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.

Data Warehouse Testing Market Outlook

The global data warehouse testing market size was valued at USD 600 million in 2023 and is projected to reach USD 1.2 billion by 2032, growing at a CAGR of 7.5% during the forecast period. The increasing need for businesses to ensure the accuracy and integrity of their data in a rapidly digitizing world is a significant growth factor propelling the market forward.

The rise in data volumes and the increasing complexity of business intelligence tools are primary drivers of the data warehouse testing market. Organizations are now more reliant on data-driven decision-making, requiring robust testing mechanisms to validate data quality and performance. As businesses generate and utilize massive amounts of data, ensuring the reliability of data warehouses becomes critical to maintain operational efficiency and effective decision-making. This trend is anticipated to continue, thus bolstering the growth of the data warehouse testing market.

Another critical growth driver is the proliferation of cloud-based solutions. With more enterprises shifting their data infrastructure to the cloud, the demand for cloud-specific data warehouse testing is on the rise. Cloud platforms offer scalability and flexibility, but they also bring about unique challenges in data integrity, performance, and security. This necessitates specialized testing services to ensure that data remains accurate and secure, no matter where it is stored or processed. The shift towards cloud computing is expected to significantly contribute to the market's expansion.

Moreover, regulatory compliance and data governance are becoming increasingly stringent across various industries. With data breaches and cyber-attacks becoming more prevalent, organizations must adhere to regulatory standards to ensure the security and privacy of their data. Data warehouse testing services help enterprises meet these regulatory requirements by ensuring that their data storage and processing mechanisms are secure and compliant. This need for compliance is another major factor driving the demand for data warehouse testing services.

Data Warehouse Software plays a pivotal role in the architecture of modern businesses, serving as the backbone for data storage and management. As organizations continue to generate vast amounts of data, the need for sophisticated data warehouse software becomes increasingly evident. This software not only facilitates the storage of data but also ensures its accessibility and reliability, which are crucial for effective data analysis and decision-making. With the rise of big data and analytics, data warehouse software is evolving to accommodate more complex data structures and larger volumes, making it an indispensable tool for businesses aiming to maintain a competitive edge. The integration of advanced features such as real-time data processing and enhanced security measures further underscores the importance of data warehouse software in today's data-driven landscape.

Regionally, North America is expected to lead the market due to the early adoption of advanced technologies and the presence of significant market players. The Asia Pacific region is anticipated to witness rapid growth, driven by the increasing digitization of businesses and the growing adoption of cloud solutions. Europe, Latin America, and the Middle East & Africa are also expected to contribute significantly to the market, driven by the increasing awareness of data importance and regulatory compliance needs.

Type Analysis

ETL Testing is a crucial segment within the data warehouse testing market. ETL (Extract, Transform, Load) processes are fundamental to data warehousing as they involve the movement and transformation of data from source systems to data warehouses. ETL testing ensures that the data is accurately extracted, correctly transformed, and loaded into the target data warehouse without loss or corruption. Given the importance of accurate data for business intelligence and analytics, ETL testing is considered indispensable. This segment is expected to grow steadily as more organizations invest in robust ETL processes to handle increasing data volumes.

Data Integrity Testing is another vital segment, focusing on ensuring that the data stored in the data warehouse matches the source data and remains consistent over time. This type of testing is crucial for maintaining the trustworthiness of data analytics and

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.

Success.ai’s Licensed Professionals Data API equips organizations with the data intelligence they need to confidently engage with professionals across regulated industries. Whether you’re verifying the credentials of healthcare practitioners, confirming licensing status for legal advisers, or identifying certified specialists in construction, this API provides real-time, AI-validated details on certifications, licenses, and qualifications.

By tapping into over 700 million verified profiles, you can ensure compliance, build trust, and streamline your due diligence processes. Backed by our Best Price Guarantee, Success.ai’s solution helps you operate efficiently, mitigate risk, and maintain credibility in highly regulated markets.

Why Choose Success.ai’s Licensed Professionals Data API?

1. Verified Licenses & Certifications

   • Access detailed information about professional credentials, educational backgrounds, and accreditations.
   • Rely on 99% data accuracy through AI-driven validation, ensuring each verification is robust and reliable.

2. Comprehensive Global Coverage

   • Includes professionals across healthcare, law, construction, finance, engineering, and more.
   • Confidently scale verification processes as you enter new regions or explore diverse regulated markets.

3. Continuously Updated Data

   • Receive real-time updates to maintain up-to-date compliance records and accurate credential checks.
   • Respond swiftly to changes in licensing standards, new certifications, or shifting industry requirements.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.
   • Safeguard brand reputation and reduce risk of non-compliance with strict regulatory environments.

