This webinar will explore address validation processes and tools. Learn how Texas and California approached the challenges of address validation in their respective child welfare case management systems. Speakers will discuss how millions of addresses were standardized, what decisions were made, what problems were encountered and how they were solved, interface considerations, cost issues, tools used, lessons learned, and future system considerations.

The following speakers will be presenting at this webinar:

Metadata-only record linking to the original dataset. Open original dataset below.

The construction of a robust healthcare information system is fundamental to enhancing countries’ capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China’s rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization’s (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital’s Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients’ physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264) This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO’s elimination goals of HBV infections.We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China. This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO’s elimination goals of HBV infections. We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China.

File Validation and Training Statistics

Validation, Training, and Testing Statistics for tasksource/leandojo Files

By tasksource (From Huggingface) [source]

About this dataset

The tasksource/leandojo: File Validation, Training, and Testing Statistics dataset is a comprehensive collection of information regarding the validation, training, and testing processes of files in the tasksource/leandojo repository. This dataset is essential for gaining insights into the file management practices within this specific repository.

The dataset consists of three distinct files: validation.csv, train.csv, and test.csv. Each file serves a unique purpose in providing statistics and information about the different stages involved in managing files within the repository.

In validation.csv, you will find detailed information about the validation process undergone by each file. This includes data such as file paths within the repository (file_path), full names of each file (full_name), associated commit IDs (commit), traced tactics implemented (traced_tactics), URLs pointing to each file (url), and respective start and end dates for validation.

train.csv focuses on providing valuable statistics related to the training phase of files. Here, you can access data such as file paths within the repository (file_path), full names of individual files (full_name), associated commit IDs (commit), traced tactics utilized during training activities (traced_tactics), URLs linking to each specific file undergoing training procedures (url).

Lastly, test.csv encompasses pertinent statistics concerning testing activities performed on different files within the tasksource/leandojo repository. This data includes information such as file paths within the repo structure (file_path), full names assigned to each individual file tested (full_name) , associated commit IDs linked with these files' versions being tested(commit) , traced tactics incorporated during testing procedures regarded(traced_tactics) ,relevant URLs directing to specific tested files(url).

By exploring this comprehensive dataset consisting of three separate CSV files - validation.csv, train.csv, test.csv - researchers can gain crucial insights into how effective strategies pertaining to validating ,training or testing tasks have been implemented in order to maintain high-quality standards within the tasksource/leandojo repository

How to use the dataset

• Familiarize Yourself with the Dataset Structure:

  • The dataset consists of three separate files: validation.csv, train.csv, and test.csv.
  • Each file contains multiple columns providing different information about file validation, training, and testing.

• Explore the Columns:

  • 'file_path': This column represents the path of the file within the repository.
  • 'full_name': This column displays the full name of each file.
  • 'commit': The commit ID associated with each file is provided in this column.
  • 'traced_tactics': The tactics traced in each file are listed in this column.
  • 'url': This column provides the URL of each file.

• Understand Each File's Purpose:

Validation.csv - This file contains information related to the validation process of files in the tasksource/leandojo repository.

Train.csv - Utilize this file if you need statistics and information regarding the training phase of files in tasksource/leandojo repository.

Test.csv - For insights into statistics and information about testing individual files within tasksource/leandojo repository, refer to this file.

• Generate Insights & Analyze Data:
• Once you have a clear understanding of each column's purpose, you can start generating insights from your analysis using various statistical techniques or machine learning algorithms.

• Explore patterns or trends by examining specific columns such as 'traced_tactics' or analyzing multiple columns together.

• Combine Multiple Files (if necessary):

• If required, you can merge/correlate data across different csv files based on common fields such as 'file_path', 'full_name', or 'commit'.

• Visualize the Data (Optional):

• To enhance your analysis, consider creating visualizations such as plots, charts, or graphs. Visualization can offer a clear representation of patterns or relationships within the dataset.

• Obtain Further Information:

• If you need additional details about any specific file, make use of the provided 'url' column to access further information.

Remember that this guide provides a general overview of how to utilize this dataset effectively. Feel ...

Billing-grade Interval Data Validation Market Outlook

According to our latest research, the global billing-grade interval data validation market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by the increasing demand for accurate and reliable data in utility billing and energy management systems. The market is expected to grow at a CAGR of 13.4% from 2025 to 2033, culminating in a projected market size of USD 4.54 billion by 2033. This substantial growth is primarily fueled by the proliferation of smart grids, the rising adoption of advanced metering infrastructure, and the necessity for regulatory compliance in billing operations across utilities and energy sectors. As per our research, the market’s momentum is underpinned by the convergence of digital transformation initiatives and the critical need for high-integrity interval data validation to support accurate billing and operational efficiency.

