Crime is a major problem in Latin America and the Caribbean. With 9 percent of the world's population, the region accounts for 33 percent of global homicides. This dataset makes extensive new survey data available to help identify what anti-crime policies citizens in the region demand from their governments, as well as who is demanding what and why. This dataset accompanies a recent report on Combating Crime in Latin America and the Caribbean.

Crime is a major problem in Latin America and the Caribbean. With 9 percent of the world's population, the region accounts for 33 percent of global homicides. This dataset makes extensive new survey data available to help identify what anti-crime policies citizens in the region demand from their governments, as well as who is demanding what and why. This dataset accompanies a recent report on Combating Crime in Latin America and the Caribbean, https://publications.iadb.org/en/combating-crime-latin-america-and-caribbean-what-public-policies-do-citizens-want. Data from Americas Barometer study were collected in 2016–17 and refer to the subsample of 17 countries in Latin America. The IADB–LAPOP–Capital Cities Project includes data collected from 2017, between August and September. A total of 6,040 interviews in seven countries were conducted as part of the project (Chile, Colombia, Honduras, Mexico, Panama, Peru, and Uruguay). Methodology and Code variables definitions are available here: https://publications.iadb.org/en/what-public-policies-do-citizens-want-combating-crime-latin-america-and-caribbean-dataset

PGvsPI-46 works on financial markets in the Inter-American Development Bank, and from that perspective provides insights into financial liberalization processes in Latin American countries and the impact of global developments on Latin American financial markets. The Financial Elite Policymakers Interviewed (FINEPINT) database consists of interviews with financial policymakers from advanced economies and emerging markets. The interviews touch upon both national-level regulatory developments and global-level policymaking processes, as well as the interactions between the two. Interviewees are officials from Ministries, Central Banks and Financial Supervisors, representatives of banking and financial associations, representatives of Civil Society Organizations, and officials from International Organizations working on global financial governance.The semi-structured interviews followed a standard format in which policymakers were asked about:1. The development of their negotiating positions, asking who is involved internally, how the positions are informed by external actors, and who are the main partners in the policymaking process.2. The developments in policymaking processes, asking what were the main drivers of new issues emerging on the agenda, what issues came up in policymaking processes, and who is influential in the policymaking processes.3. The outcomes, asking how the main issues in policymaking processes were resolved and what the expected impacts of new policies will be.Interviews were conducted from 1992 onwards, covering a period of profound changes in the global financial system and its governance. Broad topics that were the focal points of different waves of interviews were the interaction between public and private actors in global policymaking, the political economy of financial liberalization, the internationalization / globalization of financial markets and the regulatory response to this, the Basel Capital Accords, the resolution of sovereign debt crisis, and sustainable finance.Interviewees have been provided with the following options for the use of the transcript:• Quotation: direct quotes from the interview may be used and attributed in the reference.• Referencing, no quotation: interview may be refenced by name as support for a claim, but no direct quotes may be used.• No quotation or referencing: the interview may not be quoted or referenced by name.The principal investigators of the projects included in this database are prof. G.R.D. Underhill and dr. J. Blom. Data collection and development of this dataset has been made possible by:• NWO MaGW Open Competition grant ‘Public-private interaction and shifting patterns of governance’ (grant no. 400-04-233, prof. G.R.D. Underhill)• UKRI ESRC World Economy and Finance program grant ‘National and International Aspects of Financial Development’ (grant no. RES-156-25-0009).• EU Horizon 2020 Research and Innovation program, Marie Sklodowska-Curie Individual Fellowship grant ‘G20LAP: G20 Legitimacy and Policymaking’ (grant agreement no. 845121, dr. J. Blom).

