In this study, we conducted a time-motion study observing healthcare workers (HCWs) completing data management activities including monitoring and evaluation (M&E) and manual data linkage of individual-level app data to electronic medical records (EMRS). This study served as a baseline study for an open-source app to mirror EMRS and reduce HCW workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi. , , , # The workload of manual data entry for integration between mobile health applications and eHealth infrastructure

Corresponding author (Caryl Feldacker): cfeld@uw.edu

Cite this dataset

Data will be made available at Dryad upon acceptance at this link: https://doi.org/10.5061/dryad.k0p2ngfdz

Dataset Include

Data dictionary

• Was verbal consent of the health care provider given? (1 = Yes, 2 = No)
• What was the provider Cadre that was observed (1 = Community health service nurse, 2 = Data officer, 3 = IT officer/Manager, 4= Other)
• The time arrived at the facility
• Facility Name (1-9)
• Observer code
• Length of time an individual's cadre has spent at a facility working on NCAP activities (1 = < 1 year, 2 = 1 - 5 years, 3 = 6 - 10 years, 4 = more than 10 years).
• Highest level of academic qualification (1 = Diploma or above, 2 = Certificate, 3 = MSCE, 4 = JCE/PSL, 5 = Other).
• Main activit...

Two underlying datasets for a longitudinal cohort study evaluating the effectiveness of an mHealth application on quality of care and health outcomes for patients in primary health care facilities in Lebanon serving Syrian refugees and host communities. One file includes data obtained from patient exit interviews and the other from patient record reviews.

BackgroundMobile health interventions could have beneficial effects on health care delivery processes. We aimed to conduct a systematic review of controlled trials of mobile technology interventions to improve health care delivery processes. Methods and FindingsWe searched for all controlled trials of mobile technology based health interventions using MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA (Jan 1990–Sept 2010). Two authors independently extracted data on allocation concealment, allocation sequence, blinding, completeness of follow-up, and measures of effect. We calculated effect estimates and we used random effects meta-analysis to give pooled estimates. We identified 42 trials. None of the trials had low risk of bias. Seven trials of health care provider support reported 25 outcomes regarding appropriate disease management, of which 11 showed statistically significant benefits. One trial reported a statistically significant improvement in nurse/surgeon communication using mobile phones. Two trials reported statistically significant reductions in correct diagnoses using mobile technology photos compared to gold standard. The pooled effect on appointment attendance using text message (short message service or SMS) reminders versus no reminder was increased, with a relative risk (RR) of 1.06 (95% CI 1.05–1.07, I2 = 6%). The pooled effects on the number of cancelled appointments was not significantly increased RR 1.08 (95% CI 0.89–1.30). There was no difference in attendance using SMS reminders versus other reminders (RR 0.98, 95% CI 0.94–1.02, respectively). To address the limitation of the older search, we also reviewed more recent literature. ConclusionsThe results for health care provider support interventions on diagnosis and management outcomes are generally consistent with modest benefits. Trials using mobile technology-based photos reported reductions in correct diagnoses when compared to the gold standard. SMS appointment reminders have modest benefits and may be appropriate for implementation. High quality trials measuring clinical outcomes are needed. Please see later in the article for the Editors' Summary

This dataset provides information on several wearables devices, including the Fitbit Flex, Fitbit Charge HR, Basis Peak, GeneActiv Watch and others.

Data related to three studies detailed in the thesis "The adoption of mobile health foot monitoring technology in people with Diabetes Mellitus"Data for Study 2, Study 3 and Study 4 are contained in the separate workbook files attached.

Abstract

The Coronavirus Rapid Mobile Survey of Maternal and Child Health (CRAM-MATCH) was a rapid SMS (Short Message Service) survey conducted in South Africa conducted among pregnant women and mothers registered with the MomConnect mhealth platform in South Africa. This national survey was conducted in June (n=3140) with a follow up in July (n=2287). The survey collected data from pregnant women and new mothers in South Africa on how the Coronavirus pandemic has affected their health including their access to health care.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The survey collected data from pregnant women and new mothers in South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn from the Momconnect mhealth platform created by the South African National Department of Health (NDOH) in 2014. MomConnect is a mobile health (mHealth) solution created to improve and promote maternal health services in South Africa by providing pregnant mothers with free messaging facility and a helpdesk. The mobile health application also created a national pregnancy registry which has excellent coverage of pregnant women and new mothers. By 2017 more than half of the women attending public sector antenatal care services in South Africa were registered on the Momconnect platform. By 2019 there were over 2 million registered MomConnect users.

