Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.

I’ve decided to host different light/medium size by using PostgreSQL, MySQL and SQL Server backends (in strict descending order of preference!).

Why 3 database backends? I think there are a ton of small edge cases when moving between DB back ends and so testing lots with live databases is quite valuable. With this resource you can benchmark speed, compression, and DDL types.

Please send me a tweet if you need the connection strings for your lectures or workshops. My Twitter username is @pachamaltese. See the SQL dumps on each section to have the data locally.

The Chinese ebook database market presents a significant opportunity, exhibiting robust growth. While the exact CAGR isn't provided, considering the global growth in digital content and the substantial Chinese market, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the period 2025-2033 would be around 15%. This projection factors in increased internet penetration, rising smartphone usage, government initiatives promoting digital literacy, and the increasing demand for accessible educational and research resources. With a 2025 market size of $502.3 million, this CAGR suggests a substantial market expansion by 2033. Key drivers include the rising popularity of e-readers and tablets, the increasing adoption of online learning platforms, and the growing need for efficient information retrieval in academic and research settings. The market is segmented by application (university, research institute, public library) and type (arts, business, engineering, medicine, sciences), reflecting the diverse needs of various user groups. Leading companies like CEPIEC, Beijing Zhongke, and China National Sci-Tech Information are capitalizing on these trends, though competition is intensifying as new players enter the market. Growth will likely be influenced by factors such as government regulations regarding digital content, technological advancements, and the ongoing evolution of user preferences. The segmentation of the Chinese ebook database market allows for a nuanced understanding of market dynamics. The academic and research segments are expected to drive substantial growth, fueled by the expanding higher education sector and the increasing reliance on digital resources for research. The public library segment also presents a promising avenue for growth, with ongoing digitization efforts and increasing accessibility initiatives. The diverse subject categories within the "type" segmentation (arts, business, engineering, etc.) highlight the breadth of content available and the specific demands of various user groups. Market restraints might include concerns about copyright infringement, the need for improved digital infrastructure in certain regions, and potential challenges in managing the vast amount of digital content effectively. However, the overall market outlook remains positive, driven by the fundamental shift towards digital information consumption in China and the inherent advantages of ebook databases over traditional print materials.

The online database market is projected to witness significant growth, with a market size of XXX million in 2025 and a CAGR of XX% during the forecast period from 2025 to 2033. This growth is attributed to increasing adoption of cloud computing, growing demand for data analytics, and government initiatives to promote digitalization. Cloud-based databases offer scalability, cost-effectiveness, and ease of deployment, making them attractive for businesses of all sizes. Data analytics is essential for businesses to gain insights from their data and make informed decisions. Online databases provide a centralized platform for data storage and management, facilitating efficient data analysis. Governments across the globe are implementing policies to promote digitalization, driving the adoption of online databases in various sectors, including government, healthcare, and education. Key trends shaping the market include the rise of big data, the adoption of artificial intelligence (AI) and machine learning (ML), and the increasing importance of data security. Big data refers to the exponential growth of data volume, velocity, and variety. Online databases provide the infrastructure to handle and process vast amounts of data. AI and ML algorithms leverage online databases to learn from data and make predictions, driving innovation in various industries. Data security is of utmost importance given the growing threat of cyberattacks. Online databases implement robust security measures to protect sensitive data, ensuring compliance and building trust among users.

Data Privacy in Education Market Outlook

According to our latest research, the global Data Privacy in Education market size reached USD 6.2 billion in 2024, reflecting the sector’s rapid response to escalating data security concerns across the education ecosystem. The market is expected to expand at a robust CAGR of 15.8% during the forecast period, with projections indicating a value of USD 24.2 billion by 2033. This remarkable growth is driven by the increasing digitization of educational processes, the proliferation of online learning platforms, and stringent regulatory requirements surrounding student and faculty data protection worldwide.

The surge in digital transformation within educational institutions is a primary growth driver for the Data Privacy in Education market. As schools, universities, and training centers accelerate the adoption of learning management systems (LMS), cloud-based collaboration tools, and digital assessment platforms, the volume and sensitivity of data generated have increased exponentially. This includes personal identification details, academic records, behavioral analytics, and even biometric data. The rising frequency of cyberattacks targeting educational databases, coupled with high-profile incidents of data breaches, has heightened the urgency for robust privacy solutions. Educational stakeholders are now prioritizing investments in advanced data privacy software, secure hardware, and managed services to ensure compliance with global standards such as FERPA, GDPR, and CCPA, further fueling market expansion.