The data consist of transcripts of interviews with 19 individuals from Brazil and 5 individuals from Colombia, who are all involved in Black and Indigenous activist organisations or in state agencies that are charged with promoting anti-racism and/or human rights. Each transcript begins with a paragraph giving contextual informationLatin America has often been held up as a region where racism is less of a problem than in regions such as the United States or Europe. Because most people are 'mestizos' (mixed race) and mixture is often seen as the essence of national identity, clear racial boundaries are blurred, resulting in comparatively low levels of racial segregation and a traditionally low public profile for issues of race. In Europe and the United States, the racial mixture and interaction across racial boundaries, which are typical of Latin America and are becoming more visible elsewhere, are heralded by some observers as leading towards a 'post-racial' reality, where anti-racism and multiculturalism - seen in this view as divisive policies that accentuate social differences - become unnecessary. Critics point out that mixture is not an antidote to racial inequality and racism in Latin America: they all coexist. This severely qualifies claims that mixture can lead to a 'post-racial' era. This project will investigate anti-racist practices and ideologies in Bolivia, Brazil, Colombia and Mexico. The project will contribute to conceptualising and addressing problems of racism, racial inequality and anti-racism in the region. We also propose that Latin America presents new opportunities for thinking about racism and anti-racism in a 'post-racial' world. Understanding how racism and anti-racism are conceived and practised in Latin America - in contexts in which mixture is pervasive - can help us to understand how to think about racism and anti-racism in other regions of the world, where notions of race have been changing in some respects towards Latin American patterns. It is also crucial to show the variety of ways in which mixture operates and co-exists with racism in Latin America - a region that is far from homogeneous. Research teams in each country, working with a range of organisations concerned with racism and discrimination, will explore how the organisations conceptualise and address key problems, which are becoming more salient in other regions, which confront similar scenarios. First, how to practice anti-racism when most people are mixed and when they may deny the importance of race and racism and themselves be both victims and the perpetrators of racism. Second, how to conceptualise and practice anti-racism when 'culture' seems to be the dominant discourse for talking about difference, but when physical difference (skin colour, hair type, etc.) remain powerful but often unacknowledged signs that move people to discriminate. Third, how to understand racism and combat it when race and class coincide to a great extent and make it easy to deny that race and racism are important factors. Fourth, how to make sure anti-racism addresses gender difference effectively, in a context in which mixture between white men and non-white women has been seen as the founding act of the nation. Fifth, how to pursue anti-racism when it is often claimed that there is little overt racist violence and that this is evidence of racial tolerance. We will explore how these elements structure - and may constrain - ideas about (anti-)racism within institutions, organisations and everyday practice. Our project will work with organisations in Bolivia, Brazil, Colombia and Mexico - countries that capture a good range of the region's diversity - to explore how racism and anti-racism are conceptualised and addressed in state and non-state circles, in legislation and the media, and in a variety of campaigns and projects. We aim to strengthen anti-racist practice in Latin America by feeding back our findings and by helping build networks; and to provide useful insights for understanding racism and anti-racism within and outside the region. The project carried out research in four countries, Brazil, Colombia, Ecuador and Mexico. We started by scoping out a broad range of organizations and individuals who were working in a direct or indirect fashion to challenge racism and racial inequality. We then selected seventeen case studies (over a third of which were Indigenous), with which we worked in depth, while also touching on about twenty other cases in a less intensive way. The cases were selected in order to include both Black and Indigenous organisations and cases, and to include a range of cases from government bodies to grassroots activist movements, plus some legal processes in which a variety of actors and organizations were involved. Our methods were mainly ethnography and interviews, undertaken principally by the four postdoctoral researchers, each of whom worked in one country. Some interviews were done with the assistance of a research assistant hired in the country. The interviews were conducted mostly in 2017, with some in 2018, in localities appropriate to the case study, such as an organization’s offices, an individual’s residence, or an agreed neutral location (e.g. a café, a village square, a classroom). Some interviews were informal conservations, but most were at least semi-structured. Common interview guides were not used, as each interview was specific to the case in question. Many interviews were audio-recorded (some were video-recorded) and selected interviews were transcribed in full or in part. Files with the original audio recordings and the transcripts are stored on a secure server in the University of Manchester. The files uploaded here are a selection of the transcribed interviews.