The growth trajectory of the billing-grade interval data validation market is significantly influenced by the rapid digitalization of utility infrastructure worldwide. With the deployment of smart meters and IoT-enabled devices, utilities are generating an unprecedented volume of interval data that must be validated for billing and operational purposes. The integration of advanced data analytics and machine learning algorithms into validation processes is enhancing the accuracy and reliability of interval data, minimizing errors, and enabling near real-time validation. This technological advancement is not only reducing manual intervention but also ensuring compliance with increasingly stringent regulatory standards. As utilities and energy providers transition toward more automated and data-centric operations, the demand for robust billing-grade data validation solutions is set to surge, driving market expansion.

Another critical growth factor for the billing-grade interval data validation market is the intensifying focus on energy efficiency and demand-side management. Governments and regulatory bodies across the globe are implementing policies to promote energy conservation, necessitating accurate measurement and validation of consumption data. Billing-grade interval data validation plays a pivotal role in ensuring that billings are precise and reflective of actual usage, thereby fostering trust between utilities and end-users. Moreover, the shift toward dynamic pricing models and time-of-use tariffs is making interval data validation indispensable for utilities aiming to optimize revenue streams and offer personalized billing solutions. As a result, both established utilities and emerging energy management firms are investing heavily in advanced validation platforms to stay competitive and meet evolving customer expectations.

The market is also witnessing growth due to the increasing complexity of utility billing systems and the diversification of energy sources, including renewables. The integration of distributed energy resources such as solar and wind into the grid is generating multifaceted data streams that require sophisticated validation to ensure billing accuracy and grid stability. Additionally, the rise of prosumers—consumers who also produce energy—has introduced new challenges in data validation, further amplifying the need for billing-grade solutions. Vendors are responding by developing scalable, interoperable platforms capable of handling diverse data types and validation scenarios. This trend is expected to drive innovation and shape the competitive landscape of the billing-grade interval data validation market over the forecast period.

From a regional perspective, North America continues to dominate the billing-grade interval data validation market, owing to its advanced utility infrastructure, widespread adoption of smart grids, and strong regulatory framework. However, Asia Pacific is emerging as the fastest-growing region, propelled by massive investments in smart grid projects, urbanization, and government initiatives to modernize energy distribution systems. Europe, with its emphasis on sustainability and energy efficiency, is also contributing significantly to market growth. The Middle East & Africa and Latin America, though currently smaller in market share, are expected to witness accelerated adoption as utilities in these regions embark on digital transformation journeys. Overall, the global market is set for dynamic growth, shaped by regional developments and technological advancements.

Component Analys

The data describe the validation process of the HiReSPECT II scanner. Experimental and simulated sensitivities and spatial resolution are presented. Other data will be presented into the manuscript.

This Data contains the PEN-Predictor-Keras-Model as well as the 100 validation data sets.

This paper drives the process of creating VMLA, a language test meant to be used during awake craniotomies. It focuses on step by step process and aims to help other developers to build their own assessment. This project was designed as a prospective study and registered in the Ethic Committee of Educational and Research Institute of Sirio Libanês Hospital. Ethics committee approval number: HSL 2018-37 / CAEE 90603318.9.0000.5461. Images were bought by Shutterstock.com and generated the following receipts: SSTK-0CA8F-1358 and SSTK-0235F-6FC2 VMLA is a neuropsychological assessment of language function, comprising object naming (ON) and semantic. Originally composed by 420 slides, validation among Brazilian native speakers left 368 figures plus fifteen other elements, like numbers, sentences and count. Validation was focused on educational level (EL), gender and age. Volunteers were tested in fourteen different states of Brazil. Cultural differences resulted in improvements to final Answer Template. EL and age were identified as factors that influenced VLMA assessment results. Highly educated volunteers performed better for both ON and semantic. People over 50 and 35 years old had better performance for ON and semantic, respectively. Further validation in unevaluated regions of Brazil, including more balanced number of males and females and more even distribution of age and EL, could confirm our statistical analysis. After validation, ON-VMLA was framed in batteries of 100 slides each, mixing images of six different complexity categories. Semantic-VMLA kept all the original seventy verbal and non-verbal combinations. The validation process resulted in increased confidence during intraoperative test application. We are now able to score and evaluate patient´s language deficits. Currently, VLMA fits its purpose of dynamical application and accuracy during language areas mapping. It is the first test targeted to Brazilians, representing much of our culture and collective imagery. Our experience may be of value to clinicians and researchers working with awake craniotomy who seek to develop their own language test.

