These data contain concentrations of major and trace elements in quality-assurance samples.These are the machine-readable versions of Tables 2–5 from the U.S. Geological Survey Scientific Investigations Report, Distribution of Mining Related Trace Elements in Streambed and Floodplain Sediment along the Middle Big River and Tributaries in the Southeast Missouri Barite District, 2012-15 (Smith and Schumacher, 2018).

This dataset provides detailed manufacturing quality control records, including batch production information, inspection results, defect types and severities, and quality scores. It enables manufacturers to monitor process performance, identify recurring issues, and drive continuous improvement in product quality and operational efficiency.

This dataset contains simulated data related to manufacturing defects observed during quality control processes. It includes information such as defect type, detection date, location within the product, severity level, inspection method used, and repair costs. This dataset can be used for analyzing defect patterns, improving quality control processes, and assessing the impact of defects on product quality and production costs. Columns: - defect_id: Unique identifier for each defect. - product_id: Identifier for the product associated with the defect. - defect_type: Type or category of the defect (e.g., cosmetic, functional, structural). - defect_description: Description of the defect. - defect_date: Date when the defect was detected. - defect_location: Location within the product where the defect was found (e.g., surface, component). - severity: Severity level of the defect (e.g., minor, moderate, critical). - inspection_method: Method used to detect the defect (e.g., visual inspection, automated testing). - repair_action: Action taken to repair or address the defect. - repair_cost: Cost incurred to repair the defect (in local currency).

Potential Uses: Quality Control Analysis: Analyze defect patterns and trends in manufacturing processes. Process Improvement: Identify areas for process optimization to reduce defect rates. Cost Analysis: Evaluate the financial impact of defects on production costs and profitability. Product Quality Assurance: Enhance product quality assurance strategies based on defect data analysis. This dataset is entirely synthetic and generated for educational and research purposes. It can be a valuable resource for manufacturing engineers, quality assurance professionals, and researchers interested in defect analysis and quality control.

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

Summary of data quality control.

This data set includes results for hormone and pharmaceutical compounds analyzed from 2012 through 2016 in laboratory quality-control samples that are associated with environmental samples collected by the National Water-Quality Assessment (NAWQA) Project during 2013 through 2015 for a study of groundwater resources used for drinking-water supply across the United States. Hormone and pharmaceutical results are provided for laboratory set blanks and reagent spikes analyzed during a time period that encompasses laboratory analysis of the environmental samples collected by NAWQA. This data release includes: Table 1. Hormone results for laboratory set blanks, December 18, 2012 through March 7, 2016. Table 2. Pharmaceutical results for laboratory set blanks, December 14, 2012 through March 4, 2016. Table 3. Hormone results for laboratory reagent spikes, June 17, 2013 through December 11, 2015. Table 4. Pharmaceutical results for laboratory reagent spikes, June 18, 2013 through October 1, 2015.

This dataset includes data quality assurance information concerning the Relative Percent Difference (RPD) of laboratory duplicates. No laboratory duplicate information exists for 2010. The formula for calculating relative percent difference is: ABS(2*[(A-B)/(A+B)]). An RPD of less the 10% is considered acceptable.

Provide Statistics on Water Quality Control

Introduction

This dataset provides insights into factors influencing defect rates in a manufacturing environment. Each record represents various metrics crucial for predicting high or low defect occurrences in production processes.

Variables Description

Production Metrics

ProductionVolume: Number of units produced per day. - Data Type: Integer. - Range: 100 to 1000 units/day.

ProductionCost: Cost incurred for production per day. - Data Type: Float. - Range: $5000 to $20000.

Supply Chain and Logistics

SupplierQuality: Quality ratings of suppliers. - Data Type: Float (%). - Range: 80% to 100%.

DeliveryDelay: Average delay in delivery. - Data Type: Integer (days). - Range: 0 to 5 days.

Quality Control and Defect Rates

DefectRate: Defects per thousand units produced. - Data Type: Float. - Range: 0.5 to 5.0 defects.