This dataset was created to support the 2016 DIA (Related publication only available in Spanish). The accelerated aging process that countries in Latin America and the Caribbean are undergoing imposes unprecedented pressures on the long-term care sector. In this context, the growing demand for care from the elderly population occurs alongside a reduction in the availability of informal care. Governments in the region must prepare to address these pressures by supporting the provision of care services to alleviate social exclusion in old age. The Inter-American Development Bank has created an Observatory on Aging and Care — the focus of this policy brief — aimed at providing decision-makers with information to design policies based on available empirical evidence. In this initial phase, the Observatory seeks to document the demographic situation of countries in the region, the health of their elderly population, their limitations and dependency status, as well as their main socioeconomic characteristics. The goal is to estimate the care needs countries in the region will face. This brief summarizes the key findings from an initial analysis of the data. The results highlight the scale of the problem. The figures speak for themselves: in the region, 11% of the population aged 60 and older is dependent. Both the magnitude and intensity of dependency increase with age. Women are the most affected across all age groups. This policy brief is part of a series of studies on dependency care, including works by Caruso, Galiani, and Ibarrarán (2017); Medellín et al. (2018); López-Ortega (2018); and Aranco and Sorio (2018).

This statistic shows a ranking of the estimated per capita consumer spending on healthcare in 2020 in Latin America and the Caribbean, differentiated by country. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs) in the selected region. Spending by corporations or the state is not included. Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 06. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.The shown forecast is adjusted for the expected impact of the COVID-19 pandemic on the local economy. The impact has been estimated by considering both direct (e.g. because of restrictions on personal movement) and indirect (e.g. because of weakened purchasing power) effects. The impact assessment is subject to periodic review as more data becomes available.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Abstract Intergovernmental conflicts, in Latin America’s centralism and the recentralization in Peru, have been more acute over the last 30 years, producing sociopolitical, economic, and cultural problems at subnational levels. Based on the design of the grounded theory of Glasser and Strauss, this sociopolitical phenomenon is analyzed from the perspective of local governments in the Puno region to account for the emerging categories and subcategories of analysis regarding the dynamics of intergovernmental relations (IGR) in the local public policy process. The main argument is that, as a result of centralism, recentralization, and intergovernmental conflicts, territorial inequality reproduces and reinforces at the local level. These are expressed in a dichotomy between the deficit territorial dynamics (backward areas) and entrepreneurial territorial dynamics (growth areas).

Originally published by Harte-Hanks, the CiTDS dataset is now produced by Aberdeen Group, a subsidiary of Spiceworks Ziff Davis (SWZD). It is also referred to as CiTDB (Computer Intelligence Technology Database). CiTDS provides data on digital investments of businesses across the globe. It includes two types of technology datasets: (i) hardware expenditures and (ii) product installs. Hardware expenditure data is constructed through a combination of surveys and modeling. A survey is administered to a number of companies and the data from surveys is used to develop a prediction model of expenditures as a function of firm characteristics. CiTDS uses this model to predict the expenditures of non-surveyed firms and reports them in the dataset. In contrast, CiTDS does not do any imputation for product install data, which comes entirely from web scraping and surveys. A confidence score between 1-3 is assigned to indicate how much the source of information can be trusted. A 3 corresponds to 90-100 percent install likelihood, 2 corresponds to 75-90 percent install likelihood and 1 corresponds to 65-75 percent install likelihood. CiTDS reports technology adoption at the site level with a unique DUNS identifier. One of these sites is identified as an “enterprise,” corresponding to the firm that owns the sites. Therefore, it is possible to analyze technology adoption both at the site (establishment) and enterprise (firm) levels. CiTDS sources the site population from Dun and Bradstreet every year and drops sites that are not relevant to their clients. Due to this sample selection, there is quite a bit of variation in the number of sites from year to year, where on average, 10-15 percent of sites enter and exit every year in the US data. This number is higher in the EU data. We observe similar turnover year-to-year in the products included in the dataset. Some products have become absolute, and some new products are added every year. There are two versions of the data: (i) version 3, which covers 2016-2020, and (ii) version 4, which covers 2020-2021. The quality of version 4 is significantly better regarding the information included about the technology products. In version 3, product categories have missing values, and they are abbreviated in a way that are sometimes difficult to interpret. Version 4 does not have any major issues. Since both versions of the data are available in 2020, CiTDS provides a crosswalk between the versions. This makes it possible to use information about products in Version 4 for the products in Version 3, with the caveats that there will be no crosswalk for the products that exist in 2016-2019 but not in 2020. Finally, special attention should be paid to data from 2016, where the coverage is significantly different from 2017. From 2017 onwards, coverage is more consistent. Years of Coverage: APac: 2019 - 2021 Canada: 2015 - 2021 EMEA: 2019 - 2021 Europe: 2015 - 2018 Latin America: 2015, 2019- 2021 United States: 2015 - 2021

