The Cloud Data Quality Monitoring and Testing market is experiencing robust growth, driven by the increasing reliance on cloud-based data storage and processing, the burgeoning volume of big data, and the stringent regulatory compliance requirements across various industries. The market's expansion is fueled by the need for real-time data quality assurance, proactive identification of data anomalies, and improved data governance. Businesses are increasingly adopting cloud-based solutions to enhance operational efficiency, reduce infrastructure costs, and improve scalability. This shift is particularly evident in large enterprises, which are investing heavily in advanced data quality management tools to support their complex data landscapes. The growth of SMEs adopting cloud-based solutions also contributes significantly to market expansion. While on-premises solutions still hold a market share, the cloud-based segment is demonstrating a significantly higher growth rate, projected to dominate the market within the forecast period (2025-2033). Despite the positive market outlook, certain challenges hinder growth. These include concerns regarding data security and privacy in cloud environments, the complexity of integrating data quality tools with existing IT infrastructure, and the lack of skilled professionals proficient in cloud data quality management. However, advancements in AI and machine learning are mitigating these challenges, enabling automated data quality checks and anomaly detection, thus streamlining the process and reducing the reliance on manual intervention. The market is segmented geographically, with North America and Europe currently holding significant market shares due to early adoption of cloud technologies and robust regulatory frameworks. However, the Asia Pacific region is projected to experience substantial growth in the coming years due to increasing digitalization and expanding cloud infrastructure investments. This competitive landscape with established players and emerging innovative companies is further shaping the market's evolution and expansion.

Have you ever assessed the quality of your data? Just as you would run spell check before publishing an important document, it is also beneficial to perform a quality control (QC) review before delivering data or map products. This course gives you the opportunity to learn how you can use ArcGIS Data Reviewer to manage and automate the quality control review process. While exploring the fundamental concepts of QC, you will gain hands-on experience configuring and running automated data checks. You will also practice organizing data review and building a comprehensive quality control model. You can easily modify and reuse this QC model over time as your organizational requirements change.After completing this course, you will be able to:Explain the importance of data quality.Select data checks to find specific errors.Apply a workflow to run individual data checks.Build a batch job to run cumulative data checks.

The ETL (Extract, Transform, Load) automation testing market is experiencing robust growth, driven by the increasing complexity of data integration processes and the rising demand for faster, more reliable data pipelines. Businesses across all sectors are adopting cloud-based solutions and big data analytics, fueling the need for automated testing to ensure data quality and integrity. The market's expansion is further propelled by the need to reduce manual testing efforts, accelerate deployment cycles, and minimize the risk of data errors. Considering the current market dynamics and a conservative estimate based on similar technology adoption curves, let's assume a 2025 market size of $2.5 billion USD and a compound annual growth rate (CAGR) of 15% through 2033. This suggests a significant expansion in the coming years, reaching approximately $7 billion USD by 2033. The software segment currently dominates, but the services segment is expected to show strong growth due to the increasing demand for specialized expertise in ETL testing methodologies and tool implementation. Large enterprises are leading the adoption, but SMEs are increasingly adopting automation to streamline their data processes and improve operational efficiency. The key players mentioned demonstrate the competitive landscape, highlighting the presence of both established software vendors and specialized service providers. Geographic distribution shows a concentration of market share in North America and Europe initially, but significant growth is anticipated in Asia-Pacific regions, particularly in India and China, driven by their expanding digital economies and increasing data volumes. Challenges remain in terms of the initial investment required for implementing ETL automation testing solutions and the need for skilled personnel. However, the long-term benefits of improved data quality, reduced costs, and accelerated delivery outweigh these initial hurdles, ensuring continued market expansion.

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.

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Automated Industrial Quality Control (Qc) Market Size 2024-2028

The automated industrial quality control (qc) market size is forecast to increase by USD 269.5 million at a CAGR of 5.97% between 2023 and 2028.

The market is experiencing significant growth due to the increasing importance of accuracy in manufacturing processes. As industries prioritize error-free production, automated QC systems have become essential for ensuring product consistency and reducing human error. Another trend driving market growth is the reshoring of manufacturing industries, which has led to a renewed focus on domestic production and the adoption of advanced technologies to maintain quality standards. However, challenges persist, including the lack of effective interoperability between different QC systems and the high cost of implementation. To address these challenges, market participants are investing in developing open standards and collaborating to create interoperable solutions.Overall, the automated industrial QC market is poised for continued growth as industries seek to improve production efficiency and maintain high-quality standards.

What will be the Size of the Automated Industrial Quality Control (Qc) Market During the Forecast Period?

Request Free SampleThe market encompasses the deployment of digital technology, including machine learning, physics-based modeling, and augmented reality (AR), to enhance industrial processes and ensure product consistency. This market is experiencing significant growth, driven by the increasing adoption of industrial automation systems and the integration of advanced sensors and SCADA (Supervisory Control and Data Acquisition) systems with HMIs (Human-Machine Interfaces). Cloud computing plays a pivotal role in facilitating remote monitoring and real-time data analysis, further bolstering market expansion. Beyond traditional manufacturing industries, the automated QC market is gaining traction in sectors such as in vitro diagnostics, immunochemistry, molecular diagnostics, and healthcare, including hospitals and home care.Applications span various industries, including HIV, infectious illnesses, autoimmune disorders, chronic diseases, and cancer. The market's trajectory is marked by the development of advanced quality control products, industrial control systems, automation devices, and cloud-based services, all aimed at improving efficiency, accuracy, and overall product quality.