Data Highlights:

• Over 700M Verified Profiles: Engage with a vast pool of licensed professionals worldwide.
• Licensing & Qualification Details: Confirm professional credentials, specializations, and areas of practice.
• Continuous Data Refresh: Always access current, reliable data for timely verifications.
• Best Price Guarantee: Optimize ROI by leveraging premium-quality data at industry-leading rates.

Key Features of the Licensed Professionals Data API:

1. On-Demand Credential Verification

   • Seamlessly enrich CRM systems, HR platforms, or compliance tools with verified professional licensure data.
   • Minimize manual research and accelerate due diligence cycles for faster decision-making.

2. Advanced Filtering & Query Options

   • Query the API by industry (healthcare, legal, construction), geographic location, or specific certifications.
   • Target precisely the professionals required for your projects, compliance checks, or service offerings.

3. Real-Time Validation & Reliability

   • Depend on AI-driven verification processes to ensure data integrity and relevance.
   • Make confident, informed decisions backed by accurate credentials and licensing details.

4. Scalable & Flexible Integration

   • Easily integrate the API into existing workflows, analytics platforms, or recruitment systems.
   • Adjust parameters as project scopes or regulatory conditions evolve, maintaining long-term adaptability.

Strategic Use Cases:

1. Compliance & Regulatory Assurance

   • Verify credentials in healthcare (e.g., physician licenses), legal (bar admissions), or construction (professional certifications) to ensure compliance.
   • Avoid reputational damage and legal liabilities by confirming qualifications before engagement.

2. Recruitment & Talent Acquisition

   • Identify and confirm the qualifications of candidates in regulated industries.
   • Streamline hiring processes for specialized roles, improving time-to-fill and talent quality.

3. Partner & Supplier Validation

   • Confirm that partners, vendors, or contractors meet industry standards and licensing requirements.
   • Strengthen supply chain integrity and safeguard organizational interests.

4. Market Research & Industry Analysis

   • Assess the concentration of licensed professionals in specific regions or specialties.
   • Inform product development, service offerings, or strategic expansions based on verified professional talent pools.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-tier licensed professional data at leading market rates, ensuring exceptional ROI on compliance and verification efforts.

2. Seamless Integration

   • Incorporate the API effortlessly into existing tools, reducing data silos and manual handling, thus improving productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide decisions, minimize risk, and maintain compliance in highly regulated sectors.

4. Customizable & Scalable Solutions

   • Tailor datasets to meet evolving standards, regulation changes, or business ...

Beginning with the Government Paperwork Elimination Act of 1998 (GPEA), the Federal government has encouraged the use of electronic / digital signatures to enable electronic transactions with agencies, while still providing a means for proof of user consent and non-repudiation. To support this capability, some means of reliable user identity management must exist. Currently, Veterans have to physically print, sign, and mail various documents that, in turn, need to be processed by VA. This process creates a huge inconvenience on the part of the veteran and a financial burden on VA. eSig enables veterans and their surrogates to digitally sign forms that require a high level of verification that the user signing the document is a legitimate and authorized user. In addition, eSig provides a mechanism for VA applications to verify the authenticity of user documents and data integrity on user forms. This capability is enabled by the eSig service. The eSig service signing process includes the following steps: 1. Form Signing Attestation: The user affirms their intent to electronically sign the document and understands re-authentication is part of that process. 2. Re-Authentication: The user must refresh their authentication by repeating the authentication process. 3. Form Signing: The form and the identity of the user are presented to the eSig service, where they are digitally bound and secured. 4. Form Storage: The signed form must be stored for later validation. In this process, the application is entirely responsible for steps 1, 2, and 4. In step 3, the application must use the eSig web service to request signing of the document. The following table lists the detailed functions offered by the eSig service.

The ETL (Extract, Transform, Load) testing tool market is experiencing robust growth, driven by the increasing complexity of data integration processes and the rising demand for data quality assurance. The market's expansion is fueled by several key factors, including the growing adoption of cloud-based data warehousing and the increasing need for real-time data analytics. Businesses are prioritizing data accuracy and reliability, leading to greater investments in ETL testing solutions to ensure data integrity throughout the ETL pipeline. Furthermore, the rise of big data and the increasing volume, velocity, and variety of data necessitate robust testing mechanisms to validate data transformations and identify potential errors before they impact downstream applications. The market is witnessing innovation with the emergence of AI-powered testing tools that automate testing processes and enhance efficiency, further contributing to market growth. Competition in the ETL testing tool market is intensifying, with established players like Talend and newer entrants vying for market share. The market is segmented based on deployment (cloud, on-premise), organization size (SMEs, large enterprises), and testing type (unit, integration, system). While the precise market size is not specified, a reasonable estimate, given typical growth rates in the software testing sector, would place the 2025 market value at approximately $500 million. Assuming a CAGR of 15% (a conservative estimate based on current market trends), the market could reach close to $1 billion by 2033. Restraints include the high cost of implementation and the need for specialized skills to effectively utilize these tools. However, the overall market outlook remains positive, with continuous innovation and increasing adoption expected to drive future growth.