Another significant growth factor is the evolving regulatory landscape. Governments and educational authorities worldwide are enacting stricter data protection laws, compelling institutions to implement comprehensive privacy frameworks. The introduction of region-specific regulations, such as the General Data Protection Regulation (GDPR) in Europe and the Family Educational Rights and Privacy Act (FERPA) in the United States, has forced educational entities to reassess their data handling and storage practices. This has resulted in a surge in demand for tailored privacy solutions that can be seamlessly integrated with legacy systems while ensuring transparency and accountability. The need for continuous monitoring, real-time threat detection, and incident response capabilities is driving the adoption of both on-premises and cloud-based privacy technologies across all education segments.

The continued expansion of remote and hybrid learning models post-pandemic has further amplified the need for data privacy in the education sector. Online learning platforms, virtual classrooms, and digital content providers are now central to educational delivery, exposing a broader range of users—students, teachers, administrators, and parents—to potential data vulnerabilities. The shift towards personalized learning and data-driven instruction has also increased the collection and analysis of student data, necessitating more sophisticated privacy controls. As educational institutions strive to balance innovation with compliance, the Data Privacy in Education market is witnessing strong demand for solutions that offer scalability, user-centric privacy management, and integration with emerging technologies such as artificial intelligence and blockchain.

Regionally, North America continues to dominate the market, driven by high digital adoption rates, strong regulatory enforcement, and significant investments in EdTech infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by government-led digital education initiatives, rapid urbanization, and increasing awareness of data privacy risks. Europe remains a key market due to its stringent regulatory framework, while Latin America and the Middle East & Africa are witnessing steady growth as educational institutions modernize and prioritize data security. The global landscape is characterized by a diverse set of challenges and opportunities, with regional nuances shaping the adoption and evolution of data privacy solutions in education.

Component Analysis

The Component segment of the Data Privacy in Education market is divided into software, services, and hardware. Software solutions constitute the largest share, accounting for nearly 52% of the market in 2024. These include identity and access management (IAM), data encryption, data loss prevention (DLP), and privacy management platforms tai

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2023-24 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.

The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2023 to coincide with the official 2023-24 student enrollment counts collected on Fall Census Day in 2023 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.

The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website.

Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

Database Free TechBooks

This dataset is a comprehensive collection of free tech books available on the web, specifically sourced from the FreeTechBooks platform. It includes details such as the names and URLs of various free textbooks, covering a wide range of topics including computer science, programming, data science, artificial intelligence, and more. The dataset is designed for educational purposes, providing easy access to high-quality, freely available technical resources.

Dataset Details:

• The dataset consists of two columns:

• Name: The title of the book.

• URL: A direct link to the page where the book can be accessed or downloaded for free.

Features:

• Comprehensive: Contains a collection of over 1200 free tech books.
• Variety of Topics: Books span various domains such as:
• Programming Languages: (Python, Java, C++)
• Data Science & Machine Learning
• Artificial Intelligence
• Cybersecurity
• Networking
• Web Development
• Cloud Computing
• And much more.

Usage:

The dataset can be used for:

• Educational research and learning.
• Building recommendation systems for tech resources.
• Analyzing trends in the availability of open-source learning materials.
• Supporting the creation of educational tools and resources in tech-related fields.

Source:

• The data was scraped from the FreeTechBooks website, a platform that aggregates freely available textbooks on various technical topics.

Data Collection Method:

• The data was collected by iterating through 82 pages of the FreeTechBooks website, extracting the names andURLs of books listed under different topics. The dataset includes data for a total of 1200+ books.

Notes:

• All books listed are freely available and open to the public.
• URLs lead to external sites where users can read or download the books.

Dataset Size:

• Number of rows: 1200+
• Number of columns: 2 (Name, URL)

Virtual Scouting Databases Market Outlook

According to our latest research, the global virtual scouting databases market size reached USD 1.32 billion in 2024, driven by increasing digital transformation initiatives and the accelerating adoption of data-driven decision-making across various sectors. The market is projected to grow at a robust CAGR of 14.6% from 2025 to 2033, with the market size expected to reach USD 4.23 billion by 2033. This growth is largely attributed to the rising demand for efficient talent identification, recruitment, and management solutions, as well as the expanding applications of virtual scouting databases in sports, education, and enterprise environments. As per the latest research, the market’s upward trajectory is underpinned by technological advancements, integration of artificial intelligence, and the growing need for remote and scalable scouting solutions.