The aim of the Empathy Business research project (2023-2024) led by the University of Lapland focused on how to digitalize services and business prototyping through creativity. The research named challenges as well as design methods for developing digital tools for meeting the future needs of service design and business development.

In total, 20 interviews among senior service designers and managers located in Europe, Latin America and Asia were conducted during spring 2023. The interviews provide perspectives related to the future of service design as a practise, the skills required and further issues of relevance for professionals in the field. Based on affinity diagramming eight main clusters were named: Sustainability, Business compatibility, New tools, Designer’s skills, Art-based methods, People in the centre, Online workshops, and Physical workshops.

This data set includes an anonymized list of the interviewees, affinity diagram post-it notes of the interviews, short descriptions of the main clusters and an internet link to the online affinity diagram on Miro board.

The materials provided initial insights for developing Proof-of-Concepts for digitized interfaces, such as suitable plugins, 3D-based photorealistic solutions, or an application with the potential to be used in service design and service prototyping contexts as well as in other development processes within and across organizations.

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

The Latin American Public Opinion Project (LAPOP) is an academic institution hosted by Vanderbilt University that began with the study of democratic values in Costa Rica, but today carries out surveys in much of Latin America, Canada, the United States, and the Caribbean. In 2004, LAPOP established the AmericasBarometer as multi-country, regularly conducted survey of democratic values and behaviors in the Americas, and the first round included voting-age respondents from 11 countries. LAPOP collected opinions on major problems facing the country and community, threats to security, attitudes towards the country's political system, and trust in government institutions. Also measured were respondents' participation in community organizations, attitudes towards government critics, voting behavior, confidence in local government, and the prevalence of crime. Demographic information collected includes nationality, ethnicity, native language, age, gender, education level, occupation, religion, and whether the respondent lives in an urban or rural area.

AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.12 billion globally in 2024, with a robust CAGR of 34.7% expected during the forecast period. By 2033, the market is forecasted to reach an impressive USD 15.32 billion. This remarkable growth is primarily attributed to the increasing demand for privacy-preserving data solutions, the surge in AI-driven analytics, and the critical need for high-quality, diverse datasets across industries. The proliferation of regulations around data privacy and the rapid digital transformation of sectors such as healthcare, finance, and retail are further fueling market expansion as organizations seek innovative ways to leverage data without compromising compliance or security.

One of the key growth factors for the AI-Generated Synthetic Tabular Dataset market is the escalating importance of data privacy and compliance with global regulations such as GDPR, HIPAA, and CCPA. As organizations collect and process vast amounts of sensitive information, the risk of data breaches and misuse grows. Synthetic tabular datasets, generated using advanced AI algorithms, offer a viable solution by mimicking real-world data patterns without exposing actual personal or confidential information. This not only ensures regulatory compliance but also enables organizations to continue their data-driven innovation, analytics, and AI model training without legal or ethical hindrances. The ability to generate high-fidelity, statistically accurate synthetic data is transforming data governance strategies across industries.

Another significant driver is the exponential growth of AI and machine learning applications that demand large, diverse, and high-quality datasets. In many cases, access to real data is limited due to privacy, security, or proprietary concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable data that closely mirrors real-world scenarios. This accelerates the development and deployment of AI models in sectors like healthcare, where patient data is highly sensitive, or in finance, where transaction records are strictly regulated. The synthetic data market is also benefiting from advancements in generative AI techniques, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), which have significantly improved the realism and utility of synthetic tabular data.