The test is available for free use at www.vemotests.com (beginning in February, 2021)

Synthetic Data Validation for ADAS Market Outlook

According to our latest research, the global synthetic data validation for ADAS market size reached USD 820 million in 2024, reflecting a robust and expanding sector within the automotive industry. The market is projected to grow at a CAGR of 23.7% from 2025 to 2033, culminating in a forecasted market size of approximately USD 6.5 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of advanced driver-assistance systems (ADAS) in both passenger and commercial vehicles, the rising complexity of autonomous driving functions, and the need for scalable, safe, and cost-effective validation processes.

A significant growth factor for the synthetic data validation for ADAS market is the accelerating integration of ADAS technologies across automotive OEMs and Tier 1 suppliers. As regulatory bodies worldwide tighten safety standards and mandate the inclusion of features such as automatic emergency braking, lane-keeping assistance, and adaptive cruise control, manufacturers are compelled to validate these systems rigorously. Traditional data collection for ADAS validation is not only time-consuming and resource-intensive but also limited in its ability to reproduce rare or hazardous scenarios. Synthetic data validation addresses these challenges by enabling the creation of diverse, customizable datasets that accurately simulate real-world driving conditions, substantially reducing development timelines and costs while ensuring compliance with safety regulations.

Another critical driver is the rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, which underpin both synthetic data generation and validation processes. As ADAS algorithms become increasingly sophisticated, the demand for high-quality, annotated, and scalable datasets grows in tandem. Synthetic data validation empowers developers to generate massive volumes of data that cover edge cases and rare events, which are otherwise difficult or dangerous to capture in real-world testing. This capability not only expedites the training and validation of perception models but also enhances their robustness, reliability, and generalizability, paving the way for higher levels of vehicle autonomy and improved road safety.

The proliferation of connected and autonomous vehicles is further amplifying the need for synthetic data validation within the ADAS market. As vehicles become more reliant on sensor fusion, object detection, and path planning algorithms, the complexity of validation scenarios increases exponentially. Synthetic data validation enables the simulation of intricate driving environments, sensor malfunctions, and unpredictable human behaviors, ensuring that ADAS-equipped vehicles can safely navigate diverse and dynamic real-world conditions. The scalability and flexibility offered by synthetic data solutions are particularly attractive to automotive OEMs, Tier 1 suppliers, and research institutes striving to maintain a competitive edge in the fast-evolving mobility landscape.

Regionally, North America and Europe are leading adopters of synthetic data validation for ADAS, driven by stringent safety regulations, a strong presence of automotive technology pioneers, and significant investments in autonomous vehicle research. However, Asia Pacific is emerging as a high-growth market, fueled by the rapid expansion of the automotive sector, increasing consumer demand for advanced safety features, and government initiatives supporting smart mobility. Latin America and the Middle East & Africa are also witnessing gradual adoption, primarily through collaborations with global OEMs and technology providers. The global landscape is characterized by a dynamic interplay of regulatory frameworks, technological advancements, and evolving consumer expectations, shaping the future trajectory of the synthetic data validation for ADAS market.

Component Analysis

The synthetic data validation for ADAS market is segmented by compone

Loan Boarding Data Validation Market Outlook

According to our latest research, the global Loan Boarding Data Validation market size reached USD 1.42 billion in 2024, demonstrating robust momentum driven by increasing digitalization in the financial sector and stringent regulatory requirements. The market is projected to grow at a CAGR of 11.8% from 2025 to 2033, reaching an estimated USD 4.06 billion by 2033. This dynamic growth is underpinned by the escalating need for accurate data validation, risk mitigation, and compliance management across lending institutions worldwide.

A key growth factor propelling the Loan Boarding Data Validation market is the intensifying demand for automated solutions that ensure data accuracy throughout the loan lifecycle. With the proliferation of digital lending platforms, financial institutions are under increasing pressure to verify and validate vast volumes of loan data in real time. The integration of advanced analytics, machine learning, and artificial intelligence into validation processes has significantly enhanced the speed, accuracy, and efficiency of loan boarding. This technological evolution is not only reducing manual errors but also minimizing operational costs, thereby driving the adoption of sophisticated data validation tools across banks, mortgage lenders, and credit unions.