How is this Automated Industrial Quality Control (Qc) Industry segmented and which is the largest segment?

The automated industrial quality control (qc) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. End-userAutomotive industryMetal industryElectronics industryOthersSolutionHardware and softwareServiceGeographyEuropeGermanyFranceNorth AmericaUSAPACChinaJapanSouth AmericaMiddle East and Africa

By End-user Insights

The automotive industry segment is estimated to witness significant growth during the forecast period. Automated industrial quality control systems play a crucial role In the manufacturing sector, particularly In the automotive industry, where continuous production and high-quality standards are essential. These systems optimize the performance of assembly and material handling equipment, conveyor systems, industrial robots, and welding equipment. By implementing machine learning algorithms, physics-based modeling, and augmented reality technologies, manufacturers can enhance production rates, minimize errors, and ensure consistent product quality. Cloud computing and SCADA (Supervisory Control and Data Acquisition) systems facilitate remote supervision and data management, enabling real-time monitoring and analysis. Industrial sensors and digital technology further integrate with these systems to provide advanced quality control solutions.The defense industry, pharmaceuticals, oil and gas, and electrical power sectors also benefit from automated industrial quality control systems, which support digital transformation and improve overall efficiency. Key applications include in vitro diagnostics, immunochemistry, molecular diagnostics, clinical chemistry, hematology, coagulation and hemostasis, microbiology, and various chronic diseases and cancer diagnosis. Automated industrial quality control systems contribute to reducing the burden of diseases and improving the availability of fast diagnosis systems, ultimately leading to better patient outcomes.

Get a glance at the market report of various segments Request Free Sample

The Automotive industry segment was valued at USD 247.60 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

Europe is estimated to contribute 37% to the g

The global Automated Industrial Quality Control market was valued at USD 631.6 million in 2025 and is projected to grow at a CAGR of 5.5% from 2025 to 2033, reaching a market value of USD 877.3 million by 2033. The increasing adoption of Industry 4.0 and Industrial IoT (IIoT) solutions, the growing demand for automated quality control processes in various industries, the need to reduce production costs and improve product quality are driving the market. Segment-wise, the hardware and software solutions segment held the largest market share in 2025 and is expected to continue its dominance throughout the forecast period. The growth of this segment can be attributed to the increasing adoption of automated inspection systems, machine vision systems, and data analytics software solutions for quality control processes. The automotive industry is projected to be the largest application segment during the forecast period due to the growing demand for automated quality control solutions to ensure the reliability, safety, and performance of vehicles. Key players in the Automated Industrial Quality Control market include GOM, Honeywell, IVISYS, KEYENCE, Renishaw, ABB, ATS Automation Tooling Systems, MasterControl, Nanotronics, RNA Automation, Shelton Machines, and others. The market is fragmented, with numerous established players and emerging startups offering various solutions for automated industrial quality control.

The Data Warehouse Testing market is experiencing robust growth, driven by the increasing adoption of cloud-based data warehousing solutions and the rising demand for ensuring data accuracy and reliability across large enterprises and SMEs. The market's expansion is fueled by several key factors, including the growing complexity of data warehouses, stringent regulatory compliance requirements demanding rigorous testing, and the need to minimize the risk of costly data breaches. The shift towards agile and DevOps methodologies in software development also necessitates efficient and automated data warehouse testing processes. While the on-premise segment currently holds a larger market share, the cloud-based segment is projected to exhibit faster growth due to its scalability, cost-effectiveness, and ease of deployment. Key players in this competitive landscape are continuously innovating to offer comprehensive testing solutions encompassing various methodologies, including ETL testing, data quality testing, and performance testing. The North American market currently dominates due to high technological adoption and stringent data governance regulations, but significant growth potential exists in regions like Asia-Pacific, driven by increasing digitalization and expanding data centers. The forecast period (2025-2033) anticipates sustained expansion, with a projected Compound Annual Growth Rate (CAGR) of approximately 15%, indicating a significant market opportunity. However, challenges remain, including the scarcity of skilled data warehouse testing professionals and the complexity of integrating testing into existing data pipelines. Nevertheless, the increasing focus on data-driven decision-making and the growing volume of data being generated across various industries are expected to propel market growth. Strategic partnerships and mergers and acquisitions are expected amongst vendors aiming to enhance their capabilities and expand their market reach. Segmentation by enterprise size and deployment model allows for tailored solutions and market penetration strategies.