A third major growth factor is the increasing adoption of cloud computing and the integration of synthetic data generation tools into enterprise data pipelines. Cloud-based synthetic data platforms offer scalability, flexibility, and ease of integration with existing data management and analytics systems. Enterprises are leveraging these platforms to enhance data availability for testing, training, and validation of AI models, particularly in environments where access to production data is restricted. The shift towards cloud-native architectures is also enabling real-time synthetic data generation and consumption, further driving the adoption of AI-generated synthetic tabular datasets across various business functions.

From a regional perspective, North America currently dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024. This leadership is driven by the presence of major technology companies, strong investments in AI research, and stringent data privacy regulations. Europe follows closely, with significant growth fueled by the enforcement of GDPR and increasing awareness of data privacy solutions. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding AI ecosystems, and government initiatives promoting data innovation. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as organizations in these regions recognize the value of synthetic data in overcoming data access and privacy challenges.

Component Analysis

The AI-Generated Synthetic Tabular Dataset market by component is segmented into software and services, with each playing a pivotal role in shaping the industry landscape. Software solutions comprise platforms and tools that automate the generation of synthetic tabular data using advanced AI algorithms. These platforms are increasingly being adopted by enterprises seeking

Internetnutzung. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. über Telefon, ISDN, ADSL, via Kabel oder WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, TV set-top box, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen am Computer mit Internetzugang (Anti-Virus Software, smart card reader, Verschlüsselungssoftware, Firewall, elektronische Signatur); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet und Art dabei aufgetretener Probleme (verspätete Lieferung, fehlgeschlagene Lieferungen, keine Rückgabemöglichkeit, Probleme bei der Bezahlung, unzureichende Preisinformationen, schlechter Kundenservice, Missbrauch der persönlichen Daten z.B. in Form von Spam, Probleme mit der Navigation auf den Webseiten der Anbieter, unlautere/irreführende Angebote); Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, Online-Banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen. Demographie: Geschlecht; Alter; Studienstatus; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Land; Fragebogennummer; Gewichtungsfaktor. Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the television cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets. Demography: sex; age; current type of education; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: country; questionnaire number; weighting factor. Telephone interview Bevölkerung im Alter von 15 Jahren und älter

The Latin American Public Opinion Project (LAPOP) is an academic institution hosted by Vanderbilt University that began with the study of democratic values in Costa Rica, but today carries out surveys in much of Latin America, Canada, the United States, and the Caribbean. In 2004, LAPOP established the AmericasBarometer as multi-country, regularly conducted survey of democratic values and behaviors in the Americas, and the first round included voting-age respondents from 11 countries. LAPOP collected opinions on major problems facing the country and community, threats to security, attitudes towards the country's political system, and trust in government institutions. Also measured were respondents' participation in community organizations, attitudes towards government critics, voting behavior, confidence in local government, and the prevalence of crime. Demographic information collected includes nationality, ethnicity, native language, age, gender, education level, occupation, religion, and whether the respondent lives in an urban or rural area.

AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.42 billion in 2024 globally, reflecting the rapid adoption of artificial intelligence-driven data generation solutions across numerous industries. The market is expected to expand at a robust CAGR of 34.7% from 2025 to 2033, reaching a forecasted value of USD 19.17 billion by 2033. This exceptional growth is primarily driven by the increasing need for high-quality, privacy-preserving datasets for analytics, model training, and regulatory compliance, particularly in sectors with stringent data privacy requirements.

One of the principal growth factors propelling the AI-Generated Synthetic Tabular Dataset market is the escalating demand for data-driven innovation amidst tightening data privacy regulations. Organizations across healthcare, finance, and government sectors are facing mounting challenges in accessing and sharing real-world data due to GDPR, HIPAA, and other global privacy laws. Synthetic data, generated by advanced AI algorithms, offers a solution by mimicking the statistical properties of real datasets without exposing sensitive information. This enables organizations to accelerate AI and machine learning development, conduct robust analytics, and facilitate collaborative research without risking data breaches or non-compliance. The growing sophistication of generative models, such as GANs and VAEs, has further increased confidence in the utility and realism of synthetic tabular data, fueling adoption across both large enterprises and research institutions.