Another pivotal driver is the ever-tightening regulatory landscape governing the global financial services industry. Regulatory bodies such as the Basel Committee, the European Banking Authority, and the US Federal Reserve have imposed rigorous guidelines around data integrity, anti-money laundering (AML), and Know Your Customer (KYC) protocols. As a result, organizations are compelled to invest in comprehensive data validation solutions to ensure compliance, avoid penalties, and maintain customer trust. The increasing complexity and frequency of regulatory audits have made the deployment of robust validation frameworks not just a best practice, but a necessity for sustainable operations in the lending sector.

The surge in digital transformation initiatives across both developed and emerging economies is further accelerating market growth. Financial institutions are leveraging cloud-based solutions and digital onboarding platforms to enhance customer experience and streamline back-office operations. This shift is fostering the adoption of Loan Boarding Data Validation platforms that offer scalable, secure, and real-time validation capabilities. Moreover, the growing trend of mergers and acquisitions in the banking sector is necessitating seamless data migration and integration, which in turn fuels the demand for advanced validation technologies. The convergence of these factors is expected to sustain the market's upward trajectory throughout the forecast period.

Regionally, North America continues to dominate the Loan Boarding Data Validation market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading financial institutions, early adoption of digital technologies, and a robust regulatory environment have cemented North America's leadership position. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, expanding financial inclusion, and government-led digital lending initiatives. Latin America and the Middle East & Africa are also emerging as promising markets, as local banks and lenders increasingly recognize the value of automated data validation in enhancing operational efficiency and regulatory compliance.

Component Analysis

The Loan Boarding Data Validation market by component is primarily segmented into Software and Services. The software segment is witnessing substantial growth due to the rising adoption of automated validation tools that streamline the loan boarding process. These software solutions are equipped with features such as real-time data verification, audit trails, and customizable rule engines, which significantly reduce manual intervention and associated errors. Financial institutions are increasingly investing in advanced software platforms to ensure data accuracy, enhance compliance, and improve customer experience. The integration of artificial intelligence and machine learning algorithms within these software solutions is further elevating their efficiency and scalability, making them indispensable for modern lending operations.

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Several genes involved in different processes were measured by qPCR in order to determine expression levels and to validate the data gathered by DNA microarrays.

PMU Data Quality Validation Services Market Outlook

According to our latest research, the global PMU Data Quality Validation Services market size in 2024 stands at USD 1.38 billion, with a robust compound annual growth rate (CAGR) of 12.7% projected through the forecast period. This growth is primarily fueled by the increasing integration of advanced grid management technologies and the rising need for real-time data accuracy in power systems worldwide. By 2033, the market is expected to reach USD 4.12 billion, reflecting the sector’s rapid expansion and the escalating importance of data quality in modern energy infrastructures. These findings are based on the most recent industry data and comprehensive market analysis conducted in 2025.

One of the key growth drivers for the PMU Data Quality Validation Services market is the accelerating adoption of Phasor Measurement Units (PMUs) across global power grids. As utilities and grid operators strive to enhance grid reliability and resilience, PMUs have become essential for real-time monitoring and control. However, the effectiveness of PMUs is heavily dependent on the quality and integrity of the data they generate. This has led to a surge in demand for specialized data validation services, including data cleansing, auditing, and monitoring, ensuring that only accurate and actionable information is used for grid management. The increasing frequency of grid disturbances and the integration of renewable energy sources further underscore the need for robust data quality frameworks, propelling market growth.

Technological advancements are also playing a pivotal role in shaping the PMU Data Quality Validation Services market. The proliferation of advanced analytics, artificial intelligence (AI), and machine learning (ML) in data validation processes has significantly improved the efficiency and accuracy of data quality assessments. These technologies enable automated detection of anomalies, real-time data correction, and predictive maintenance, thereby reducing operational risks and enhancing decision-making capabilities for utilities and grid operators. As digital transformation sweeps through the energy sector, the adoption of cloud-based validation solutions and scalable service models is expanding, making high-quality data validation services accessible to a broader range of end-users and regions.

Another significant factor contributing to market growth is the increasing regulatory emphasis on grid reliability and data integrity. Governments and regulatory bodies across North America, Europe, and Asia Pacific are mandating stricter compliance standards for power system monitoring and reporting. This regulatory push is compelling utilities and industrial users to invest in comprehensive data validation and auditing services to ensure adherence to industry standards and minimize the risk of non-compliance penalties. The convergence of regulatory requirements, technological innovation, and the critical need for reliable grid operations is expected to sustain the upward trajectory of the PMU Data Quality Validation Services market in the coming years.