One of the primary growth factors for the virtual scouting databases market is the increasing digitalization of talent identification and management processes across sports organizations, educational institutions, and recruitment agencies. With the proliferation of data analytics and cloud computing, organizations are now able to streamline their scouting operations, making them more efficient, accurate, and scalable. The integration of artificial intelligence and machine learning algorithms into scouting databases allows for advanced data analysis, predictive modeling, and automated candidate matching, which significantly enhances the overall effectiveness of talent scouting. Furthermore, the shift towards remote work and virtual collaboration, accelerated by global events such as the COVID-19 pandemic, has further cemented the importance of virtual scouting databases in ensuring business continuity and operational agility.

Another significant driver is the growing emphasis on diversity, equity, and inclusion (DEI) in recruitment and talent management. Virtual scouting databases enable organizations to access a broader and more diverse pool of candidates by eliminating geographical and logistical barriers. This democratization of access not only aligns with modern workforce trends but also enhances organizational competitiveness by fostering a more inclusive environment. Additionally, the integration of advanced analytics and reporting tools within these databases empowers organizations to monitor and improve their DEI initiatives, ensuring compliance with regulatory requirements and corporate social responsibility objectives. The ability to harness big data for unbiased, data-driven decision-making is proving to be a game-changer for organizations across sectors.

The rapid evolution of the sports industry, particularly with the rise of eSports and digital sports management platforms, is also fueling the demand for virtual scouting databases. Sports organizations are increasingly leveraging these databases to identify emerging talent, analyze player performance, and optimize recruitment strategies. The use of virtual scouting tools has become indispensable for maintaining a competitive edge in a globalized sports market, where real-time data and analytics are critical for success. Moreover, the adoption of virtual scouting databases in educational institutions is enabling more effective student-athlete recruitment and talent nurturing, further expanding the market’s reach and impact. The synergy between sports, education, and technology is expected to drive sustained market growth over the forecast period.

From a regional perspective, North America currently dominates the virtual scouting databases market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced technologies, presence of leading market players, and strong focus on innovation are key factors contributing to North America’s leadership position. Meanwhile, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by increasing investments in digital infrastructure, rising internet penetration, and growing demand for talent management solutions in emerging economies such as China and India. Europe continues to be a significant market, supported by robust sports and education sectors, as well as progressive regulatory frameworks promoting digitalization and data privacy.

Component Analysis

The component segment of the virtual scouting dat

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2022-23 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.

The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2022 to coincide with the official 2022-23 student enrollment counts collected on Fall Census Day in 2022 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.

The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website.

Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing MAF/TIGER Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. School District Administrative Areas are single-purpose administrative units within which local officials provide educational administrative services for the area's residents. Currently, the Census Bureau maintains school district administrative areas for the state of Vermont. The Census Bureau obtains the boundaries, names, local education agency codes, grade ranges, and school district levels for school districts from State officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children in poverty within each school district. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to States and school districts.

Education Data Lake Market Outlook

According to our latest research, the global Education Data Lake market size reached USD 2.45 billion in 2024, demonstrating robust momentum driven by the digital transformation of the education sector. The market is projected to grow at a CAGR of 21.7% during the forecast period, reaching an estimated USD 17.3 billion by 2033. Key growth factors include the proliferation of data-driven decision-making in educational institutions, the need for scalable data storage solutions, and the increasing adoption of advanced analytics to enhance learning outcomes and administrative efficiency.

The rapid digitalization of educational processes is a primary catalyst for the growth of the Education Data Lake market. With the integration of learning management systems, online assessments, digital content, and student information systems, educational institutions generate massive volumes of structured and unstructured data. Traditional databases often struggle to handle this data deluge, leading to the adoption of data lakes that offer scalable, cost-effective storage and real-time analytics capabilities. Data lakes empower educators and administrators to gain actionable insights, personalize learning experiences, and streamline operations, fueling their widespread adoption across K-12, higher education, and vocational training environments.

Another significant growth driver is the increasing emphasis on learning analytics and performance monitoring. Educational institutions are leveraging data lakes to centralize disparate data sources, enabling holistic views of student progress, engagement, and learning outcomes. By applying advanced analytics and artificial intelligence (AI) to these unified datasets, institutions can identify at-risk students, tailor interventions, and optimize curriculum design. This data-driven approach not only enhances student success but also supports institutional accountability and continuous improvement, further propelling the expansion of the Education Data Lake market.

