sophisticated analytics tools, thereby driving market growth.

Another critical growth factor is the rising importance of data-driven decision-making in various industries. Organizations across sectors such as BFSI, healthcare, retail, and media are increasingly relying on app data statistics tools to make informed decisions. These tools enable businesses to analyze large datasets, uncover trends, and optimize their strategies. The adoption of analytics tools is also propelled by the need to improve customer satisfaction and loyalty, as companies strive to offer tailored experiences to their users. The integration of artificial intelligence and machine learning in analytics tools further enhances their efficiency and accuracy, contributing to market growth.

Moreover, the market is benefitting from technological advancements and the increasing availability of advanced analytics tools. Innovations such as real-time analytics, predictive analytics, and big data analytics are enhancing the capabilities of app data statistics tools. These advancements enable organizations to gain deeper insights and make faster, more accurate decisions. Additionally, the proliferation of cloud-based solutions is making analytics tools more accessible and affordable for businesses of all sizes. Cloud deployment offers scalability, flexibility, and cost-efficiency, which are particularly attractive to small and medium enterprises (SMEs).

The role of Product Analytics Software is becoming increasingly significant in the realm of app data statistics tools. These software solutions are designed to help businesses understand how users interact with their products, providing insights that are crucial for enhancing user experience and driving product development. By analyzing user data, companies can identify trends and patterns that inform strategic decisions, such as feature enhancements and marketing strategies. The integration of Product Analytics Software with app data statistics tools enables businesses to gain a comprehensive view of user behavior, facilitating more informed decision-making and ultimately leading to improved product offerings.

Regionally, North America holds the largest market share, driven by the presence of numerous tech giants and a high adoption rate of advanced technologies. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period. The rapid digitization, increasing smartphone penetration, and the rising number of app developers in countries like China and India are driving the demand for app data statistics tools. Europe also presents significant growth opportunities, with increasing investments in technology and data analytics across various industries. Latin America and the Middle East & Africa are emerging markets with growing awareness and adoption of analytics tools.

Component Analysis

The app data statistics tool market is segmented by components into software and services. Software components dominate the market, driven by the demand for sophisticated analytics solutions that can process vast amounts of data. These software tools are designed to collect, analyze, and visualize data, enabling organizations to derive meaningful insights. The growing adoption of artificial intelligence and machine learning technologies in software solutions further enhances their capabilities, making them indispensable for

• The Ministry of the Interior and Safety publishes the 'Administrative Safety Statistical Yearbook' every year by compiling statistical data from the headquarters of the Ministry of the Interior and Safety and its affiliated organizations in accordance with the 'Ministry of the Interior and Safety Statistics Management Regulations'. - The statistical information by field included in the 'Administrative Safety Statistical Yearbook' is provided as an open API so that it can be used in various fields in both the public and private sectors. - This open API provides statistical information including the personnel of central responsible operating organizations such as general positions (Grades 3 to 9), special positions (Grades 3 to 9), and specific positions (police and fire, education) among the 'government organizations' included in the 'Administrative Safety Statistical Yearbook'. - In addition, the 'Administrative Safety Statistical Yearbook' can be downloaded in PDF format from the Ministry of the Interior and Safety website at Policy Data > Statistics > Statistical Yearbook/Statistics by Subject.

Percentage of total non-profit organizations, by region, size, International Classification of Non-Profit Organizations (ICNPO) and organization type, Canada, 2023.

Armenia Construction Organizations Statistics: Average Number of Payroll Workers data was reported at 20.200 Person th in 2021. This records an increase from the previous number of 19.600 Person th for 2020. Armenia Construction Organizations Statistics: Average Number of Payroll Workers data is updated yearly, averaging 17.650 Person th from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 22.700 Person th in 2000 and a record low of 11.400 Person th in 2004. Armenia Construction Organizations Statistics: Average Number of Payroll Workers data remains active status in CEIC and is reported by Statistical Committee of the Republic of Armenia. The data is categorized under Global Database’s Armenia – Table AM.EA003: Construction Organizations Statistics.

Dataset Overview

This dataset contains global COVID-19 case and death data by country, collected directly from the official World Health Organization (WHO) COVID-19 Dashboard. It provides a comprehensive view of the pandemic’s impact worldwide, covering the period up to 2025. The dataset is intended for researchers, analysts, and anyone interested in understanding the progression and global effects of COVID-19 through reliable, up-to-date information.