From a regional perspective, North America currently leads the market, driven by substantial investments in smart grid infrastructure and early adoption of PMU technologies. Europe and Asia Pacific are also witnessing rapid growth, fueled by government initiatives to modernize aging power grids and integrate renewable energy sources. In particular, Asia Pacific is emerging as a high-growth region, with countries like China and India investing heavily in grid modernization projects and digital transformation. Latin America and the Middle East & Africa, while still nascent markets, are expected to experience accelerated growth as grid modernization initiatives gain momentum and the benefits of high-quality data validation become more widely recognized.

Service Type Analysis

The Service Type segment within the PMU Data Quality Validation Services ma

Types of data processing Claude's Code Interpreter can handle

PLACI Data Quality Validation for Airfreight Market Outlook

According to our latest research, the global PLACI Data Quality Validation for Airfreight market size reached USD 1.18 billion in 2024, with a robust CAGR of 14.6% projected through the forecast period. By 2033, the market is expected to attain a value of USD 3.58 billion, driven by the increasing adoption of digital transformation initiatives and regulatory compliance requirements across the airfreight sector. The growth in this market is primarily fueled by the rising need for accurate, real-time data validation to ensure security, compliance, and operational efficiency in air cargo processes.

The surge in e-commerce and global trade has significantly contributed to the expansion of the PLACI Data Quality Validation for Airfreight market. As airfreight volumes continue to soar, the demand for rapid, secure, and compliant cargo movement has never been higher. This has necessitated the implementation of advanced data quality validation solutions to manage the vast amounts of information generated during air cargo operations. Regulatory mandates such as the Pre-Loading Advance Cargo Information (PLACI) requirements in various regions have further compelled airlines, freight forwarders, and customs authorities to adopt robust data validation systems. These solutions not only help in mitigating risks associated with incorrect or incomplete data but also streamline cargo screening and documentation processes, leading to improved efficiency and reduced operational bottlenecks.

Technological advancements have played a pivotal role in shaping the PLACI Data Quality Validation for Airfreight market. The integration of artificial intelligence, machine learning, and big data analytics has enabled stakeholders to automate and enhance data validation processes. These technologies facilitate real-time risk assessment, anomaly detection, and compliance checks, ensuring that only accurate and verified data is transmitted across the airfreight ecosystem. The shift towards cloud-based deployment models has further accelerated the adoption of these solutions, offering scalability, flexibility, and cost-effectiveness to both large enterprises and small and medium-sized businesses. As the market matures, we expect to see increased collaboration between technology providers and airfreight stakeholders to develop customized solutions tailored to specific operational and regulatory needs.

The evolving regulatory landscape is another key growth driver for the PLACI Data Quality Validation for Airfreight market. Governments and international organizations are continuously updating air cargo security protocols to address emerging threats and enhance global supply chain security. Compliance with these regulations requires airfreight operators to validate data accuracy at multiple touchpoints, from cargo screening to documentation validation. Failure to comply can result in severe penalties, shipment delays, and reputational damage. Consequently, there is a growing emphasis on implementing end-to-end data validation frameworks that not only meet regulatory requirements but also provide actionable insights for risk management and operational optimization. This trend is expected to persist throughout the forecast period, further propelling market growth.

From a regional perspective, North America currently dominates the PLACI Data Quality Validation for Airfreight market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of major air cargo hubs, stringent regulatory frameworks, and high technology adoption rates in these regions have contributed to their market leadership. Asia Pacific is expected to witness the fastest growth during the forecast period, driven by the rapid expansion of cross-border e-commerce, increasing air cargo volumes, and ongoing investments in digital infrastructure. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as key markets, supported by improving logistics networks and growing awareness of data quality validation benefits.

Solution Type Analysis

The PLACI Data Quality Validation for Airfreight market is segmented by solution type into software and services, each playing a critical role in ensuring data integrity and compliance across the airfreight value chain. Software solutions encompass a wide range of applications, including automated data validation tools, risk assessment engines

Interval Data Validation and Estimation Tools Market Outlook

According to our latest research, the Global Interval Data Validation and Estimation Tools market size was valued at $1.42 billion in 2024 and is projected to reach $4.98 billion by 2033, expanding at a robust CAGR of 14.7% during the forecast period of 2025–2033. The primary factor fueling this significant growth is the increasing demand for high-quality, reliable data across industries, driven by the proliferation of big data analytics, regulatory compliance requirements, and the digital transformation of core business processes. As organizations continue to digitize their operations, the need for advanced interval data validation and estimation tools that can ensure data accuracy, integrity, and actionable insights has never been more critical.