The proliferation of cloud-based solutions and services is also accelerating market growth. Cloud deployment offers scalability, flexibility, and cost-efficiency, making it easier for institutions to implement and manage data lakes without heavy upfront investments in infrastructure. Cloud-based education data lakes facilitate seamless integration with other EdTech platforms and enable secure, remote access to data for educators, administrators, and policymakers. As educational organizations increasingly transition to hybrid and online learning models, the demand for cloud-enabled data lake solutions is expected to surge, contributing significantly to the market's upward trajectory.

From a regional perspective, North America currently leads the Education Data Lake market due to its advanced digital infrastructure, high adoption of EdTech solutions, and strong focus on data-driven education policies. However, Asia Pacific is emerging as the fastest-growing market, driven by government initiatives to modernize education systems, rising investments in digital learning, and the growing student population. Europe also demonstrates significant potential, with educational institutions prioritizing digital transformation and analytics to enhance learning outcomes and institutional performance. These regional dynamics underscore the global nature of the market’s expansion and the diverse opportunities across geographies.

Component Analysis

The Component segment of the Education Data Lake market is bifurcated into Solutions and Services, both of which play pivotal roles in the ecosystem. Solutions encompass the core data lake platforms, analytics engines, and integration tools that form the backbone of data processing and management in educational institutions. These solutions enable the ingestion, storage, and analysis of vast datasets from multiple sources, offering institutions the ability to unify disparate data streams and derive actionable insights. The demand for robust, scalable, and interoperable solutions is rising as educational organizations seek to overcome data silos and harness the full potential of their digital assets.

Services, on the other hand, are integral to ensuring the successful deployment, customization, and ongoing management of education data lakes. This sub-segment includes consulting, implementation

Childcare System Market size was valued at USD 1.4 Billion in 2024 and is projected to reach USD 6.8 Billion by 2032, growing at a CAGR of 30% from 2026-2032.

Major factors that are driving the market growth include a decrease in child mortality rate, increasing labor force participation of women, and positive government initiatives. Governments have a significant role in driving revenues for the global Childcare System Market. The implementation of stringent regulations by governments, coupled with increased funding to daycare centers, has helped parents, especially from low-income families. One of the major factors driving the growth of the Childcare System Market is the rise in the working women population. Changes in lifestyle as well as standard of living especially in developing economies have seen a surge in the women working for the population.

Global Childcare System Market Definition

Childcare system software is a tool that helps in managing the daily operations of daycare centers or preschools to save childcare time and make work and life easier. It is specifically designed for child care centers and other similar child-oriented facilities. The software supports, guides, and automates administrative tasks like parents' contact information database, scheduled appointments, attendance records, and children's health data management. The Childcare system saves time for childcare centers or pre-schools by automating administrative tasks such as invoicing, reporting, and admissions.

Most childcare software is interconnected with social media tools so that childcare centers can communicate with parents on social media through the software. For instance, Procare is a popular childcare software, which stores information regarding the child and their family. Childcare software can be either operated from local computers or via mobile if it has been given access to other systems running somewhere else. The system is mainly used to increase staff productivity by storing information regarding the child and family.

Early child care is an equally important and often overlooked component of child development. Child care providers can be children's first teachers and therefore play an integral role in systems of early childhood education. Quality care from a young age can have a substantial impact on the future successes of children. The main focus of the childcare system is on the development of the child, whether that be mental, social, or psychological.

Growing Demand for Childcare Services: Increasing workforce participation and changing family structures drive the need for childcare solutions.

Technological Advancements: Adoption of digital tools for managing enrollment, scheduling, and communication streamlines childcare operations.

Regulatory Compliance: Stringent regulations regarding safety, staffing ratios, and educational standards necessitate the adoption of comprehensive childcare systems.

Focus on Early Childhood Education: Rising awareness of the importance of early childhood education fuels demand for systems that support educational programming and developmental tracking.

Shift towards Flexible and Remote Work: The trend towards remote work increases the demand for flexible childcare options, driving the need for systems that enable remote monitoring and communication between parents and caregivers.