A self-weighted sample of 15 000 pregnant women and mothers with children under 12 months was drawn from the database of MomConnect users. The sample was stratified based on province, gestational age or age of their baby and their type of phone. The 15 000 women all received an invitation to join the SMS survey on the afternoon of 24 June 2020. They could respond by SMS with "JOIN" to participate in the survey, by SMSing "STOP" to not participate or to reply with "MORE" if they needed more information. Those who participated in the survey received R10 in airtime. The wave 1 survey was completed on June 30, 2020. The wave 2 survey invitation was sent on the 2nd of July 2020 and the survey ended on the 5th of July 2020.

Poverty Quintiles Two sets of poverty quintiles were created for respondents by constructing poverty quintiles for primary care public health facilities. The first poverty quintile measures the wealth quintile of the small area place where the facility that the respondent last visited is located. The second poverty quintile measures the average wealth quintile of the catchment area that the facility covers. Because of the focus on access to primary care and because the Momconnect moms' registrations are at their local primary care facility, only data related to public sector primary care facilities was extracted from the government database of facilities (clinics, community health centres and community day centres).

The richest 15% of areas was also excluded since these individuals are unlikely to make use of public facilities. This implies that the 'wealthiest' quintile only represents the wealthiest of the 85% poorest South Africans. Each small area place in Census was then linked to their closest public primary care facility, using the GIS codes in both the Census and the national facility database to create a catchment area for each facility.Poverty quintiles were created by deriving a measure of living standards and wealth measures via Principal Component Analysis (PCA), using data on employment status, education level, earnings, household size, and cell phone and car ownership of the residents of the area collected during the 2011 census. PCA was used to calculate wealth scores and these were aggregated over the entire catchment area, weighted by the population size of each Small Area place in the Census 2011. The sample of respondents was matched to these poverty quintiles via the Momconnect facility identifier, which captures the facility where the mother was registered.

Mode of data collection

Other [oth]

Research instrument

Two questionnaires were used, one for the Wave 1 Survey and another for the Wave 2 Survey.

Response rate

Assuming a response rate of 20%, from the targeted sample of 15 000 women, the project aimed to achieve a survey sample of 3000 and realised a sample of 3140 for wave 1 and thus had an effective response rate of 21%. Of the 3140 individuals who responded to wave 1, 2287 also responded in wave 2. The attrition rate between wave 1 and wave 2 was thus about 27%.

IntroductionAnxiety and depression are major causes of disability in Arab countries, yet resources for mental health services are insufficient. Mobile devices may improve mental health care delivery (mental m-Health), but the Arab region's mental m-Health app landscape remains under-documented. This study aims to systematically assess the features, quality, and digital safety of mental m-Health apps available in the Arab marketplace. We also contrast a set of recommended Australian apps to benchmark current strategies and evidence-based practices and suggest areas for improvement in Arabic apps.MethodsFifteen Arab country-specific iOS Apple Stores and an Android Google Play Store were searched. Apps that met the inclusion criteria were downloaded and evaluated using the Mobile App Rating Scale (MARS) and the Mobile App Development and Assessment Guide (MAG).ResultsTwenty-two apps met the inclusion criteria. The majority of apps showed no evidence of mental health experts being involved in the app design processes. Most apps offered real-time communication with specialists through video, text, or audio calls rather than evidence-based self-help techniques. Standardized quality assessment showed low scores for design features related to engagement, information, safety, security, privacy, usability, transparency, and technical support. In comparison to apps available in Australia, Arabic apps did not include evidence-based interventions like CBT, self-help tools and crisis-specific resources, including a suicide support hotline and emergency numbers.DiscussionIn conclusion, dedicated frameworks and strategies are required to facilitate the effective development, validation, and uptake of Arabic mental mHealth apps. Involving end users and healthcare professionals in the design process may help improve app quality, dependability, and efficacy.