Another significant driver is the surge in digital transformation initiatives and the proliferation of AI and machine learning applications across industries. As businesses strive to leverage predictive analytics, automation, and intelligent decision-making, the need for large, diverse, and high-quality datasets has become paramount. However, real-world data is often siloed, incomplete, or inaccessible due to privacy concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable, and bias-mitigated data for model training and validation. This not only accelerates AI deployment but also enhances model robustness and generalizability. The flexibility of synthetic data generation platforms, which can simulate rare events and edge cases, is particularly valuable in sectors like finance and healthcare, where such scenarios are underrepresented in real datasets but critical for risk assessment and decision support.

The rapid evolution of the AI-Generated Synthetic Tabular Dataset market is also underpinned by technological advancements and growing investments in AI infrastructure. The availability of cloud-based synthetic data generation platforms, coupled with advancements in natural language processing and tabular data modeling, has democratized access to synthetic datasets for organizations of all sizes. Strategic partnerships between technology providers, research institutions, and regulatory bodies are fostering innovation and establishing best practices for synthetic data quality, utility, and governance. Furthermore, the integration of synthetic data solutions with existing data management and analytics ecosystems is streamlining workflows and reducing barriers to adoption, thereby accelerating market growth.

Regionally, North America dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024 due to the presence of leading AI technology firms, strong regulatory frameworks, and early adoption across industries. Europe follows closely, driven by stringent data protection laws and a vibrant research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, government initiatives, and increasing investments in AI research and development. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in sectors like finance and government, though market maturity varies across countries. The regional landscape is expected to evolve dynamically as regulatory harmonization, cross-border data collaboration, and technological advancements continue to shape market trajectories globally.

Abstract copyright UK Data Service and data collection copyright owner. As the statistical branch of the United Nations Educational, Scientific and Cultural Organisation (UNESCO), the Institute for Statistics (UIS) produces cross-nationally comparable statistics on education, science and technology, culture, and communication for more than 200 countries and territories. The UNESCO Education Statistics dataset includes data from 1970 onwards. From pre-primary school enrolment to tertiary graduation rates, it covers all education levels and addresses key policy issues such as gender parity, teachers and education financing. Main Topics: • Out-of-school children • Entry • Participation • Progression • Completion • Literacy • Educational attainment • International student mobility in tertiary education • Human resources • Financial resources • School resources and teaching conditions (Africa only) • Adult education (Latin America and the Carribbean only) • Disparities in teacher's training, deployment, characteristics and working conditions at sub-national level (East and South West Asia only).

GAN-Synthesized Augmented Radiology Dataset Market Outlook

According to our latest research, the GAN-Synthesized Augmented Radiology Dataset market size reached USD 412 million in 2024, supported by a robust surge in the adoption of artificial intelligence across healthcare imaging. The market demonstrated a strong CAGR of 25.7% from 2021 to 2024 and is on track to reach a valuation of USD 3.2 billion by 2033. The primary growth factor fueling this expansion is the increasing demand for high-quality, diverse, and annotated radiology datasets to train and validate advanced AI diagnostic models, especially as regulatory requirements for clinical validation intensify globally.

The exponential growth of the GAN-Synthesized Augmented Radiology Dataset market is being driven by the urgent need for large-scale, diverse, and unbiased datasets in medical imaging. Traditional methods of acquiring and annotating radiological images are time-consuming, expensive, and often limited by patient privacy concerns. Generative Adversarial Networks (GANs) have emerged as a transformative technology, enabling the synthesis of high-fidelity, realistic medical images that can augment existing datasets. This not only enhances the statistical power and generalizability of AI models but also helps overcome the challenge of data imbalance, especially for rare diseases and underrepresented demographic groups. As AI-driven diagnostics become integral to clinical workflows, the reliance on GAN-augmented datasets is expected to intensify, further propelling market growth.