According to Cognitive Market Research, the global file integrity monitoring market size is USD 1,056.7 million in 2024 and will expand at a compound annual growth rate (CAGR) of 14.2% from 2024 to 2031. Market Dynamics of File Integrity Monitoring Market

Key Drivers for File Integrity Monitoring Market

Introduction of Cloud-based Antivirus Software - The main factor propelling the growth of the file integrity monitoring (FIM) market is the introduction of cloud-based antivirus software. Applications, including video management systems, biometric data storage, and authentication, use cloud-based antivirus software services. Large amounts of sensitive data are stored on the cloud by banks and hospitals, thus, it's critical to protect the information from unwanted access. Cloud-based services are becoming more and more popular among SMEs because they are affordable and don't require an infrastructure to enable their installation. The rising use of cloud-based security solutions can be attributed to their scalability and flexibility in meeting the diverse needs of users. Many firms have adopted cloud-based services for business processes, including payroll, enterprise communication, and customer relationship management (CRM), in order to provide remote access to data in light of the rising mobility of their workforce. Throughout the forecast period, the worldwide file integrity monitoring market is anticipated to continue growing as a result of the growing requirement to secure data stored in the cloud, which is driving up demand for security solutions.
Rise in Global Data Thefts

Key Restraints for File Integrity Monitoring Market

Growing Numbers Of Difficulties And Security Concerns
Organizational Decisions are Heavily Influenced by Financial Constraints

Introduction of the File Integrity Monitoring Market

File integrity monitoring is a technology that uses an internal control mechanism to validate the integrity of application software and operating system (OS) files in order to monitor and identify changes in files. The most fundamental validation techniques include comparing the cryptographic checksum—also known as the file's original baseline calculations—with the checksum that represents the file's current state. Because it can scan, evaluate, and report on unexpected changes to critical files in an IT environment, like operating system (OS), database, and application software files, file integrity monitoring technology is regarded as a key component of cybersecurity processes and technology. File integrity monitoring offers a number of advantages, such as a unified security posture, a strong real-time change detection engine, and protected IT infrastructure. These and other benefits are drawing the attention of various end-user industries and propelling the growth of the global file integrity monitoring market.

Data Matrix Validator Market Outlook

The global Data Matrix Validator market size is expected to reach $3.8 billion by 2032, up from $1.5 billion in 2023, with a compound annual growth rate (CAGR) of 10.5%. The robust growth in this market is driven by an increasing need for accurate product tracking and inventory management across various industries, coupled with advancements in data matrix technology.

One of the primary growth factors of the Data Matrix Validator market is the escalating demand for traceability in supply chains. Companies across sectors such as healthcare, manufacturing, and retail are increasingly adopting data matrix technology to ensure the integrity and authenticity of their products. This capability is crucial in industries where product recalls or counterfeiting pose significant risks. Enhanced traceability solutions provided by data matrix validators help companies comply with stringent regulatory requirements, further driving their adoption. The integration of these technologies into automated systems and IoT devices is also streamlining operations, reducing human error, and enhancing overall efficiency.

Another significant driver for the market is technological advancements in data matrix validation systems. Innovations such as high-speed scanning, improved accuracy, and real-time data processing are making data matrix validators more reliable and effective. These enhancements are particularly beneficial in high-volume industries like retail and logistics, where speed and precision are paramount. Additionally, the development of cloud-based validation solutions offers greater flexibility and scalability, allowing businesses of all sizes to implement advanced tracking systems without significant upfront investment. The shift towards Industry 4.0 and smart manufacturing is further fueling the demand for sophisticated data matrix validation solutions.

The third major growth factor is the increasing adoption of these systems in emerging markets. Regions such as Asia Pacific and Latin America are witnessing rapid industrialization and urbanization, leading to a surge in demand for advanced inventory management solutions. Governments in these regions are also implementing policies to enhance product safety and traceability, which is boosting the market for data matrix validators. Moreover, the rising e-commerce sector in these regions is creating additional opportunities for market growth as businesses seek efficient ways to manage and track a growing volume of shipments.

Regionally, North America and Europe continue to dominate the Data Matrix Validator market due to the presence of a well-established industrial base and stringent regulatory frameworks. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid adoption of advanced technologies in countries like China, India, and Japan is a significant factor driving market expansion. Furthermore, increasing investments in manufacturing and logistics infrastructure, along with growing awareness about the benefits of data matrix validation, are contributing to the market's regional growth. Latin America and the Middle East & Africa are also expected to grow steadily, supported by rising industrial activities and improving economic conditions.

Component Analysis

The Data Matrix Validator market is segmented into three main components: Software, Hardware, and Services. Each of these components plays a critical role in the overall functionality and effectiveness of data matrix validation systems. The software component includes the programs and applications used to scan, decode, and verify data matrix codes. The hardware component comprises the physical devices such as scanners and sensors required to capture the data. Services include installation, maintenance, and technical support provided to ensure the systems operate efficiently.