Deidentified data, data dictionary, statistical code

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Comprehensive WHO Health Indicators for Thailand

Physical Activity, Mortality, and Access to Care

By Humanitarian Data Exchange [source]

About this dataset

This dataset contains an abundance of health indicators from the World Health Organization's data portal that relate to a variety of topics such as mortality, sustainable development goals, child health, infectious diseases, public health and environment, substance use and mental health, tobacco use, HIV/AIDS and other STIs nutrition, urban health and noncommunicable diseases. This dataset is designed to provide valuable insights into the determinants of various healthcare issues in Thailand by providing detailed metrics related to the state of the country's healthcare infrastructure. It includes indicators on topics ranging from financing & policy strategies to treatment capacity & coverage; human resources; information systems; youth; financial protection & AMR Glass; sexual & reproductive health; immunization services; neglect tropical diseases (NTDs); essential technologies & medical equipment as well as demographic & socioeconomic statistics. With access to an extensive range of data points covering multiple categories this collection allows users explore further trends or develop custom analysis tailored towards their particular research needs

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How to use the dataset

This dataset provides comprehensive health indicators related to mortality, global health estimates, sustainable development goals, and much more. With it, you can analyze the health status of Thailand over time and help inform decision makers on important healthcare policy matters.

To get started using this dataset: - Familiarize yourself with the columns in the dataset by reading through their descriptions in the ‘About this Dataset’ section above. - Review any caveats or specific methodology associated with individual indicators in their respective resource descriptions where available. - Select which columns or areas that you want to further study or compare (e.,g., compare different regions). 4. Filter data points based on your criteria (e.,g., filter by end year). 5. Use the information presented in your analysis to help inform decision makers on important healthcare policy matters for Thailand!

Research Ideas

• Creating an interactive visualization of the health indicators in Thailand to capture the trend over time or analyze correlations between different indicators.
• Developing a mobile app to help users access essential health indicator data for Thailand in real-time, such as rate of child deaths or number of tuberculosis cases per region.
• Creating predictive models based on these health indicators to track disease spread, forecast scarcity of certain medical resources and detect trends emerging among specific populations such as youths or males over females

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rsud-governance-policy-and-financing-prevention-indicators-for-thailand-19.csv | Column name | Description | |:----------------------------|:-----------------------------------------------------------------------| | GHO (CODE) | The Global Health Observatory code for the indicator. (String) | | GHO (DISPLAY) | The Global Health Observatory display name for the indicator. (String) | | GHO (URL) | The URL for the Global Health Observatory indicator. (URL) | | PUBLISHSTATE (CODE) | The code for the publish state of the indicator. (String) | | PUBLISHSTATE (DISPLAY) | The display name for the publish state of the indicator. (String) | | PUBLISHSTATE (URL) | The URL for the publish state of the indicator. (URL) | | YEAR (CODE) | The code for the year of the indicator. (String) | | YEAR (DISPLAY) | The display name for the year of the indicator. (String) | | YEAR (URL) | The URL for the year of the indicator. (URL) | | REGION (CODE) | The code for the region of the indicator. (String) | | REGION (DISPLAY) | The display name for the region of the indicator. (String) | | ...

Four electronic databases were searched on March 6, 2020 including Scopus, PubMed, ISI, and Embase Our search consisted of concepts of “self-care,” “elderly” and “Mobile device.” English journal papers and, RCTs conducted for individuals older than 60 in the last 10 years were included. A narrative approach was used to synthesize the data due to the heterogeneous nature of the data. Initially, 3047 studies were obtained and finally 19 studies were identified for deep analysis. 13 outcomes were identified in m-health interventions to help older adults’ self-care. Each outcome has at least one or more positive results. The psychological status and clinical outcome measures were all significantly improved. According to the findings, it is not possible to draw a definite positive decision about the effectiveness of interventions on older adults because the measures are very diverse and have been measured with different tools. However, it might be declared that m-health interventions have one or more positive results and can be used along with other interventions to improve the health of older adults.

This study examines the factors influencing users’ intention to continue using mobile medical apps within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Through a combination of questionnaire surveys and interviews, the research finds that doctor-patient trust, Performance Expectancy (PE), social influence, and facilitating conditions significantly impact users’ intention to utilize mobile medical apps. Furthermore, the study reveals the moderating effect of doctor-patient trust on social influence, indicating an increased trust level during the epidemic, attributed to positive media coverage, complimentary medical services, and risk-sharing initiatives. These results provide valuable insights for the field of internet healthcare, COVID-19 response strategies, health information management, and the advancement of digital health technologies, spotlighting the pivotal roles of trust, PE, and social influence in fostering sustained engagement with mobile health apps.