Another significant growth driver is the increasing collaboration between radiology departments, AI technology vendors, and academic research institutes. These partnerships are focused on developing standardized protocols for dataset generation, annotation, and validation, leveraging GANs to create synthetic images that closely mimic real-world clinical scenarios. The resulting datasets facilitate the training of AI algorithms for a wide array of applications, including disease detection, anomaly identification, and image segmentation. Additionally, the proliferation of cloud-based platforms and open-source AI frameworks has democratized access to GAN-synthesized datasets, enabling even smaller healthcare organizations and startups to participate in the AI-driven transformation of radiology.

The regulatory landscape is also evolving to support the responsible use of synthetic data in healthcare. Regulatory agencies in North America, Europe, and Asia Pacific are increasingly recognizing the value of GAN-generated datasets for algorithm validation, provided they meet stringent standards for data quality, privacy, and clinical relevance. This regulatory endorsement is encouraging more hospitals, diagnostic centers, and research institutions to adopt GAN-augmented datasets, further accelerating market expansion. Moreover, the ongoing advancements in GAN architectures, such as StyleGAN and CycleGAN, are enhancing the realism and diversity of synthesized images, making them virtually indistinguishable from real patient scans and boosting their acceptance in both clinical and research settings.

From a regional perspective, North America is currently the largest market for GAN-Synthesized Augmented Radiology Datasets, driven by substantial investments in healthcare AI, the presence of leading technology vendors, and proactive regulatory support. Europe follows closely, with a strong emphasis on data privacy and cross-border research collaborations. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation in healthcare, rising investments in AI infrastructure, and increasing disease burden. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as healthcare systems in these regions begin to adopt AI-driven radiology solutions.

Dataset Type Analysis

The dataset type segment of the GAN-Synthesized Augmented Radiology Dataset market is pi

Database Performance Monitoring Software Tools Market Outlook

In 2023, the global database performance monitoring software tools market size was valued at approximately USD 1.8 billion. With a robust compound annual growth rate (CAGR) of 10.5%, it is projected to reach an impressive USD 4.5 billion by 2032. The growth of this market is primarily driven by the increasing demand for real-time data analytics and the need to maintain optimal database performance across various industries. The proliferation of data generated by businesses, along with the rising adoption of cloud computing technologies, acts as a catalyst for the expansion of this market.

One of the significant growth factors for the database performance monitoring software tools market is the increasing complexity of database environments. As organizations transition from traditional databases to more complex, distributed systems, the need for advanced monitoring tools has become essential. These tools provide critical insights into database performance, helping businesses optimize operations, reduce downtime, and ensure efficient data management. The rise of technologies like artificial intelligence and machine learning further enhances the capabilities of these software tools, allowing for predictive analytics and automated performance optimization, which are crucial in today's fast-paced business environment.

Another driving force behind the market's growth is the escalating demand for better user experience and service delivery. Enterprises are increasingly focusing on improving their database performance to ensure faster data retrieval and processing, which directly impacts customer satisfaction and retention. Additionally, regulatory compliance requirements across various sectors necessitate the use of sophisticated monitoring solutions to maintain data integrity and security. The integration of IoT devices and the explosion of big data analytics are also contributing to the demand for comprehensive database performance monitoring solutions.

Furthermore, the ongoing digital transformation initiatives across industries are fostering the growth of the database performance monitoring software tools market. Organizations are investing in digital technologies to enhance their operational efficiency and gain competitive advantages. As part of these initiatives, the need to monitor and manage database performance has become more pronounced. The shift towards cloud-based solutions and the increasing adoption of DevOps practices are also encouraging enterprises to deploy advanced monitoring tools that can seamlessly integrate with their existing IT infrastructure, thereby driving market growth.

In the realm of database management, Database Comparison Software plays a pivotal role in ensuring data consistency and integrity across various platforms. As organizations increasingly rely on complex database systems, the ability to compare and synchronize data becomes essential. This software facilitates the identification of discrepancies between databases, enabling IT teams to rectify issues swiftly and maintain seamless operations. By automating the comparison process, businesses can save time and resources, reducing the risk of human error and enhancing overall efficiency. As the demand for robust data management solutions grows, the integration of Database Comparison Software into existing IT infrastructures is becoming a strategic priority for many enterprises.