Starting with the software component, this segment is anticipated to experience substantial growth over the forecast period. The rise in demand for customized software solutions that can integrate seamlessly with existing ERP and inventory management systems is a driving factor. Additionally, advancements in artificial intelligence and machine learning are enhancing the capabilities of data matrix validation software, making them more intelligent and capable of handling complex tasks. Cloud-based software solutions are also gaining traction, offering businesses the advantage of remote access and real-time data analytics.

In the hardware segment, the market

This repository contains the replication package for the paper "The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models" by Fernando Vallecillos Ruiz, Max Hort, and Leon Moonen, accepted for the research track of the 29th International Conference on Evaluation and Assessment in Software Engineering (EASE 2025). A preprint of the paper is included.

The source code is distributed under the MIT license, and except for 3rd party datasets that come with their own license, all documentation, data, models and results in this repository are distributed under the CC BY 4.0 license.

Repository Overview

This repository contains the necessary scripts, data, and resources to replicate the experiments presented in our conference paper. The structure of this repository has been organized to facilitate ease of use for researchers interested in reproducing our results, conducting similar analyses, or building upon our work.

Repository Structure

* Note that all contents except for the model files from the models/ folder are included in the compressed zip file in this Zenodo repository. The model files are uploaded separately to the repository to facilitate individual downloads, as several of them are relatively large (9.5-11.2GB).

Detailed Folder Descriptions

Analysis (analysis/)

This folder contains Jupyter notebook scripts used to generate tables and visual analyses of the experimental data. These scripts are designed to assist in visualizing results, comparing performance metrics, and summarizing experimental outcomes. Researchers can easily export the generated tables to spreadsheets for further processing or visualization. The outputs help in validating the experiment's consistency and provide insights into the performance of various model configurations.

Inference and Validation Source (inference_and_validation_src/)

The Python scripts in this folder are used for generating patches and validating them against predefined benchmarks. We utilize the "Fire" library to parse parameters and execute the relevant methods efficiently. This folder contains:

• Scripts for generating patches directly from the benchmark data or using iterative approaches.
• Validation utilities for Defects4J and HumanEval benchmarks to ensure the generated patches are functional and comply with benchmark requirements.

Key components include:

• Patch generation logic.
• Validation commands for HumanEval and Defects4J benchmarks.
• Utilities to verify data integrity of generated JSON files.

Training Source (train_src/)

This folder contains the scripts used for model fine-tuning:

• full_finetune.py: This script performs full fine-tuning of a model on a given training dataset. It updates all trainable parameters to achieve optimal model performance on the target task.

• lora_finetune.py: This script implements LoRA (Low-Rank Adaptation) fine-tuning. LoRA is a parameter-efficient fine-tuning approach where only a smaller subset of model parameters are updated, making it effective for resource-constrained tasks.

Inference Scripts (inference_scripts/)

These Bash scripts are designed to automate the inference process by submitting multiple iterations of inference and validation jobs to the compute cluster. The scripts create job dependencies, ensuring that all necessary tasks are completed in a logical sequence.

The available inference scripts include:

• model_inferencing_adjustable_FULL_d4j_big.sh: Executes inference for specified model configurations with multiple iterations and outputs per iteration.
• model_inferencing_adjustable_FULL_d4j_lora_big.sh: Similar to the previous script, but optimized for LoRA-based models.

These scripts accept three parameters:

• MODEL: The name of the model, as found in the models/ folder.
• NUM_ITERATIONS: The number of iterations to run.
• NUM_OUTPUTS: The number of outputs generated in each iteration.

Citation and Zenodo links

We hope this package serves as a useful resource for reproducing and expanding upon our research results. Please cite this work by referring to the published paper:

Fernando Vallecillos Ruiz, Max Hort, and Leon Moonen, 2025. The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models. In proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering (EASE 2025), ACM, 12 pages.

@inproceedings{ruiz2025:art,
  title = {{The Art of Repair: Optimizing Iterative Program Repair with 
       Instruction-Tuned Models}},
  author = {Ruiz, Fernando Vallecillos and Hort, Max and Moonen, Leon},
  booktitle = {{Proceedings of the 29th International Conference on Evaluation 
         and Assessment in Software Engineering (EASE)}},
  year = {2025},
  pages = {12},
  publisher = {{ACM}},
  language = {en}
}

The replication package is archived on Zenodo with DOI: 10.5281/zenodo.15294695.

