Historical Dataset of Hope Chinese Charter School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2013-2023),Total Classroom Teachers Trends Over Years (2013-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2013-2023),American Indian Student Percentage Comparison Over Years (2013-2018),Asian Student Percentage Comparison Over Years (2013-2023),Hispanic Student Percentage Comparison Over Years (2013-2023),Black Student Percentage Comparison Over Years (2013-2023),White Student Percentage Comparison Over Years (2013-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2013-2023),Reading and Language Arts Proficiency Comparison Over Years (2015-2022),Math Proficiency Comparison Over Years (2015-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2015-2023)

The replication folder includes the Stata do-files and data files (Stata version 15) that can be used to replicate the empirical analysis in the paper. The data sources and variable definitions can be found in section 4 of the paper along with various Internet appendices. The main description of the replication files are as follows:

Time series data for the statistic Adjusted savings: education expenditure (current US$) and country China. Indicator Definition:Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment.The indicator "Adjusted savings: education expenditure (current US$)" stands at 314.54 Billion usd as of 12/31/2021, the highest value at least since 12/31/1971, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 20.61 percent compared to the value the year prior.The 1 year change in percent is 20.61.The 3 year change in percent is 27.02.The 5 year change in percent is 57.21.The 10 year change in percent is 134.89.The Serie's long term average value is 59.62 Billion usd. It's latest available value, on 12/31/2021, is 427.54 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1971, to it's latest available value, on 12/31/2021, is +22,412.29%.The Serie's change in percent from it's maximum value, on 12/31/2021, to it's latest available value, on 12/31/2021, is 0.0%.

This dataset is derived from the Whoʻs Who of American Returned Students 遊美同學錄 [Youmei Tongxue Lu] published in Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early liumei's experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). The dataset records all the institutions to which the students were affiliated in the course of their lives, including the educational institutions in which they studied in China, the United States, and other countries; the public or private organizations in which they were employed; as well as their memberships in clubs and associations. The names of organizations were retrieved automatically from the Chinese biographies using named entity recognition (SpaCy model), then manually cleaned, classified, and validated by the author. The attached file contains three tabs for (1) the list of affiliations (data); (2) the classification of organizations (class), and (3) the description of variables (key). The dataset records a total of 2,883 affiliations, linking 401 unique individuals to 1,344 unique institutions, distributed as followed: category n education 565 association 271 administration 132 business 110 facility 92 media 66 government 49 factory 30 other 22 military 7

Time series data for the statistic Adjusted savings: education expenditure (current US$) and country Hong Kong SAR, China.

Historical Dataset of East China School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,American Indian Student Percentage Comparison Over Years (1999-2005),Asian Student Percentage Comparison Over Years (2006-2022),Hispanic Student Percentage Comparison Over Years (1995-2023),Black Student Percentage Comparison Over Years (2007-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2014-2023),Comparison of Students By Grade Trends

The following dataset contains the list of the 418 members - both Chinese and non-Chinese - of the American University Club of China (Shanghai), based on a directory published in 1936. Established around 1902, the American University Club (AUC) was one of the earliest and largest organizations of American university alumni in pre-1949 China. The attached file comprises two tabs, one for the data and one for describing the variables (fields). The dataset includes the following variables: Group Name Description DataType Identity Name_full Full name, as given in source (English or Wade-Giles) Raw Identity SurName Surname as given in source Raw Identity FirstName First name or initials, as given in source Raw Identity Name_zh Full name in Chinese Raw Identity Name_py Pinyin transliteration of full name Cooked Identity Nationality Nationality or country of origin Cooked Identity Deceased Deceased member or not Raw Club Membership Life_member Life membership in AUC (year of admission) Raw Education University University in which the individual studied Raw Education State State in which the university was located Cooked Education Country_edu Country in which the university was located Cooked Education Degree_source Academic degree, as given in source Raw Education Degree_level Level of qualification Cooked Education Field_main Field of study (general category) Cooked Education Field_2 Field of study (subcategory) Cooked Education Year_start Year of enrollment Raw Education Year_end Year of graduation Raw Education Honorary Honorary degree Cooked Career Employer_main Employer (name of institution, main level) Raw Career Employer_2 Employer (institution sublevel) Raw Career Sector_1 Sector of employment (main category) Cooked Career Sector_2 Sector of employment (subcategory) Cooked Career Country_1936 Country of residence or employment (1936) Cooked Career City City of residence or employment (1936) Raw Career Street_name Current address (business or residence): street name (main) Raw Career Street_2 Current address (business or residence): street name (secondary) Raw Career Street_nbr Current address (business or residence): street number Raw Career Building Current address (business or residence): building name Raw Metadata Page Source: Page number in the original source Raw Note: "DataType" indicates whether the information was provided as such in the original source, or whether it was re-processed by the historian. Référence: American University Club of Shanghai. American University Men in China. Shanghai: Comacrib Press, 1936.