QualityScore: Overall quality assessment. - Data Type: Float (%). - Range: 60% to 100%.

Maintenance and Downtime

MaintenanceHours: Hours spent on maintenance per week. - Data Type: Integer. - Range: 0 to 24 hours.

DowntimePercentage: Percentage of production downtime. - Data Type: Float (%). - Range: 0% to 5%.

Inventory Management

InventoryTurnover: Ratio of inventory turnover. - Data Type: Float. - Range: 2 to 10.

StockoutRate: Rate of inventory stockouts. - Data Type: Float (%). - Range: 0% to 10%.

Workforce Productivity and Safety

WorkerProductivity: Productivity level of the workforce. - Data Type: Float (%). - Range: 80% to 100%.

SafetyIncidents: Number of safety incidents per month. - Data Type: Integer. - Range: 0 to 10 incidents.

Energy Consumption and Efficiency

EnergyConsumption: Energy consumed in kWh. - Data Type: Float. - Range: 1000 to 5000 kWh.

EnergyEfficiency: Efficiency factor of energy usage. - Data Type: Float. - Range: 0.1 to 0.5.

Additive Manufacturing

AdditiveProcessTime: Time taken for additive manufacturing. - Data Type: Float (hours). - Range: 1 to 10 hours.

AdditiveMaterialCost: Cost of additive materials per unit. - Data Type: Float ($). - Range: $100 to $500.

Target Variable

DefectStatus: Predicted defect status. - Data Type: Binary (0 for Low Defects, 1 for High Defects).

Defect Instances

The dataset focuses on defect instances more because they do not occur often. However, non-defect instances were added too for this reason the dataset is imbalanced, consider balancing it before proceeding with machine learning techniques.

Data Conclusion

This dataset encompasses a comprehensive collection of metrics vital for predicting defect rates in manufacturing operations. It includes production volumes, supply chain quality, quality control assessments, maintenance schedules, inventory management details, workforce productivity metrics, energy consumption patterns, additive manufacturing specifics, and more.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Quality Control for Data Annotation Software Market Outlook

According to our latest research, the market size of the global Quality Control for Data Annotation Software Market in 2024 is valued at USD 1.32 billion. The market is experiencing robust expansion, registering a CAGR of 18.7% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 6.55 billion, driven by the surging demand for high-quality annotated data to fuel artificial intelligence (AI) and machine learning (ML) applications across diverse industries. This growth is underpinned by the rising complexity of data-driven models and the critical need for accuracy in training datasets, as per our latest research findings.

The growth of the Quality Control for Data Annotation Software Market is being propelled by the exponential increase in AI and ML adoption across verticals such as healthcare, automotive, and retail. As organizations scale their AI initiatives, the integrity and reliability of labeled datasets have become mission-critical. The growing sophistication of AI algorithms necessitates not only large volumes of annotated data but also stringent quality control mechanisms to minimize errors and bias. This has led to a surge in demand for advanced quality control software that can automate the validation, verification, and correction of annotated data, ensuring that end-users can trust the outputs of their AI systems. Furthermore, the proliferation of unstructured data formats such as images, videos, and audio files is amplifying the need for robust quality control tools that can handle complex annotation tasks with high precision.

Another significant growth driver is the increasing regulatory scrutiny and ethical considerations surrounding AI deployment, particularly in sensitive sectors like healthcare and finance. Regulatory bodies are mandating higher standards for data transparency, traceability, and fairness, which in turn necessitates rigorous quality control throughout the data annotation lifecycle. Companies are now investing heavily in quality control solutions to maintain compliance, reduce risks, and safeguard their reputations. Additionally, the emergence of new data privacy laws and global standards is pushing organizations to adopt more transparent and auditable annotation workflows, further boosting market demand for quality control software tailored to these requirements.