According to Cognitive Market Research, the global Data Quality Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS of

Data Quality Software

The Emergence of Big Data and IoT drives the Market

The rise of big data analytics and Internet of Things (IoT) applications has significantly increased the volume and complexity of data that businesses need to manage. As more connected devices generate real-time data, the amount of information businesses handle grows exponentially. This surge in data requires organizations to ensure its accuracy, consistency, and relevance to prevent decision-making errors. For instance, in industries like healthcare, where real-time data from medical devices and patient monitoring systems is used for diagnostics and treatment decisions, inaccurate data can lead to critical errors. To address these challenges, organizations are increasingly investing in data quality software to manage large volumes of data from various sources. Companies like GE Healthcare use data quality software to ensure the integrity of data from connected medical devices, allowing for more accurate patient care and operational efficiency. The demand for these tools continues to rise as businesses realize the importance of maintaining clean, consistent, and reliable data for effective big data analytics and IoT applications. With the growing adoption of digital transformation strategies and the integration of advanced technologies, organizations are generating vast amounts of structured and unstructured data across various sectors. For instance, in the retail sector, companies are collecting data from customer interactions, online transactions, and social media channels. If not properly managed, this data can lead to inaccuracies, inconsistencies, and unreliable insights that can adversely affect decision-making. The proliferation of data highlights the need for robust data quality solutions to profile, cleanse, and validate data, ensuring its integrity and usability. Companies like Walmart and Amazon rely heavily on data quality software to manage vast datasets for personalized marketing, inventory management, and customer satisfaction. Without proper data management, these businesses risk making decisions based on faulty data, potentially leading to lost revenue or customer dissatisfaction. The increasing volumes of data and the need to ensure high-quality, reliable data across organizations are significant drivers behind the rising demand for data quality software, as it enables companies to stay competitive and make informed decisions.

Key Restraints to

Data Quality Software

Lack of Skilled Personnel and High Implementation Costs Hinders the market growth

The effective use of data quality software requires expertise in areas like data profiling, cleansing, standardization, and validation, as well as a deep understanding of the specific business needs and regulatory requirements. Unfortunately, many organizations struggle to find personnel with the right skill set, which limits their ability to implement and maximize the potential of these tools. For instance, in industries like finance or healthcare, where data quality is crucial for compliance and decision-making, the lack of skilled personnel can lead to inefficiencies in managing data and missed opportunities for improvement. In turn, organizations may fail to extract the full value from their data quality investments, resulting in poor data outcomes and suboptimal decision-ma...

Success.ai’s Online Search Trends Data API empowers businesses, marketers, and product teams to stay ahead by monitoring real-time online search behaviors of over 700 million users worldwide. By tapping into continuously updated, AI-validated data, you can track evolving consumer interests, pinpoint emerging keywords, and better understand buyer intent.

This intelligence allows you to refine product positioning, anticipate market shifts, and deliver hyper-relevant campaigns. Backed by our Best Price Guarantee, Success.ai’s solution provides the valuable insight needed to outpace competitors, adapt to changing market dynamics, and consistently meet consumer expectations.

Why Choose Success.ai’s Online Search Trends Data API?

1. Real-Time Global Insights

   • Leverage up-to-the-minute search data from users spanning all major industries, regions, and demographics.
   • Confidently tailor campaigns, content, and product roadmaps to match dynamic consumer interests and seasonality.

2. AI-Validated Accuracy

   • Rely on 99% data accuracy through AI-driven validation, reducing guesswork and improving conversion rates.
   • Make data-driven decisions supported by credible, continuously refreshed intelligence.

3. Continuous Data Updates

   • Stay aligned with changing market conditions, competitor moves, and evolving consumer behaviors as they happen.
   • Adapt swiftly to shifting trends, product demands, and industry developments, maintaining long-term relevance.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible data usage and brand protection.

Data Highlights:

• 700M+ Global User Insights: Access search trends, queries, and user behaviors for unparalleled audience understanding.
• Real-Time Updates: Maintain agility in content creation, product development, and marketing strategies.
• AI-Validated Accuracy: Trust in high-fidelity data to inform critical decisions, reducing wasted investments.
• Best Price Guarantee: Maximize ROI by accessing premium-quality data at unbeatable value.

Key Features of the Online Search Trends Data API:

1. On-Demand Trend Analysis

   • Query the API to identify emerging keywords, popular topics, and changing consumer priorities.
   • React rapidly to new opportunities, delivering content and offers that resonate with current market interests.

2. Advanced Filtering and Segmentation

   • Filter by region, industry vertical, time frames, or user attributes.
   • Focus on audiences and themes most relevant to your strategic goals, improving campaign performance and message relevance.

3. Real-Time Validation and Reliability

   • Benefit from AI-driven validation to ensure data integrity and accuracy.
   • Reduce risk, optimize resource allocation, and confidently direct initiatives supported by up-to-date, trustworthy data.

4. Scalable and Flexible Integration

   • Easily integrate the API into existing marketing automation platforms, analytics tools, or product management software.
   • Adjust parameters as goals evolve, ensuring long-term flexibility and alignment with strategic objectives.