China Number of Foreign Student: America data was reported at 38,077.000 Person in 2016. This records an increase from the previous number of 34,934.000 Person for 2015. China Number of Foreign Student: America data is updated yearly, averaging 25,557.000 Person from Dec 2000 (Median) to 2016, with 13 observations. The data reached an all-time high of 38,077.000 Person in 2016 and a record low of 4,703.000 Person in 2003. China Number of Foreign Student: America data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GD: Number of Student: Foreign Student.

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce.While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity.Here, we fill this gap by presenting Course-Skill Atlas -- a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education's role in preparing students for the labor market.Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.

Comprehensive demographic dataset for China, ME, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Generative AI in Education Market Outlook

According to our latest research, the Generative AI in Education market size reached USD 1.92 billion in 2024, reflecting robust adoption across global educational ecosystems. The market is projected to grow at a remarkable CAGR of 36.8% from 2025 to 2033, culminating in a forecasted market size of USD 27.67 billion by 2033. This rapid expansion is primarily driven by the increasing demand for personalized learning experiences, administrative automation, and innovative content generation tools within educational institutions. The integration of generative AI technologies is reshaping traditional pedagogical models, enabling more adaptive, efficient, and student-centric approaches to learning and administration.

A primary growth factor for the Generative AI in Education market is the surging demand for personalized learning solutions. Educational institutions, from K-12 to higher education and corporate training, are increasingly leveraging AI-powered platforms that dynamically tailor content, assessments, and feedback to individual student needs. This customization improves learning outcomes, engagement, and retention rates. Generative AIÂ’s ability to analyze vast datasets and generate unique learning pathways is revolutionizing how educators approach differentiated instruction. Furthermore, the proliferation of digital devices and high-speed internet access has made it easier for institutions to deploy these advanced AI solutions, further accelerating market growth.

Another significant driver is the automation of administrative and grading tasks, which has historically consumed a substantial portion of educators' time. Generative AI tools are now capable of automating grading for assignments, quizzes, and even subjective essays with remarkable accuracy. This automation not only enhances efficiency but also reduces human bias and error. In addition, administrative processes such as student enrollment, scheduling, and resource allocation are increasingly being managed by AI-driven systems. These advancements free up valuable time for educators, allowing them to focus more on teaching and student engagement, while simultaneously lowering operational costs for institutions.

The expansion of generative AI applications in education is also propelled by advancements in natural language processing (NLP) and machine learning algorithms. These technologies underpin the development of virtual tutors, content generation tools, and interactive educational platforms that can simulate human-like interactions and provide real-time support to learners. The shift towards hybrid and remote learning models, particularly post-pandemic, has further highlighted the need for scalable, AI-driven educational solutions. As institutions seek to provide consistent and high-quality education regardless of location, generative AI is positioned as a critical enabler of this transformation.

Regionally, North America currently leads the Generative AI in Education market owing to its advanced digital infrastructure, significant investments in educational technology, and a high concentration of leading AI solution providers. However, Asia Pacific is emerging as a high-growth region, fueled by large student populations, government initiatives to digitize education, and increasing adoption of AI technologies in countries like China, India, and Japan. Europe is also witnessing steady growth, driven by progressive educational policies and a strong focus on innovation. Latin America and the Middle East & Africa are gradually catching up, supported by improving internet penetration and growing awareness of AIÂ’s potential in education.