Technological advancements are also catalyzing market expansion. Innovations such as automated error detection, AI-powered annotation validation, and real-time feedback loops are making quality control processes more efficient and scalable. These technologies enable organizations to reduce manual intervention, lower operational costs, and accelerate time-to-market for AI-driven products and services. Moreover, the integration of quality control modules into end-to-end data annotation platforms is streamlining workflows and enhancing collaboration among distributed teams. As organizations increasingly adopt cloud-based solutions, the accessibility and scalability of quality control tools are further improving, making them attractive to both large enterprises and small and medium-sized businesses alike.

From a regional perspective, North America currently dominates the global Quality Control for Data Annotation Software Market, owing to its mature AI ecosystem, strong presence of leading technology companies, and substantial investments in R&D. However, Asia Pacific is rapidly emerging as a high-growth region, fueled by the digital transformation of industries in countries like China, India, and Japan. Europe follows closely, driven by stringent data regulations and a growing focus on ethical AI. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a relatively slower pace, as organizations in these regions begin to recognize the strategic value of quality-controlled annotated data for their AI initiatives.

Component Analysis

The Quality Control for Data Annotation Software Market is broadly segmented by component into Software

Map Data Quality Assurance Market Outlook

As per our latest research, the global map data quality assurance market size reached USD 1.85 billion in 2024, driven by the surging demand for high-precision geospatial information across industries. The market is experiencing robust momentum, growing at a CAGR of 10.2% during the forecast period. By 2033, the global map data quality assurance market is forecasted to attain USD 4.85 billion, fueled by the integration of advanced spatial analytics, regulatory compliance needs, and the proliferation of location-based services. The expansion is primarily underpinned by the criticality of data accuracy for navigation, urban planning, asset management, and other geospatial applications.

One of the primary growth factors for the map data quality assurance market is the exponential rise in the adoption of location-based services and navigation solutions across various sectors. As businesses and governments increasingly rely on real-time geospatial insights for operational efficiency and strategic decision-making, the need for high-quality, reliable map data has become paramount. Furthermore, the evolution of smart cities and connected infrastructure has intensified the demand for accurate mapping data to enable seamless urban mobility, effective resource allocation, and disaster management. The proliferation of Internet of Things (IoT) devices and autonomous systems further accentuates the significance of data integrity and completeness, thereby propelling the adoption of advanced map data quality assurance solutions.

Another significant driver contributing to the market’s expansion is the growing regulatory emphasis on geospatial data accuracy and privacy. Governments and regulatory bodies worldwide are instituting stringent standards for spatial data collection, validation, and sharing to ensure public safety, environmental conservation, and efficient governance. These regulations mandate comprehensive quality assurance protocols, fostering the integration of sophisticated software and services for data validation, error detection, and correction. Additionally, the increasing complexity of spatial datasets—spanning satellite imagery, aerial surveys, and ground-based sensors—necessitates robust quality assurance frameworks to maintain data consistency and reliability across platforms and applications.

Technological advancements are also playing a pivotal role in shaping the trajectory of the map data quality assurance market. The advent of artificial intelligence (AI), machine learning, and cloud computing has revolutionized the way spatial data is processed, analyzed, and validated. AI-powered algorithms can now automate anomaly detection, spatial alignment, and feature extraction, significantly enhancing the speed and accuracy of quality assurance processes. Moreover, the emergence of cloud-based platforms has democratized access to advanced geospatial tools, enabling organizations of all sizes to implement scalable and cost-effective data quality solutions. These technological innovations are expected to further accelerate market growth, opening new avenues for product development and service delivery.

From a regional perspective, North America currently dominates the map data quality assurance market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region’s early adoption of advanced geospatial technologies, strong regulatory frameworks, and the presence of leading industry players. However, the Asia Pacific region is poised to witness the fastest growth over the forecast period, propelled by rapid urbanization, infrastructure development, and increased investments in smart city projects. Europe also maintains a significant market presence, driven by robust government initiatives for environmental monitoring and urban planning. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing digitalization and expanding geospatial applications in transportation, utilities, and resource management.