Strategic Use Cases:

1. Product Development and Innovation

   • Identify rising user interests, unmet needs, or competitive gaps by analyzing search trends.
   • Shape product features, enhancements, or entirely new offerings based on verified consumer demand.

2. Content Marketing and SEO

   • Uncover trending topics, popular keywords, and seasonal interests to produce relevant content.
   • Improve organic reach, engagement, and lead generation by meeting users at the intersection of their search intent.

3. Market Entry and Expansion

   • Validate market readiness and user curiosity in new regions or niches.
   • Enter unfamiliar territories or launch product lines confidently, backed by real-time search insights.

4. Advertising and Campaign Optimization

   • Align ad creatives, messaging, and promotions with the most popular search terms.
   • Increase CTRs, conversions, and overall campaign efficiency by resonating more deeply with consumer interests.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access high-quality search trends data at the most competitive prices, ensuring exceptional ROI on data-driven initiatives.

2. Seamless Integration

   • Incorporate the API into your workflow with ease, enhancing productivity and eliminating data silos.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide strategies, refine targeting, and achieve stronger engagement outcomes.

4. Customizable and Scalable Solutions

   • Tailor datasets, filters, and time frames to your evolving market conditions, strategic ambitions, and audience needs.

Additional APIs for Enhanced Functionality:

1. Data Enrichment API
   • Combine search trends data with o...

The ckanext-cprvalidation extension for CKAN is designed to validate resources specifically for the Danish national open data platform. According to the documentation, this extension ensures that datasets adhere to specific standards. It appears to be developed for CKAN v2.6, and the documentation stresses that compatibility with other versions is not ensured. Key Features: Resource Validation: Validates resources against specific criteria, presumably related to or mandated by the Danish national open data platform. The exact validation rules are not detailed in the available documentation. Scheduled Scanning: Can be configured to scan resources at regular intervals via a CRON job, enabling automated and ongoing validation. Exception Handling: Allows adding exceptions to the database, potentially to exclude certain resources or validation errors from triggering alerts or blocking publication. Database Integration: Requires a dedicated database user ("cprvalidation") for operation, with database connection settings added to the CKAN configuration file (production.ini). Technical Integration: The extension installs as a CKAN plugin and requires activation in the CKAN configuration. It necessitates database setup, including the creation of a specific database user and corresponding credentials. The extension likely adds functionality through CKAN's plugin interface and may provide custom CLI commands for database initialization. Scheduled tasks are managed through a CRON job, external to CKAN itself, but triggered to interact with the validation logic. It's also evident that the extension makes use of additional database settings to be configured in the production.ini file. Benefits & Impact: The ckanext-cprvalidation extension ensures data quality and compliance with the standards of the Danish national open data platform. By automating validation and enabling scheduled checks, it reduces the manual effort needed to maintain data integrity, ensuring that published resources meet required standards.

The Data Migration Testing market is experiencing robust growth, driven by the increasing complexity of data environments and the rising need for ensuring data integrity during migration projects. The market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This expansion is fueled by several key factors. The surge in cloud adoption necessitates rigorous testing to ensure seamless data transfer and minimal downtime. Furthermore, stringent regulatory compliance requirements, particularly concerning data privacy (like GDPR and CCPA), are compelling organizations to invest heavily in robust data migration testing processes to mitigate risks and avoid penalties. The growing adoption of big data and advanced analytics further adds to the demand for sophisticated testing methodologies that can validate the accuracy and reliability of vast datasets post-migration. Large enterprises are leading the adoption, followed by a rapidly growing segment of small and medium-sized enterprises (SMEs) recognizing the importance of data quality and business continuity. The service segment currently dominates the market, offering expertise in various testing aspects, but the software segment is witnessing significant growth as automated solutions gain traction. Geographical expansion is another key driver. North America currently holds the largest market share, fueled by early adoption and a strong technology infrastructure. However, regions like Asia Pacific, particularly India and China, are demonstrating rapid growth due to increasing IT spending and the expansion of digital transformation initiatives. Challenges remain, including the scarcity of skilled data migration testing professionals and the complexity of integrating testing into agile development methodologies. Nonetheless, the overall market outlook remains highly positive, with continued innovation in testing tools and methodologies expected to further accelerate market expansion in the coming years. The increasing focus on data security and compliance will further solidify the demand for these services.

This EnviroAtlas dataset is a point feature class showing the locations of stream confluences, with attributes showing indices of ecological integrity in the upstream catchments and watersheds of stream confluences and the results of a cluster analysis of these indices. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

In an era where healthcare relies heavily on data-driven decision-making, the Data Validation in Healthcare market has emerged as a crucial component for ensuring the integrity and accuracy of patient information. This market encompasses a range of processes and technologies designed to verify, clean, and maintain d

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.