The regional outlook of the database performance monitoring software tools market underscores a strong growth trajectory in North America, which holds the largest market share due to the presence of key industry players and advanced technological infrastructure. The Asia Pacific region is anticipated to witness the highest growth rate, driven by rapid industrialization, increasing IT investments, and a surge in cloud computing adoption. Europe and Latin America are also expected to experience significant growth as enterprises in these regions continue to adopt digital solutions to optimize their database management processes.

Component Analysis

The database performance monitoring software tools market is segmented into two primary components: software and services. Within the software segment, there is an increasing demand for comprehensive solutions that offer real-time monitoring, advanced analytics, and automated alerts to proactively address performance issues. As databases become more complex, organizations a

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AI Training Dataset Market Size 2025-2029

The AI training dataset market size is forecast to increase by USD 7.33 billion at a CAGR of 29% between 2024 and 2029.

The market is witnessing significant growth, driven by the proliferation and increasing complexity of foundational AI models. As AI applications expand across industries, the demand for high-quality, diverse, and representative training datasets is escalating. This trend is leading companies to invest heavily in acquiring and curating datasets, shifting their focus from data quantity to data quality. However, this strategic shift presents challenges. Navigating data privacy, security, and copyright complexities is becoming increasingly important. Deep learning algorithms and serverless functions are emerging technologies that are gaining traction in the market.
Companies must invest in robust infrastructure and expertise to effectively manage, preprocess, and label their datasets for optimal AI model performance. By addressing these challenges and capitalizing on the opportunities presented by the growing demand for high-quality training datasets, companies can gain a competitive edge in the AI market. Ensuring compliance with regulations and protecting sensitive information is crucial to avoid potential legal and reputational risks. Simultaneously, generative AI is becoming increasingly pervasive as a co-developer and application component, further expanding the market's potential.

What will be the Size of the AI Training Dataset Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, classification accuracy and data labeling accuracy are paramount for businesses seeking to optimize their machine learning models. Data mining algorithms and computer vision algorithms are employed to extract valuable insights from raw data, while inference latency and model training time are critical factors for efficient model deployment. Model selection criteria, such as AUC score evaluation and precision and recall, are essential for assessing the performance of various machine learning libraries and deep learning frameworks. Regularization techniques, hyperparameter tuning, and loss function optimization are integral to enhancing model complexity analysis and regression performance.

Time series forecasting and cross validation strategy are essential for businesses seeking to make data-driven decisions based on historical trends. Neural network architecture and natural language processing are advanced techniques that can significantly improve model accuracy and monitoring tools are necessary for anomaly detection methods and model retraining schedules. Resource utilization and model deployment strategy are crucial considerations for businesses looking to optimize their AI investments. Gradient descent methods and backpropagation algorithm are fundamental techniques for optimizing model performance, while statistical modeling techniques and F1 score calculation offer additional insights for model evaluation.

How is this AI Training Dataset Industry segmented?

The AI training dataset 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.

Service Type

  Text
  Image or video
  Audio


Deployment

  On-premises
  Cloud


Type

  Unstructured data
  Structured data
  Semi-structured data


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Service Type Insights

The Text segment is estimated to witness significant growth during the forecast period. The cloud-based data storage market is experiencing significant growth due to the increasing demand for large volumes of diverse, high-quality data for artificial intelligence (AI) training, particularly in the field of natural language processing and large language models (LLMs). The importance of this market segment lies in the vast quantities of data required for pre-training, instruction fine-tuning, and safety alignment. Pre-training datasets, which can consist of petabytes of information sourced from the public web and supplemented with digitized books, academic papers, and code repositories, form the foundation. However, the true value and differentiation come from subsequent stages. Natural language processing, intelligent task routing, and computer vision integration are also key features that enhance the capabilities of these platforms.

Model deployment workflows and scalable data infrastructure are essential components of the