The advent of large-scale cabled ocean observatories brought about the need to handle large amounts of ocean-based data, continuously recorded at a high sampling rate over many years and made accessible in near-real time to the ocean science community and the public. Ocean Networks Canada (ONC) commenced installing and operating two regional cabled observatories on Canada’s Pacific Coast, VENUS inshore and NEPTUNE offshore in the 2000s, and later expanded to include observatories in the Atlantic and Arctic in the 2010s. The first data streams from the cabled instrument nodes started flowing in February 2006. This paper describes Oceans 2.0 and Oceans 3.0, the comprehensive Data Management and Archival System that ONC developed to capture all data and associated metadata into an ever-expanding dynamic database. Oceans 2.0 was the name for this software system from 2006–2021; in 2022, ONC revised this name to Oceans 3.0, reflecting the system’s many new and planned capabilities aligning with Web 3.0 concepts. Oceans 3.0 comprises both tools to manage the data acquisition and archival of all instrumental assets managed by ONC as well as end-user tools to discover, process, visualize and download the data. Oceans 3.0 rests upon ten foundational pillars: (1) A robust and stable system architecture to serve as the backbone within a context of constant technological progress and evolving needs of the operators and end users; (2) a data acquisition and archival framework for infrastructure management and data recording, including instrument drivers and parsers to capture all data and observatory actions, alongside task management options and support for data versioning; (3) a metadata system tracking all the details necessary to archive Findable, Accessible, Interoperable and Reproducible (FAIR) data from all scientific and non-scientific sensors; (4) a data Quality Assurance and Quality Control lifecycle with a consistent workflow and automated testing to detect instrument, data and network issues; (5) a data product pipeline ensuring the data are served in a wide variety of standard formats; (6) data discovery and access tools, both generalized and use-specific, allowing users to find and access data of interest; (7) an Application Programming Interface that enables scripted data discovery and access; (8) capabilities for customized and interactive data handling such as annotating videos or ingesting individual campaign-based data sets; (9) a system for generating persistent data identifiers and data citations, which supports interoperability with external data repositories; (10) capabilities to automatically detect and react to emergent events such as earthquakes. With a growing database and advancing technological capabilities, Oceans 3.0 is evolving toward a future in which the old paradigm of downloading packaged data files transitions to the new paradigm of cloud-based environments for data discovery, processing, analysis, and exchange.

ZIP archive with FastQC-generated quality reports for the short-read libraries used in the assembly and annotation

The size of the Data Quality Management Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

The size of the Data Quality Management Service market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 3 example notebooks and associated data files.

Notebooks: 1. Example 1: Import and plot data 2. Example 2: Perform rules-based quality control 3. Example 3: Perform model-based quality control (ARIMA)

Data files: Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are: - temp: water temperature, degrees C - cond: specific conductance, μS/cm - ph: pH, standard units - do: dissolved oxygen, mg/L - turb: turbidity, NTU - stage: stage height, cm

For each variable, there are 3 columns: - Raw data value measured by the sensor (column header is the variable abbreviation). - Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor'). - Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

This dataset includes laboratory instrument detection limit data associated with laboratory instruments used in the analysis of surface water samples collected as part of the USGS - Yukon River Inter-Tribal Watershed Council collaborative water quality monitoring project.

Dataset

This dataset was created by JoanBotzev

Contents

V2X Data Quality Assurance Market Outlook

According to our latest research, the global V2X Data Quality Assurance market size reached USD 1.42 billion in 2024, reflecting robust growth driven by the increasing adoption of connected vehicle technologies and regulatory mandates for vehicular safety. The market is projected to expand at a remarkable CAGR of 16.8% from 2025 to 2033, reaching a forecasted value of USD 6.09 billion by 2033. This expansion is primarily fueled by the integration of advanced communication systems in vehicles, rising demand for real-time data validation, and the proliferation of smart transportation infrastructure. As per our latest research, the V2X Data Quality Assurance industry is experiencing heightened investment in both hardware and software solutions, underscoring its critical role in enabling safe and efficient vehicle-to-everything (V2X) communication ecosystems.