The Global Hotel Data dataset is an extensive collection of data providing insights into the hotel industry worldwide. This dataset encompasses diverse information, including hotel profiles, room types, amenities, pricing, occupancy rates, and guest reviews. With a size of 500k lines, the Global Hotel Data offers valuable information for hotel chains, independent hotels, travel agencies, and researchers to understand market trends, optimize pricing strategies, and enhance guest experiences globally.

Features:

1. Hotel Profiles: Data on hotel properties, including location, star ratings, facilities, and contact information, enabling analysis of hotel distribution and market positioning.
2. Room Types: Information about room categories, room sizes, bed configurations, and amenities, allowing travelers to compare room options and make informed booking decisions.
3. Amenities: Details about hotel amenities and services, such as Wi-Fi availability, parking facilities, swimming pools, and restaurants, facilitating guest preferences and satisfaction assessments.
4. Pricing: Metrics such as room rates, seasonal pricing variations, and promotional offers, enabling hoteliers and revenue managers to optimize pricing strategies and maximize revenue.
5. Occupancy Rates: Data on room occupancy rates, demand patterns, and booking lead times, allowing hoteliers to forecast demand, manage inventory, and allocate resources effectively.
6. Guest Reviews: Ratings and feedback from guests regarding their stay experience, including cleanliness, staff friendliness, and overall satisfaction, providing insights into hotel reputation and service quality.
7. Size: The dataset comprises 500 thousand lines of data.

Potential Applications:

1. Competitive Analysis: Hotel chains can analyze competitor profiles, pricing strategies, and guest reviews to benchmark their performance and identify areas for improvement and differentiation.
2. Revenue Optimization: Revenue managers can use the dataset to monitor demand trends, adjust pricing strategies dynamically, and maximize revenue through strategic pricing and distribution decisions.
3. Guest Experience Enhancement: Hoteliers can leverage guest feedback and reviews to identify service gaps, address guest concerns, and enhance overall guest satisfaction and loyalty.
4. Market Research: Travel agencies and researchers can analyze hotel data to understand market trends, traveler preferences, and destination popularity, informing product development and marketing strategies.

Usage Considerations:

1. Data Privacy: Protect guest privacy and personal information in compliance with data protection regulations, ensuring secure handling and storage of sensitive guest data while analyzing and sharing hotel data.
2. Data Quality: Validate the accuracy and reliability of hotel data sources and performance metrics to ensure data integrity and reliability for decision-making and analysis.
3. Ethical Use: Use hotel data ethically and responsibly, respecting guest consent and preferences regarding data collection, usage, and sharing in hotel analytics and research.

Disclaimer:

While the Global Hotel Data dataset provides valuable insights into the hotel industry and guest preferences, users are reminded to use the data responsibly and ethically. Hotel data analytics should be interpreted with caution, considering factors such as data biases, seasonal variations, and market dynamics, and any actions taken based on the dataset should prioritize guest satisfaction, data privacy, and regulatory compliance.

US And Europe Genetic Integrity Testing Market size was valued at USD 6,528.60 Million in 2023 and is projected to reach USD 14,760.23 Million by 2031 growing at a CAGR of 10.57% during the forecast period 2024-2031.

US And Europe Genetic Integrity Testing Executive Summary

Genetic integrity testing is a process used to assess the accuracy and completeness of genetic material within a biological sample. It involves examining the DNA and RNA of an organism to identify any changes, mutations that may have occurred. This testing is particularly important in fields such as biotechnology, and conservation biology, where maintaining the purity and authenticity of genetic material is crucial for research, breeding programs, and species conservation efforts. The primary goal of genetic integrity testing is to ensure that the genetic material being studied or manipulated remains true to its original form and has not undergone any unintended modifications or contaminations. By analyzing specific genetic sequences, scientists can detect any deviations from the expected genetic profile and take appropriate measures to address them. Genetic integrity testing methods allow researchers to compare the genetic material of different samples and identify any discrepancies that may indicate genetic drift, cross-contamination, and other sources of variation. Ultimately, genetic integrity testing plays a critical role in maintaining the accuracy and reliability of genetic data and ensuring the validity of scientific research and applications.

The demand for genetic integrity testing in humans and animals is being primarily driven by advancements in genomics and biotechnology, coupled with the increasing awareness of the importance of genetic purity and authenticity in various fields. In human medicine, genetic integrity testing is crucial for diagnosing genetic disorders, identifying disease risk factors, and guiding personalized treatment strategies. With the growing popularity of genetic testing services and the expanding availability of direct-to-consumer genetic testing kits, there is a heightened demand for accurate and reliable testing methods to ensure the integrity of genetic data and interpretation. In the realm of animal breeding and conservation, genetic integrity testing plays a vital role in maintaining the genetic diversity and health of populations. Breeders and conservationists rely on genetic testing to verify the parentage of animals, prevent inbreeding, and preserve desirable traits. Additionally, genetic integrity testing is essential for ensuring the authenticity and purity of livestock breeds, pedigree animals, and endangered species. As the importance of genetic diversity and sustainability becomes increasingly recognized, the demand for genetic integrity testing in both human and animal contexts is expected to continue to rise, driving innovation and adoption of advanced testing technologies.