Mobile Robot Dataset Versioning Market Outlook

According to our latest research, the global mobile robot dataset versioning market size reached USD 412 million in 2024, and is expected to grow at a robust CAGR of 16.2% during the forecast period, reaching approximately USD 1.15 billion by 2033. This growth is primarily driven by the increasing adoption of mobile robots across diverse industries and the critical need for robust dataset management solutions to ensure accurate training, deployment, and continuous improvement of autonomous systems. The proliferation of AI-powered robots and rapid advancements in machine learning algorithms are further fueling the demand for sophisticated dataset versioning platforms, enabling organizations to manage, track, and audit data changes efficiently.

One of the most significant growth factors for the mobile robot dataset versioning market is the exponential increase in the deployment of autonomous robots in industries such as logistics, manufacturing, and healthcare. As these robots become more sophisticated, the datasets required for their training and operation also become larger and more complex. Accurate dataset versioning ensures that every iteration of training and operational data is meticulously tracked, which is essential for regulatory compliance, quality assurance, and continuous performance improvement. Companies are increasingly recognizing the role of dataset versioning in minimizing errors, reducing operational downtime, and accelerating the development lifecycle of autonomous systems. The ability to roll back to previous dataset versions or audit changes has become a vital requirement, especially in safety-critical applications.

Another key driver is the rise of collaborative robotics and multi-robot systems, which generate vast amounts of heterogeneous data from diverse sources such as sensors, cameras, and LIDAR. Managing these datasets in real time, especially when updates and modifications are frequent, necessitates advanced versioning solutions that can handle distributed environments. The growing emphasis on data quality, integrity, and traceability is pushing organizations to invest in specialized software and services that provide granular control over dataset modifications. Furthermore, the integration of cloud-based platforms with dataset versioning capabilities allows for seamless collaboration among geographically dispersed teams, thus enhancing productivity and innovation in robot development and deployment.

The market is also benefiting from increased research activities in academia and industry, focusing on improving the accuracy and efficiency of autonomous navigation, mapping, and object recognition. These research initiatives generate vast volumes of experimental data that must be versioned and managed efficiently to support reproducibility and peer collaboration. The growing adoption of open-source frameworks and standardized dataset management practices is further catalyzing market growth. At the same time, regulatory requirements for data transparency and auditability in sectors like healthcare and defense are compelling organizations to adopt advanced dataset versioning solutions, ensuring that all data used in robot training and operation is properly documented and traceable.

From a regional perspective, North America and Europe currently dominate the mobile robot dataset versioning market, driven by robust investments in robotics research, a strong presence of technology vendors, and early adoption of advanced data management solutions. However, the Asia Pacific region is emerging as the fastest-growing market, propelled by rapid industrialization, increased automation in manufacturing and logistics, and significant government initiatives supporting AI and robotics innovation. The Middle East & Africa and Latin America are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the benefits of dataset versioning in optimizing robot performance and ensuring data compliance. The global landscape is thus characterized by a dynamic interplay of technological advancement, regulatory evolution, and industry-specific adoption patterns.

Component Analysis

The component segment of the mobile robot dataset versioning market is divided into software, hardware, and services, each playing a distinct role in the ecosystem. Software solutions form the backb