The growth of the V2X Data Quality Assurance market is underpinned by the rapid digital transformation within the automotive and transportation sectors. As vehicles become increasingly connected and autonomous, the volume and complexity of data exchanged between vehicles, infrastructure, and other entities are soaring. Ensuring the integrity, accuracy, and reliability of this data is crucial for the successful deployment of V2X systems, as any compromise in data quality can have significant safety and operational implications. This demand for robust data quality assurance frameworks is further amplified by the emergence of new mobility paradigms, such as shared mobility and autonomous fleets, which rely heavily on seamless and trustworthy data exchange. Consequently, automotive OEMs, fleet operators, and government agencies are investing heavily in advanced data quality assurance solutions to support the next generation of intelligent transportation systems.

Another pivotal growth factor for the V2X Data Quality Assurance market is the increasing regulatory focus on road safety and emission control. Governments across North America, Europe, and Asia Pacific are implementing stringent regulations that mandate the adoption of V2X technologies as part of broader smart city initiatives. These regulations not only drive the deployment of V2X-enabled vehicles and infrastructure but also necessitate rigorous data validation processes to ensure compliance with safety and performance standards. Furthermore, the growing emphasis on cybersecurity within the automotive ecosystem is compelling stakeholders to prioritize data quality assurance as a means of mitigating risks associated with data breaches and system failures. As a result, the market is witnessing a surge in demand for integrated solutions that combine data quality management with real-time monitoring and analytics capabilities.

Technological advancements are also playing a significant role in shaping the trajectory of the V2X Data Quality Assurance market. The advent of 5G connectivity, edge computing, and artificial intelligence is enabling more sophisticated data validation and anomaly detection mechanisms, thereby enhancing the overall reliability of V2X communications. These innovations are not only improving the scalability and efficiency of data quality assurance processes but also opening up new opportunities for solution providers to differentiate their offerings. Moreover, the increasing collaboration between automotive OEMs, technology vendors, and infrastructure providers is fostering the development of standardized protocols and interoperable platforms, which are essential for ensuring consistent data quality across diverse V2X ecosystems. This collaborative approach is expected to accelerate the adoption of V2X data quality assurance solutions and drive sustained market growth over the forecast period.

From a regional perspective, the V2X Data Quality Assurance market is witnessing significant traction in Asia Pacific, North America, and Europe, with each region exhibiting unique growth drivers and adoption trends. Asia Pacific, led by China, Japan, and South Korea, is emerging as the fastest-growing market, propelled by large-scale investments in smart transportation infrastructure and the rapid deployment of connected vehicles. North America remains a key market, driven by robust regulatory support, high levels of R&D activity, and the presence of leading automotive and technology companies. Europe, on the other hand, is characterized by strong government initiatives aimed at enhancing road safety and reducing emissions, which a

In this paper, we show that concept of Statistical Process Control tools was thoroughly examined and the definitions of quality control concepts were presented. This is significant because of it is anticipated that this study will contribute to the literature as an exemplary application that demonstrates the role of statistical process control (SPC) tools in quality improvement in the evaluation and decision-making phase.

This is significant because of this study is to investigate applications of quality control, to clarify statistical control methods and problem-solving procedures, to generate proposals for problem-solving approaches, and to disseminate improvement studies in the ready-to-wear industry. The basic Statistical Process Control tools used in the study, the most repetitive faults were detected and these faults were divided into sub-headings for more detailed analysis. In this way, it was tried to prevent the repetition of faults by going down to the root causes of any detected fault. With this different perspective, it is expected that the study will contribute to other fields.

We give consent for the publication of identifiable details, which can include photograph(s) and case history and details within the text (“Material”) to be published in the Journal of Quality Technology. We confirm that have seen and been given the opportunity to read both the Material and the Article (as attached) to be published by Taylor & Francis.