McGRAW’s US B2B Data: Accurate, Reliable, and Market-Ready

Our B2B database delivers over 80 million verified contacts with 95%+ accuracy. Supported by in-house call centers, social media validation, and market research teams, we ensure that every record is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

Our B2B database is one of the most accurate and extensive datasets available, covering over 91 million business executives with a 95%+ accuracy guarantee. Designed for businesses that require the highest quality data, this database provides detailed, validated, and continuously updated information on decision-makers and industry influencers worldwide.

The B2B Database is meticulously curated to meet the needs of businesses seeking precise and actionable data. Our datasets are not only extensive but also rigorously validated and updated to ensure the highest level of accuracy and reliability.

Key Data Attributes:

• Personal Identifiers: First name, last name
• Professional Details: Title, direct dial numbers
• Business Information: Company name, address, phone number, fax number, website
• Company Metrics: Employee size, sales volume
• Technology Insights: Information on hardware and software usage across organizations
• Social Media Connections: LinkedIn, Facebook, and direct dial contacts
• Corporate Insights: Detailed company profiles

Unlike many providers that rely solely on third-party vendor files, McGRAW takes a hands-on approach to data validation. Our dedicated nearshore and offshore call centers engage directly with data before each delivery to ensure every record meets our high standards of accuracy and relevance.

In addition, our teams of social media validators, market researchers, and digital marketing specialists continuously refine and update records to maintain data freshness. Each dataset undergoes multiple verification checks using internal validation processes and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee the highest data quality.

Additional Data Solutions and Services

• Data Enhancement: Email and LinkedIn appends, contact discovery across global roles and functions

• Business Verification: Real-time validation through call centers, social media, and market research

• Technology Insights: Detailed IT infrastructure reports, spending trends, and executive insights

• Healthcare Database: Access to over 80 million healthcare professionals and industry leaders

• Global Reach: US and international GDPR-compliant datasets, complete with email, postal, and phone contacts

• Email Broadcast Services: Full-service campaign execution, from testing to live deployment, with tracking of key engagement metrics such as opens and clicks

Many B2B data providers rely on vendor-contributed files without conducting the rigorous validation necessary to ensure accuracy. This often results in outdated and unreliable data that fails to meet the demands of a fast-moving business environment.

McGRAW takes a different approach. By owning and operating dedicated call centers, we directly verify and validate our data before delivery, ensuring that every record is up-to-date and ready to drive business success.

Through continuous validation, social media verification, and real-time updates, McGRAW provides a high-quality, dependable database for businesses that prioritize data integrity and performance. Our Global Business Executives database is the ideal solution for companies that need accurate, relevant, and market-ready data to fuel their strategies.

Abstract This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Tables as taken from Appendix B to F of the - Galilee Basin: Report on the Hydrogeological Investigations, Prepared by RPS Australia PTY LTD for RLMS. PR102603-1: Rev 1 / December 2012 ## Dataset History Tables supplied in .xlsx format as taken from Appendix B to F of the - Galilee Basin: Report on the Hydrogelogical Investigations, Prepared by RPS Australia PTY LTD for RLMS. PR102603-1: Rev 1 / December 2012 Data tables included are: Table B-1 Summary of DERM registered water bores in the Galilee Basin Table C-1 Summary of the Galilee Basin exploration bores recorded in QPED Table D-1 Summary of the available Galilee Basin groundwater level data Table E-1 Galilee Basin Water Quality Summary Table Table F-1 Table of formation symbols for the geological map shown on Figure 3.1 The following is taken from the Executive Summary of the original report from which this dataset was supplied. Data sources for this report include:  Groundwater data available in the DERM GWDB;  Petroleum exploration wells recorded in Queensland Petroleum Exploration Data (QPED);  DERM groundwater data logger/tipping bucket rain gauge program;  Springs of Queensland Dataset (version 4.0) held by DERM;  PressurePlot Version 2 developed by CSIRO and linked to a Pressure-Hydrodynamics database; and  Direct communication with GBOF members. Data was sourced in January 2011. Since then there has been considerable additional drilling by GBOF members, which is not incorporated in this report. All data has been used by RPS as provided without independent investigations to validate the data. It is recognised that historical data may be subject to inaccuracies, however, as work progresses in the region, an improvement in data integrity should be realised. ## Dataset Citation RPS Australia East Pty Ltd (2012) RPS Galilee Hydrogeological Investigations - Appendix tables B to F (original). Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/bfe35a54-7a71-45ec-b20e-f3bfcc8999ef.