Regional Outlook

North America currently dominates the global interval data validation and estimation tools market, accounting for the largest share of global revenue in 2024. The region’s leadership can be attributed to its mature IT infrastructure, high adoption rates of advanced analytics, and a strong regulatory environment that prioritizes data integrity and compliance. Major industries such as BFSI, healthcare, and IT & telecommunications in the United States and Canada are heavily investing in sophisticated data validation and estimation solutions to mitigate risks associated with inaccurate or incomplete data. Furthermore, the presence of leading technology vendors and an innovation-driven business ecosystem have accelerated the deployment of both on-premises and cloud-based solutions, solidifying North America’s market dominance.

In contrast, the Asia Pacific region is emerging as the fastest-growing market, projected to register the highest CAGR of 17.2% during the forecast period. This rapid growth is fueled by substantial investments in digital infrastructure, expanding IT and telecom sectors, and increasing regulatory scrutiny regarding data management in countries such as China, India, and Japan. Governments and enterprises in Asia Pacific are actively adopting interval data validation and estimation tools to enhance data-driven decision-making, improve operational efficiency, and comply with evolving data privacy laws. The influx of global technology providers, coupled with the rise of local solution developers, is further catalyzing market expansion in this region.

Meanwhile, emerging economies in Latin America, the Middle East, and Africa are gradually embracing interval data validation and estimation tools, albeit at a slower pace due to challenges such as limited digital infrastructure, budget constraints, and varying regulatory frameworks. However, growing awareness about the importance of data quality for business competitiveness and increasing investments in digital transformation are expected to drive adoption over the coming years. Localized solutions tailored to address specific regulatory and operational requirements are gaining traction, particularly in sectors like government, healthcare, and retail, where data accuracy is increasingly critical.

Report Scope

This dataset contains the deidentified data used in the validation process for the Math-Biology Values Instrument (MBVI). MBVI_spring2016 contains data collected from undergraduate life science majors (via electronic survey) to develop the MBVI and was used for exploratory factor analyses and establishing convergent and divergent validity. MBVI_fall2016 contains data collected from a second independent sample of undergraduate life science majors (also via electronic survey) that was used for confirmatory factor analyses. The two "key" files contain survey item text, response options, and notes for all column headings in the data files. A full description of the data collection process and analyses can be found in the related publication cited below.

Analytical validation has fundamental importance in the scope of Good Manufacturing Practice (GMP) for pharmaceutical products since it establishes scientific evidence that an analytical procedure provides reliable results. However, even with validation guidelines available it is very common to observe misunderstandings in the execution of validation and data interpretation. The misguided approaches of validation guidelines, allied with a disregard for the peculiarities of the analytical techniques, the nature of the sample, and the analytical purpose, have significantly contributed to oversights in analytical validation. This work aims to present a critical overview of the validation process in pharmaceutical analysis, addressing relevant aspects of various analytical performance parameters, their different means of accomplishment and limitations in face of the analytical techniques, the nature of the sample, and the analytical purpose. To help in the planning and execution of the validation process, some case studies are discussed, mainly in the area of high-performance liquid chromatography (HPLC).

Data for method validation on detecting pmp-glucose by HPLC

The objective of the fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to Artificial Intelligence for automatization and improving reliability of control processes.

This dataset contains the quantitative questionnaire designed for the validation of a serious game aimed at teaching business ethics, specifically focusing on informal business practices. It includes the questionnaire itself, a detailed codebook, and the dataset generated from the experimental application and validation of the game.

The questionnaire was developed based on a solid theoretical foundation, integrating the Technology Acceptance Model III (TAM III) and the Theory of Planned Behavior (TPB). The dataset comprises responses from 118 accounting students from a Peruvian university who participated in the experimental phase of the game.

Additionally, this dataset includes the data validation process conducted to ensure its suitability for Partial Least Squares Structural Equation Modeling (PLS-SEM), using SmartPLS 4 software. Researchers and educators interested in serious games, business ethics education, or behavioral modeling will find this dataset valuable for further studies and applications.

Ideal for researchers in business ethics, educational technology, and behavioral studies.

This data package contains information on Structured Product Labeling (SPL) Terminology for SPL validation procedures and information on performing SPL validations.