Source Information

• Website: WHO COVID-19 Dashboard
• Organization: World Health Organization (WHO)
• Data Coverage: Global (by country/territory)
• Time Period: Up to 2025

The World Health Organization is the United Nations agency responsible for international public health. The WHO COVID-19 Dashboard is a trusted source that aggregates official reports from countries and territories around the world, providing daily updates on cases, deaths, and other key metrics related to COVID-19.

Dataset Contents

• Country/Region: The name of the country or territory.
• Date: Reporting date.
• New Cases: Number of new confirmed COVID-19 cases.
• Cumulative Cases: Total confirmed COVID-19 cases to date.
• New Deaths: Number of new confirmed deaths due to COVID-19.
• Cumulative Deaths: Total deaths reported to date.
• Additional fields may include population, rates per 100,000, and more (see data files for details).

How to Use

This dataset can be used for: - Tracking the spread and trends of COVID-19 globally and by country - Modeling and forecasting pandemic progression - Comparative analysis of the pandemic’s impact across countries and regions - Visualization and reporting

Data Reliability

The data is sourced from the WHO, widely regarded as the most authoritative source for global health statistics. However, reporting practices and data completeness may vary by country and may be subject to revision as new information becomes available.

Acknowledgements

Special thanks to the WHO for making this data publicly available and to all those working to collect, verify, and report COVID-19 statistics.

United States Payees: SE: Non-Profit Organization data was reported at 1,518.000 Person in 2015. This records an increase from the previous number of 1,375.000 Person for 2014. United States Payees: SE: Non-Profit Organization data is updated yearly, averaging 322.000 Person from Sep 1996 (Median) to 2015, with 15 observations. The data reached an all-time high of 1,518.000 Person in 2015 and a record low of 208.000 Person in 1996. United States Payees: SE: Non-Profit Organization data remains active status in CEIC and is reported by Pension Benefit Guaranty Corporation. The data is categorized under Global Database’s USA – Table US.G079: Single Employer Program Statistics.

MethodsThe objective of this project was to determine the capability of a federated analysis approach using DataSHIELD to maintain the level of results of a classical centralized analysis in a real-world setting. This research was carried out on an anonymous synthetic longitudinal real-world oncology cohort randomly splitted in three local databases, mimicking three healthcare organizations, stored in a federated data platform integrating DataSHIELD. No individual data transfer, statistics were calculated simultaneously but in parallel within each healthcare organization and only summary statistics (aggregates) were provided back to the federated data analyst.Descriptive statistics, survival analysis, regression models and correlation were first performed on the centralized approach and then reproduced on the federated approach. The results were then compared between the two approaches.ResultsThe cohort was splitted in three samples (N1 = 157 patients, N2 = 94 and N3 = 64), 11 derived variables and four types of analyses were generated. All analyses were successfully reproduced using DataSHIELD, except for one descriptive variable due to data disclosure limitation in the federated environment, showing the good capability of DataSHIELD. For descriptive statistics, exactly equivalent results were found for the federated and centralized approaches, except some differences for position measures. Estimates of univariate regression models were similar, with a loss of accuracy observed for multivariate models due to source database variability.ConclusionOur project showed a practical implementation and use case of a real-world federated approach using DataSHIELD. The capability and accuracy of common data manipulation and analysis were satisfying, and the flexibility of the tool enabled the production of a variety of analyses while preserving the privacy of individual data. The DataSHIELD forum was also a practical source of information and support. In order to find the right balance between privacy and accuracy of the analysis, set-up of privacy requirements should be established prior to the start of the analysis, as well as a data quality review of the participating healthcare organization.

United States Employment: NF: OS: Professional & Similar Organization data was reported at 502.600 Person th in Sep 2018. This records a decrease from the previous number of 515.800 Person th for Aug 2018. United States Employment: NF: OS: Professional & Similar Organization data is updated monthly, averaging 470.600 Person th from Jan 1990 (Median) to Sep 2018, with 345 observations. The data reached an all-time high of 532.700 Person th in Aug 2008 and a record low of 363.500 Person th in Jan 1990. United States Employment: NF: OS: Professional & Similar Organization data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.