The Validation extension for CKAN enhances data quality within the CKAN ecosystem by leveraging the Frictionless Framework to validate tabular data. This extension allows for automated data validation, generating comprehensive reports directly accessible within the CKAN interface. The validation process helps identify structural and schema-level issues, ensuring data consistency and reliability. Key Features: Automated Data Validation: Performs data validation automatically in the background or during dataset creation, streamlining the quality assurance process. Comprehensive Validation Reports: Generates detailed reports on data quality, highlighting issues such as missing headers, blank rows, incorrect data types, or values outside of defined ranges. Frictionless Framework Integration: Utilizes the Frictionless Framework library for robust and standardized data validation. Exposed Actions: Provides accessible action functions that allows data validation to be integrated into custom workflows from other CKAN extensions. Command Line Interface: Offers a command-line interface (CLI) to manually trigger validation jobs for specific datasets, resources, or based on search criteria. Reporting Utilities: Enables the generation of global reports summarizing validation statuses across all resources. Use Cases: Improve Data Quality: Ensures data integrity and adherence to defined schemas, leading to better data-driven decision-making. Streamline Data Workflows: Integrates validation as part of data creation or update processes, automating quality checks and saving time. Customize Data Validation Rules: Allows developers to extend the validation process with their own custom workflows and integrations using the exposed actions. Technical Integration: The Validation extension integrates deeply within CKAN by providing new action functions (resourcevalidationrun, resourcevalidationshow, resourcevalidationdelete, resourcevalidationrunbatch) that can be called via the CKAN API. It also includes a plugin interface (IPipeValidation) for more advanced customization, which allows other extensions to receive and process validation reports. Users can utilize the command-line interface to trigger validation jobs and generate overview reports. Benefits & Impact: By implementing the Validation extension, CKAN installations can significantly improve the quality and reliability of their data. This leads to increased trust in the data, better data governance, and reduced errors in downstream applications that rely on the data. Automated validation helps to proactively identify and resolve data issues, contributing to a more efficient data management process.

The global data validation services market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period. Growing concerns over data inaccuracy and the increasing volume of data being generated by organizations are the key factors driving the market growth. Additionally, the adoption of cloud-based data validation solutions is expected to further fuel the market expansion. North America and Europe are the largest markets for data validation services, with a significant presence of large enterprises and stringent data regulations. The market is fragmented with several established players and a number of emerging vendors offering specialized solutions. Key market participants include TELUS Digital, Experian Data Quality, Flatworld Solutions Inc., Precisely, LDC, InfoCleanse, Level Data, Damco Solutions, Environmental Data Validation Inc., DataCaptive, Process Fusion, Ann Arbor Technical Services, Inc., and others. These companies are focusing on expanding their geographical reach, developing new products and features, and offering value-added services to gain a competitive edge in the market. The growing demand for data privacy and security solutions is also expected to drive the adoption of data validation services in the coming years.

The Validator extension for CKAN enables data validation within the CKAN ecosystem, leveraging the 'goodtables' library. This allows users to ensure the quality and integrity of tabular data resources published and managed within their CKAN instances. By integrating data validation capabilities, the extension aims to improve data reliability and usability. Key Features: Data Validation using Goodtables: Utilizes the 'goodtables' library for validating tabular data resources, providing a standardized and robust validation process. Automated Validation: Automatically validate packages, resources or datasets upon each upload or update. Technical Integration: Given the limited information in the README, it can be assumed that the extension integrates with the CKAN resource creation and editing workflow. The extension likely adds validation steps to the data upload and modification process, possibly providing feedback to users on any data quality issues detected. Benefits & Impact: By implementing the Validator extension, data publishers increase the reliability and reusability of data resources. This directly improves data quality control, enhances collaboration, lowers the risk of data-driven problems in data applications, and creates opportunities for data-driven organizations to scale up.

The global data validation services market is anticipated to grow exponentially over the coming years. The market is projected to reach a value of USD 25.47 billion by 2033, expanding at a CAGR of 14.2% from 2025 to 2033. The increasing volume of data, growing need for data accuracy, and stringent regulatory compliance are major drivers fueling the market growth. Moreover, the adoption of cloud-based data validation solutions, growing adoption of AI and ML technologies, and increasing investments in data governance initiatives are anticipated to create lucrative opportunities for market players. The market is segmented based on type, application, enterprise size, and region. The cloud-based segment is expected to hold the largest market share due to its scalability, cost-effectiveness, and accessibility. The SMEs segment is projected to grow at a higher CAGR, driven by the increasing adoption of data validation solutions among small and medium-sized businesses. The North American region is anticipated to dominate the market, followed by Europe and Asia Pacific. Key market players include TELUS Digital, Experian Data Quality, Flatworld Solutions Inc., Precisely, LDC, InfoCleanse, Level Data, Damco Solutions, Environmental Data Validation Inc., DataCaptive, Process Fusion, Ann Arbor Technical Services, Inc., among others.