BackgroundAdvanced Practice Physiotherapy (APP) is a higher level of practice grounded in 4 pillars: clinical practice, leadership, education and research. A critical step toward successful integration and sustainability of APP in healthcare systems is understanding the educational pathway to APP.Objectives1) To describe the post-licensure educational pathways that physiotherapists engage in to advance their level of practice.2) To evaluate demonstration of the pillars of APP by the physiotherapist after traversing a post-licensure educational pathway.MethodsThis systematic mixed studies review is reported in accordance with PRISMA and pre-registered (PROSPERO: CRD42024499563). 8 databases plus the grey literature were searched. 2 independent reviewers determined eligibility, extracted data, assessed quality (QuADS) and determined the overall confidence in the cumulative evidence (GRADE-CERQual).Results81 studies (18 qualitative, 17 mixed methods, 46 quantitative) were included in a data based convergent qualitative synthesis. 6 distinct post-licensure educational pathways were described and evaluated: Masters level education, residency and fellowship programs, accredited area of practice education, mentorship, multiple encounter courses and single encounter courses.ConclusionThere is a high level of confidence (GRADE-CERQual) in the finding that Masters level education consistently resulted in all 4 pillars demonstrated by the physiotherapist. Masters level education appears to be the optimal pathway to APP.

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED

Natural selection is a key mechanism of evolution, which results from the differential reproduction of phenotypes. We describe fecundity selection at different parity transitions on 15 anthropometric traits and educational attainment in Estonian children, who were born between 1938 and 1962 and measured at around 13 years of age (Juhan Aul’s database). The sample sizes reach up to 7000 in boys and 10 000 in girls. The direction of selection on educational attainment and bodily traits was sexually antagonistic, and it occurred via different parity transitions in boys and girls. Compared to boys with primary education, obtaining tertiary education was associated with 3.5 times and secondary education two times higher odds of becoming a father. Transition to motherhood was not related to educational attainment, while education above primary was associated with lower odds (OR = 0.5 – 0.7) to progression to parities above one and two. Selection on anthropometric traits occurred almost exclusively via childlessness in boys, while among the girls, most of the traits that were associated with becoming a mother were additionally associated with a transition from one child to higher parities. Male (but not female) fitness was thus primarily determined by traits related to mating success. Selection favoured stronger and larger boys and smaller girls. Selection on girls favoured some traits that associate with perceived femininity while other feminine traits were selected against.

This record includes the data and R code to reproduce the statistical analyses the results are based on.

Background: Self-regulated learning refers to the monitoring and controlling of one's own cognitive performance before, during, and after a learning episode. Previous literature suggested that self-regulated learning had a significant relationship with academic achievement, but not all self-regulated learning strategies exerted the same influences. Using an invalid strategy may waste the limited psychological resources, which will cause the ego depletion effect. The present meta-analysis study intended to search for the best self-regulated learning strategies and inefficient strategies for Chinese students in elementary and secondary school, and analyzed the critical phases of self-regulated learning according to Zimmerman's theory. The moderating effects of gender, grade, and publication year were also analyzed.Methods: Empirical studies which conducted in real teaching situations of elementary and secondary education were systematically searched using Chinese academic databases. Studies focused on undergraduate students, students of special education, or online learning environments were excluded. Fifty-five cross-sectional studies and four intervention studies (which generated 264 independent samples) were included with a total sample size of 23,497 participants. Random effects model was chosen in the current meta-analysis, and publication bias was also examined.Results: The results indicated that the overall effect size of self-regulated learning on academic achievement was small for primary and secondary school students in China. The effect sizes of self-efficacy, task strategies, and self-evaluation were relatively higher than other strategies. Self-regulated learning strategies have the largest effect size on science disciplines (including mathematics and physics). Performance phase and self-reflection phase are key phases of self-regulated learning. From 1998 to 2016, the effect size between self-regulated learning and academic achievement was gradually decreasing.Conclusions: The main findings of the current study showed that self-efficacy, task strategies, and self-evaluation were key self-regulated learning strategies for Chinese students. Performance phase and self-reflection phase played significant roles in the process of self-regulated learning. Future studies need to include more intervention studies with rigorous treatment fidelity control and provide more empirical evidence from online learning, so as to compare the different effects of self-regulated learning between traditional education and online education.