The Veterans Health Administration Medical Facilities dataset includes Veteran Affairs hospitals, Veteran Affairs Residential Rehabilitation Treatment Programs (RRTP), Veteran Affairs Nursing Home Care Units (NHCU), Veteran Affairs Outpatient Clinics (VAOC), Vet Centers, and Veteran Affairs Medical Centers (VAMC). It should not include planned and suspended (non-operational) sites and mobile clinics. These definitions were set by the Veterans Health Administration (VHA) Policy Board in December 1998 and are the basis for defining the category and the additional service types for each VHA service site. These definitions cover sites generally owned by the Department of Veterans Affairs (VA) with the exception of leased and contracted community-based outpatient clinics (CBOCs).1. VA HOSPITAL: an institution (health care site) that is owned, staffed and operated by VA and whose primary function is to provide inpatient services. NOTE: Each geographically unique inpatient division of an integrated facility is counted as a separate hospital.2. VA RESIDENTIAL REHABILITATION TREATMENT PROGRAM (RRTP): provides comprehensive health and social services in a VA facility for eligible veterans who are ambulatory and do not require the level of care provided in nursing homes.3. VA NURSING HOME CARE UNITS (NHCU): provides care to individuals who are not in need of hospital care, but who require nursing care and related medical or psychosocial services in an institutional setting. VA NHCUs are facilities designed to care for patients who require a comprehensive care management system coordinated by an interdisciplinary team. Services provided include nursing, medical, rehabilitative, recreational, dietetic, psychosocial, pharmaceutical, radiological, laboratory, dental and spiritual.4. VA OUTPATIENT CLINICS:a. Community-Based Outpatient Clinic (CBOC): a VA-operated, VA-funded, or VA-reimbursed health care facility or site geographically distinct or separate from a parent medical facility. This term encompasses all types of VA outpatient clinics, except hospital-based, independent and mobile clinics. Satellite, community-based, and outreach clinics have been redefined as CBOCs. Technically, CBOCs fall into four Categories, which are: >(i) VA-owned. A CBOC that is owned and staffed by VA. >(ii) Leased. A CBOC where the space is leased (contracted), but is staffed by VA. NOTE: This includes donated space staffed by VA. >(iii) Contracted. A CBOC where the space and the staff are not VA. This is typically a Healthcare Management Organization (HMO)-type provided where multiple sites can be associated with a single station identifier. >(iv) Not Operational. A CBOC which has been approved by Congress, but has not yet begun operating.b. Hospital-Based Outpatient Clinic: outpatient clinic functions located at a hospital.c. Independent Outpatient Clinic: a full-time, self-contained, freestanding, ambulatory care clinic that has no management, program, or fiscal relationship to a VA medical facility. Primary and specialty health care services are provided in an outpatient setting.5. VET CENTER: Provides professional readjustment counseling, community education, outreach to special populations, brokering of services with community agencies, and access to links between the veteran and VA.6. VA MEDICAL CENTER (VAMC): a medical center is a unique VA site of care providing two or more types of services that reside at a single physical site location. The services provided are the primary service as tracked in the VHA Site Tracking (VAST) (i.e., VA Hospital, Nursing Home, Domiciliary, independent outpatient clinic (IOC), hospital-based outpatient clinic (HBOC), and CBOC). The definition of VA medical center does not include the Vet Centers as an identifying service. This dataset is based upon GFI data received from the National Geospatial-Intelligence Agency (NGA). At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 09/21/2007 and the newest record dates from 10/15/2007.

Dataset from the STAX Study, which investigated the acceptability, feasibility and cost of point of care testing for sexually transmitted infections among South African adolescents from a youth centre health facility and an adolescent mobile health service.

This dataset looks at enrollment data, STI testing data, and acceptability feedback from participants recruited from the Desmond Tutu Health Foundation Youth Centre.

Total-Assets Time Series for Phreesia Inc. Phreesia, Inc. provides an integrated SaaS-based software and payment platform for the healthcare industry in the United States and Canada. The company offers an appointment scheduling system for online appointments, reminders, and referral tracking and management; registration solutions to automate patient self-registration; revenue cycle solutions that provide insurance-verification processes, point-of-sale payments applications, post-visit payment collection, and flexible payment options; and network solutions to deliver clinically relevant content to patients. It deploys its platform in a range of modalities, including Phreesia Mobile, a patients' mobile device; PhreesiaPads, a self-service intake tablets; Phreesia Dashboard, a web-based dashboard for healthcare services clients; and Arrivals Kiosks, which are on-site kiosks. The company serves a range of healthcare services clients, including single-specialty practices, multi-specialty groups, and health systems; and pharmaceutical, medical device, and biotechnology companies, as well as government entities and other organizations. Phreesia, Inc. was incorporated in 2005 and is based in Wilmington, Delaware.

Background: There is a lack of evidence concerning the effective implementation of strategies for stroke prevention and management, particularly in resource-limited settings. A primary-care-based integrated mobile health intervention (SINEMA intervention) has been implemented and evaluated via a 1-year-long cluster-randomized controlled trial. This study reports the findings from the trial implementation and process evaluation that investigate the implementation of the intervention and inform factors that may influence the wider implementation of the intervention in the future.Methods: We developed an evaluation framework by employing both the RE-AIM framework and the MRC process evaluation framework to describe the implementation indicators, related enablers and barriers, and illustrate some potential impact pathways that may influence the effectiveness of the intervention in the trial. Quantitative data were collected from surveys and extracted from digital health monitoring systems. In addition, we conducted quarterly in-depth interviews with stakeholders in order to understand barriers and enablers of program implementation and effectiveness. Quantitative data analysis and thematic qualitative data analysis were applied, and the findings were synthesized based on the evaluation framework.Results: The SINEMA intervention was successfully implemented in 25 rural villages, reached 637 patients with stroke in rural Northern China during the 12 months of the trial. Almost 90% of the participants received all follow-up visits per protocol, and about half of the participants received daily voice messages. The majority of the intervention components were adopted by village doctors with some adaptation made. The interaction between human-delivered and technology-enabled components reinforced the program implementation and effectiveness. However, characteristics of the participants, doctor-patient relationships, and the healthcare system context attributed to the variation of program implementation and effectiveness.Conclusion: A comprehensive evaluation of program implementation demonstrates that the SINEMA program was well implemented in rural China. Findings from this research provide additional information for program adaptation, which shed light on the future program scale-up. The study also demonstrates the feasibility of combining RE-AIM and MRC process evaluation frameworks in process and implementation evaluation in trials.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT03185858.