The role of Artificial Intelligence (AI) in Education is becoming increasingly pivotal as institutions strive to enhance learning experiences and outcomes. AI technologies are being integrated into educational systems to provide personalized learning experiences, automate administrative tasks, and facilitate innovative teaching methods. By analyzing student data, AI can tailor educational content to individual learning styles and paces, ensuring that each student receives the support they need to succeed. This personalized approach not only improves student engagement but also helps educators identify areas where students

Time series data for the statistic Services, value added (current US$) and country China. Indicator Definition:Services correspond to ISIC divisions 50-99. They include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges and import duties. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Data are in current U.S. dollars.The indicator "Services, value added (current US$)" stands at 10.64 Trillion usd as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 3.34 percent compared to the value the year prior.The 1 year change in percent is 3.34.The 3 year change in percent is 6.60.The 5 year change in percent is 32.27.The 10 year change in percent is 102.39.The Serie's long term average value is 1.83 Trillion usd. It's latest available value, on 12/31/2024, is 481.20 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1963, to it's latest available value, on 12/31/2024, is +76,781.28%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

Comprehensive demographic dataset for China Grove, TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

ECLS-B is a longitudinal study that followed a nationally representative sample of approximately 10,700 participating children from birth through kindergarten entry. The children participating in the study were born in the United States in 2001, and came from diverse socioeconomic and racial/ethnic backgrounds, with over-samples of Chinese children, other Asian and Pacific Islander children, American Indian and Alaska Native children, twins, and children born with low and very low birth weight.

Comprehensive demographic dataset for China Spring, TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

CCNP takes its pilot stage (2013 – 2022) of the first ten-year. It aims at establishing protocols on the Chinese normative brain development trajectories across the human lifespan. It implements a structured multi-cohort longitudinal design (or accelerated longitudinal design), which is particularly viable for lifespan trajectory studies, and optimal for recoverable missing data. The CCNP pilot comprises three connected components: developing CCNP (devCCNP, baseline age = 6-18 years, 12 age cohorts, 3 waves, interval = 15 months), maturing CCNP (matCCNP, baseline age = 18-60 years, 14 age cohorts, 3 waves, interval = 39 months) and ageing CCNP (ageCCNP, baseline age = 60-84 years, 12 age cohorts, 3 waves, interval = 27 months). The developmental component of CCNP (devCCNP, 2013-2022), also known as "Growing Up in China", a ten-year's pilot stage of CCNP, has established follow-up cohorts in Chongqing (CKG, Southwest China) and Beijing (PEK, Northeast China). It is an ongoing project focused on longitudinal developmental research as creating and sharing a large-scale multimodal dataset for typically developing Chinese children and adolescents (ages 6.0-17.9 at enrollment) carried out in both school- and community-based samples. The devCCNP houses longitudinal data about demographics, biophysical measures, psychological and behavioral assessments, cognitive phenotyping, ocular-tracking, as well as multimodal magnetic resonance imaging (MRI) of brain's resting and naturalistic viewing function, diffusion structure and morphometry. With the collection of longitudinal structured images and psychobehavioral samples from school-age children and adolescents in multiple cohorts, devCCNP has constructed a full school-age brain template and its growth curve reference for Han Chinese which demonstrated the difference in brain development between Chinese and American school-aged children.*This dataset contains only T1-weighted MRI, Resting-state fMRI and Diffusion Tensor MRI data of devCCNP.To access the devCCNP Lite data, investigators must complete the application file Data Use Agreement on CCNP (DUA-CCNP) at http://deepneuro.bnu.edu.cn/?p=163 and have it reviewed and approved by the Chinese Color Nest Consortium (CCNC). All terms specified by the DUA-CCNP must be complied with. Meanwhile, the baseline CKG Sample on brain imaging are available to researchers via the International Data-sharing Neuroimaging Initiative (INDI) through the Consortium for Reliability and Reproducibility (CoRR). More information about CCNP can be found at: http://deepneuro.bnu.edu.cn/?p=163 or https://github.com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by the CCNC, and please read the Data Use Agreement and contact us via deepneuro@bnu.edu.cn.

BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

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Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

What will be the Size of the Data Visualization Tools Market during the forecast period?

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven sales, data connectivit