The Data Validation Services market is experiencing robust growth, driven by the increasing reliance on data-driven decision-making across various industries. The market's expansion is fueled by several key factors, including the rising volume and complexity of data, stringent regulatory compliance requirements (like GDPR and CCPA), and the growing need for data quality assurance to mitigate risks associated with inaccurate or incomplete data. Businesses are increasingly investing in data validation services to ensure data accuracy, consistency, and reliability, ultimately leading to improved operational efficiency, better business outcomes, and enhanced customer experience. The market is segmented by service type (data cleansing, data matching, data profiling, etc.), deployment model (cloud, on-premise), and industry vertical (healthcare, finance, retail, etc.). While the exact market size in 2025 is unavailable, a reasonable estimation, considering typical growth rates in the technology sector and the increasing demand for data validation solutions, could be placed in the range of $15-20 billion USD. This estimate assumes a conservative CAGR of 12-15% based on the overall IT services market growth and the specific needs for data quality assurance. The forecast period of 2025-2033 suggests continued strong expansion, primarily driven by the adoption of advanced technologies like AI and machine learning in data validation processes. Competitive dynamics within the Data Validation Services market are characterized by the presence of both established players and emerging niche providers. Established firms like TELUS Digital and Experian Data Quality leverage their extensive experience and existing customer bases to maintain a significant market share. However, specialized companies like InfoCleanse and Level Data are also gaining traction by offering innovative solutions tailored to specific industry needs. The market is witnessing increased mergers and acquisitions, reflecting the strategic importance of data validation capabilities for businesses aiming to enhance their data management strategies. Furthermore, the market is expected to see further consolidation as larger players acquire smaller firms with specialized expertise. Geographic expansion remains a key growth strategy, with companies targeting emerging markets with high growth potential in data-driven industries. This makes data validation a lucrative market for both established and emerging players.

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.

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 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)

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

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the competition are provided here, as well as competition details and baseline solutions. The data are derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

Data for method validation on detecting pmp-glucose by HPLC

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

The datasetvalidation extension for CKAN enforces a mandatory data validation step before datasets can be published. This plugin ensures that only validated datasets are made publicly available, thus promoting data quality and reliability within the CKAN data portal. By integrating a validation process, the extension helps maintain the integrity of the data catalog and reduces the risk of publishing flawed or incorrect information.

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.

Cross-validation is one of the most popular model and tuning parameter selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, due to the ignorance of the uncertainty in the testing sample. We develop a novel statistically principled inference tool based on cross-validation that takes into account the uncertainty in the testing sample. This method outputs a set of highly competitive candidate models containing the optimal one with guaranteed probability. As a consequence, our method can achieve consistent variable selection in a classical linear regression setting, for which existing cross-validation methods require unconventional split ratios. When used for tuning parameter selection, the method can provide an alternative trade-off between prediction accuracy and model interpretability than existing variants of cross-validation. We demonstrate the performance of the proposed method in several simulated and real data examples. Supplemental materials for this article can be found online.

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Email Validation Tools Market Outlook

The global email validation tools market size was valued at approximately USD 1.1 billion in 2023 and is expected to reach around USD 2.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.2% during the forecast period. The robust growth in this market is driven by increasing demand for accurate and reliable email communication, as well as the rising awareness of the necessity to maintain clean email lists to enhance marketing effectiveness and ensure compliance with data protection regulations.

One of the key growth factors propelling the email validation tools market is the increasing adoption of digital marketing strategies by businesses across various sectors. As companies strive to reach their target audience efficiently, the need for accurate email lists has become paramount. Invalid email addresses can lead to wasted resources, and lower email deliverability rates, and even harm the sender's reputation. Therefore, businesses are investing in email validation tools to ensure that their email marketing campaigns reach the intended recipients, thereby maximizing their return on investment.

Furthermore, the growing emphasis on data security and privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, has significantly contributed to the market growth. These regulations mandate businesses to maintain clean and accurate email lists to avoid penalties and ensure compliance. Email validation tools help organizations adhere to these regulations by identifying and removing invalid or risky email addresses, thus mitigating the risk of data breaches and improving email deliverability.