In collaboration with Texas Adult Protective Services (APS) and one of the largest mobile healthcare providers in North Texas -- MedStarMobile Healthcare (MedStar) -- this study developed and piloted an elder abuse (EA) screening tool: Detection of Elder Abuse Through Emergency Care Technicians (DETECT). The DETECT tool was designed specifically to help medics identify potential EA among community-dwelling older adults during an emergency response. DETECT relies entirely on the medics' systematic observations of the older adults' physical and social environment -- no direct questioning of the older adult or their caregivers is involved. The DETECT tool was developed through an iterative, user-centered design process in which input was gathered from key stakeholders, and revisions to the tool incorporated their feedback. The intent was for that process to result in an EA screening tool that was easy for medics to use in the field and that helped medics capture information about older adults, their environments, and their caregivers that is thought to be associated with the occurrence of EA.

Golden Dataset Curation for LLMs Market Outlook

According to our latest research, the Global Golden Dataset Curation for LLMs market size was valued at $1.2 billion in 2024 and is projected to reach $8.7 billion by 2033, expanding at a CAGR of 24.8% during 2024–2033. This remarkable growth trajectory is primarily driven by the increasing demand for high-quality, bias-mitigated, and diverse datasets essential for training and evaluating large language models (LLMs) across industries. As generative AI applications proliferate, organizations are recognizing the strategic importance of curating "golden datasets"—carefully selected, annotated, and validated data collections that ensure robust model performance, regulatory compliance, and ethical AI outcomes. The accelerating adoption of AI-powered solutions in sectors such as healthcare, finance, and government, coupled with ongoing advances in data curation technologies, are further fueling the expansion of the Golden Dataset Curation for LLMs market globally.

Regional Outlook

North America currently commands the largest share of the Golden Dataset Curation for LLMs market, accounting for approximately 38% of the global revenue in 2024. This dominance is underpinned by the region’s mature artificial intelligence ecosystem, the presence of leading technology companies, and robust investments in R&D. The United States, in particular, boasts a high concentration of AI expertise, advanced data infrastructure, and a strong regulatory framework that supports ethical data curation. Furthermore, North America’s proactive adoption of generative AI across industries such as healthcare, BFSI, and government has spurred demand for meticulously curated datasets to drive innovation and ensure compliance with evolving data privacy standards. The region’s leadership in launching open-source initiatives and public-private partnerships for AI research further cements its preeminent position in the global market.

Asia Pacific is emerging as the fastest-growing region, projected to register a robust CAGR of 28.4% from 2024 to 2033. The region’s rapid market expansion is propelled by exponential growth in digital transformation initiatives, increasing AI investments, and supportive government policies aimed at fostering indigenous AI capabilities. Countries such as China, India, and South Korea are making significant strides in AI research, with a particular emphasis on local language and multimodal dataset curation to cater to diverse populations. The proliferation of startups and technology incubators, coupled with strategic collaborations between academia and industry, is accelerating the development and adoption of golden datasets. Additionally, the region’s burgeoning internet user base and mobile-first economies are generating vast volumes of data, providing fertile ground for dataset curation innovation.

Emerging economies in Latin America, the Middle East, and Africa are witnessing gradual but promising adoption of Golden Dataset Curation for LLMs. While market penetration remains lower compared to developed regions, localized demand for AI-driven solutions in sectors such as public health, education, and government services is spurring investment in dataset curation capabilities. However, challenges such as limited access to high-quality data, fragmented regulatory environments, and a shortage of specialized talent are impeding rapid growth. Despite these hurdles, targeted policy reforms, international collaborations, and capacity-building initiatives are laying the groundwork for future market expansion, particularly as governments recognize the strategic value of AI and data sovereignty.

Report Scope

Alternative clustering methods used for comparison purposes.