Tables definition

Key measures

• Contains key measures (e.g. cases, deahts, recovered); listed by country and time
• Source:
  • Johns Hopkins University
  • World Health Organisation (WHO)
  • European Centre for Disease Prevention and Control (ECDC)

• Attribute:

  coulmn name	datatype	note
  country_id	int	foreign key
  time_id	int	foreign key
  cases	string
  deaths	string
  recovered	string
  cumulate_cases	string
  cumulate_deaths	string
  cumulate_recovered	string
  source_id	string

Measurements

• Contains measures by country and at which time
• Source:
  • 2019-novel-coronavirus-covid-19-2019-ncov-data-repository-recovered.csv'

• Attribute:

  coulmn name	datatype	note
  country_id	int	foreign key
  time_id	int	foreign key
  value	string	measure value
  note	string
  category	string
  source_id	string

Countries health index

• Contains the health index of a country (float)

• Attribute:

  coulmn name	datatype	note
  country_id	int	foreign key
  time_id	int	foreign key
  value	string

Countries testing policy

• Bitfield: is country testing Cov-19

• Attribute:

  coulmn name	datatype	note
  country_id	int	foreign key
  time_id	int	foreign key
  value	string

Time dimension

• Timespan from 01-01-2018 to 12-31-2022 & the years (1990,1991,....,2017) -> format: 01-01-
• Source: own generated

• Attribute:

  coulmn name	datatype	note
  id	int	foreign key
  date	int	foreign key
  day	string
  month	int	foreign key
  year	int	foreign key
  cw	int	1= monday,2=tuesday,..,7=sunday
  day_of_week	int	foreign key

Country dimension

• From WHO datasets - selected distinct country names
• Converted by cocoConverter (input: country_code)
  • if nan -> country will be ignored

• Attribute:

  coulmn name	datatype	note
  name	int	country name
  country_code	int	ISO3166(3letters)
  continent	string
  population	int	total from 2018
  convert_name	int	convert by Country-Converter

Source dimension

• From measure data

• id 1-3: 'WHO','ECDC' and 'JHU'

  coulmn name	datatype	note
  id	int	country name
  name	string	ISO3166(3letters)

Implementation

Database

ConnectDb - access to database via python - inbound lib: sqlite03 - conn = sql.connect('database.db') - Return connection

Interface

Returns pandas dataframe (see pandas link).

read_dataset

df = read_dataset(connection,min_date,max_date,country,region)

• In:
  • Connection
  • Min_date; if == 0 then return earliest date
  • Max_date; if ==0 then return lastest date
  • County; filtering country (string); if ==”all” return all countries
  • Region; filtering continent(string);if==”all” return..
• Out
  • country_code
  • country_name
  • continent
  • time_id
  • date (mm-dd-yyyy)
  • caleder week (in string; monday...)
  • cases
  • deaths
  • recovered
  • cumulate_cases
  • cumulate_deaths
  • cumulate_recovered
  • source

links

• Frontend for DB
• Country Converter
• sqlite3 for python

The global Knowledge Graph Visualization Tool market is poised for substantial growth, projected to reach approximately $2,500 million by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of around 18-22% through 2033. This expansion is primarily fueled by the escalating demand for sophisticated data analysis and interpretation across diverse industries. Key drivers include the burgeoning volume of complex, interconnected data and the increasing recognition of knowledge graphs as powerful tools for uncovering hidden patterns, relationships, and actionable insights. The ability of these tools to transform raw data into intuitive, visual representations is critical for stakeholders to make informed decisions, enhance operational efficiency, and gain a competitive edge. Sectors like finance, where fraud detection and risk assessment are paramount, and healthcare, for drug discovery and personalized medicine, are leading this adoption. Educational institutions are also leveraging these tools for more engaging and effective learning experiences, further broadening the market's reach. The market's trajectory is further shaped by the continuous innovation in visualization techniques and the integration of advanced AI and machine learning capabilities. The emergence of both structured and unstructured knowledge graph types caters to a wider array of data complexities, allowing businesses to harness insights from both highly organized databases and free-form text or multimedia content. While the potential is immense, market restraints include the initial complexity and cost associated with implementing and maintaining knowledge graph solutions, as well as the need for specialized skill sets to manage and interpret the data effectively. However, as the technology matures and becomes more accessible, these challenges are expected to diminish, paving the way for widespread adoption. Geographically, North America and Europe are currently dominant markets due to their advanced technological infrastructure and early adoption rates, but the Asia Pacific region is rapidly emerging as a significant growth area driven by its large digital economy and increasing investments in data analytics. This comprehensive report delves into the dynamic landscape of Knowledge Graph Visualization Tools, providing an in-depth analysis of market dynamics, key players, and future projections. The study period spans from 2019 to 2033, with a base year of 2025, offering a thorough examination of historical trends (2019-2024) and forecasting future growth during the forecast period of 2025-2033. The estimated year for market assessment is also 2025. The report aims to equip stakeholders with actionable insights, forecasting a market value that is projected to reach into the millions of USD.

The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.