Another factor driving the market is the increasing use of artificial intelligence (AI) and machine learning (ML) technologies in email validation tools. These advanced technologies enhance the accuracy and efficiency of email validation processes by analyzing large volumes of data and identifying patterns that indicate invalid or fraudulent email addresses. The integration of AI and ML in email validation tools not only improves the quality of email lists but also reduces the time and effort required for manual validation, thereby enhancing overall operational efficiency for businesses.

Regionally, North America holds the largest share in the email validation tools market due to the early adoption of advanced technologies and the presence of a large number of email marketing companies in the region. The United States, in particular, is a major contributor to market growth, driven by the high penetration of digital marketing and stringent data protection regulations. Europe follows closely, with significant growth opportunities arising from the strict enforcement of GDPR. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digital transformation of businesses and the increasing adoption of email marketing strategies in emerging economies such as India and China.

Component Analysis

The email validation tools market is segmented into software and services. The software segment dominates the market and is anticipated to maintain its dominance throughout the forecast period. Email validation software solutions offer comprehensive features such as syntax verification, domain validation, and email address checking, which are essential for maintaining a clean and accurate email list. The growing adoption of cloud-based software solutions is further driving the growth of this segment, as businesses seek scalable and cost-effective solutions to manage their email marketing campaigns.

Services, on the other hand, represent a smaller but steadily growing segment within the email validation tools market. These services include consulting, implementation, and support services that help businesses optimize their email validation processes. As the competition intensifies, service providers are offering customized solutions to meet the specific needs of different industries, thereby enhancing the overall customer experience. Additionally, the increasing complexity of email validation processes, driven by the evolving nature of email threats and spam, is leading to a higher demand for expert services to ensure the effectiveness of email validation tools.

Within the software segment, the integration of artificial intelligence and machine learning technologies is a notable trend. These technologies enhance the accuracy and efficiency of email v

A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalised relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions are representative of the real world based on the specific conditions and confidence level pertaining to the experiment from which the measurements were acquired. Relative error metrics are traditionally designed for use with series of data values but orthogonal decomposition has been employed to reduce the dimensionality of data matrices to feature vectors so that the metric can be applied to fields of data. Three previously published case studies are employed to demonstrate the efficacy of this quantitative approach to the validation process in the discipline of structural analysis, for which historical data was available; however, the concept could be applied to a wide range of disciplines and sectors where modelling and simulation plays a pivotal role. ValidationMetricmatlab functionCS_rubber_blockfeature vectors describing data fields for case study 2 (rubber block)CS_ibeamfeature vectors representing data fields in case study 1(i-beam)CS_bonnetfeature vectors describing data in case study 3 (bonne liner)

The global medical device process validation services market is experiencing robust growth, projected to reach a market size of $2.5 billion in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033. This growth is fueled by several key drivers, including the increasing stringency of regulatory requirements for medical devices, the rising demand for advanced medical technologies, and the growing adoption of automation in medical device manufacturing. The pharmaceutical and medical industries are the largest consumers of these services, demanding rigorous validation procedures to ensure product safety and efficacy. Further growth is anticipated from the increasing outsourcing of validation activities by medical device manufacturers, seeking specialized expertise and cost optimization. Market segmentation reveals significant demand across various validation types, including medical device manufacturing and packaging validation, cleaning and sterilization validation, and automation/control system validation. While data limitations prevent precise regional breakdowns, North America and Europe are expected to dominate market share due to the presence of established medical device manufacturers and stringent regulatory landscapes. The market's trajectory reflects the increasing complexity of medical device manufacturing and the associated need for comprehensive validation services. Future growth will be shaped by advancements in validation technologies, such as digitalization and AI, as well as the evolving regulatory landscape. Challenges remain, including the high cost of validation services and the potential for skill shortages in the specialized workforce. Despite these restraints, the ongoing emphasis on patient safety and product quality will continue to drive demand for these critical services across all segments and regions, ensuring sustained market expansion throughout the forecast period.

These are sets of data collected from the manual cross-validation of DOIs (and related research outputs) that are sampled from Web of Science (WoS), Scopus and Microsoft Academic (MSA). For each of the 15 universities, we initially collect all DOIs indexed by each of the three bibliographic sources. Subsequently, we randomly sample 40, 30 and 30 DOIs from sets of DOIs that are exclusively indexed by WoS, Scopus and MSA, respectively, for each university. A manual cross-validation process is then followed to validate certain characteristics across the data sources. This cross-validation process was carried out by a data wrangler, on a part-time basis over a few months, for which online data was accessed from 18 December 2018 to 20 May 2019.