The ETL Testing Services market is experiencing robust growth, driven by the increasing adoption of cloud-based data warehousing and the expanding volume of big data requiring rigorous validation. The market's Compound Annual Growth Rate (CAGR) is estimated to be around 15% from 2025 to 2033, indicating a significant expansion opportunity. Key drivers include the rising demand for data quality and accuracy, stringent regulatory compliance requirements necessitating thorough testing, and the need for efficient data integration across diverse systems. Furthermore, the shift towards agile and DevOps methodologies necessitates faster and more reliable ETL testing processes, fueling market growth. While the market faces certain restraints, such as the complexity of ETL processes and the scarcity of skilled professionals, these challenges are being addressed through the development of automated testing tools and specialized training programs. The segmentation of the market likely includes services based on testing methodologies (e.g., unit, integration, system), deployment models (cloud, on-premise), industry verticals (finance, healthcare, retail), and geographic regions. The competitive landscape comprises a mix of large established players like Accenture and Infosys, along with specialized ETL testing firms like QuerySurge and niche providers. This diverse landscape offers clients a range of choices based on their specific needs and budget. The substantial market size, projected to be around $5 billion in 2025, signifies considerable investment and growth potential. Leading vendors continually enhance their offerings, incorporating Artificial Intelligence (AI) and Machine Learning (ML) to improve test automation and efficiency. This innovation cycle will further accelerate the market's growth, particularly in areas needing high-throughput data processing and real-time analytics. The market's regional distribution is likely skewed towards North America and Europe initially due to higher adoption rates of advanced data technologies, but other regions such as Asia-Pacific are expected to witness rapid growth in the forecast period due to increasing digitalization efforts. The overall outlook for the ETL testing services market remains strongly positive, driven by the ongoing expansion of data-driven businesses and the rising importance of data quality assurance.

In urban areas, dense atmospheric observational networks with high-quality data are still a challenge due to high costs for installation and maintenance over time. Citizen weather stations (CWS) could be one answer to that issue. Since more and more owners of CWS share their measurement data publicly, crowdsourcing, i.e., the automated collection of large amounts of data from an undefined crowd of citizens, opens new pathways for atmospheric research. However, the most critical issue is found to be the quality of data from such networks. In this study, a statistically-based quality control (QC) is developed to identify suspicious air temperature (T) measurements from crowdsourced data sets. The newly developed QC exploits the combined knowledge of the dense network of CWS to statistically identify implausible measurements, independent of external reference data. The evaluation of the QC is performed using data from Netatmo CWS in Toulouse, France, and Berlin, Germany, over a 1-year period (July 2016 to June 2017), comparing the quality-controlled data with data from two networks of reference stations. The new QC efficiently identifies erroneous data due to solar exposition and siting issues, which are common error sources of CWS. Estimation of T is improved when averaging data from a group of stations within a restricted area rather than relying on data of individual CWS. However, a positive deviation in CWS data compared to reference data is identified, particularly for daily minimum T. To illustrate the transferability of the newly developed QC and the applicability of CWS data, a mapping of T is performed over the city of Paris, France, where spatial density of CWS is especially high.

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 Patient Record Quality Control market is experiencing robust growth, driven by increasing healthcare data volumes, stringent regulatory compliance mandates (like HIPAA and GDPR), and the rising adoption of electronic health records (EHRs). The market's complexity necessitates sophisticated quality control measures to ensure data accuracy, completeness, and consistency for effective patient care and research. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors, including the increasing prevalence of chronic diseases necessitating detailed and accurate medical records, the growing focus on improving healthcare operational efficiency, and the expanding use of data analytics in healthcare for predictive modeling and improved patient outcomes. The inpatient medical record quality control segment currently holds a significant market share, owing to the higher volume of data generated in inpatient settings. However, the outpatient segment is projected to witness faster growth due to the increasing adoption of telehealth and remote patient monitoring, resulting in a substantial increase in electronically generated outpatient records. Hospitals currently dominate the application segment, but clinics are witnessing rapid adoption of advanced quality control solutions. Leading companies like Huimei, BaseBit, Lantone, and Goodwill are actively investing in research and development to enhance their offerings and cater to the growing demand for advanced data quality control features, such as automated error detection, intelligent data validation, and real-time data monitoring. Geographic expansion, particularly in emerging markets of Asia-Pacific and Latin America, presents significant growth opportunities for market players. Despite the positive outlook, challenges like high initial investment costs associated with implementing advanced quality control systems and the need for skilled personnel to manage these systems pose potential restraints to market growth. Future advancements in artificial intelligence (AI) and machine learning (ML) are expected to further automate quality control processes, streamlining workflows and reducing errors, thereby further boosting market expansion.

Test Data Generation Tools Market Outlook

The global market size for Test Data Generation Tools was valued at USD 800 million in 2023 and is projected to reach USD 2.2 billion by 2032, growing at a CAGR of 12.1% during the forecast period. The surge in the adoption of agile and DevOps practices, along with the increasing complexity of software applications, is driving the growth of this market.

One of the primary growth factors for the Test Data Generation Tools market is the increasing need for high-quality test data in software development. As businesses shift towards more agile and DevOps methodologies, the demand for automated and efficient test data generation solutions has surged. These tools help in reducing the time required for test data creation, thereby accelerating the overall software development lifecycle. Additionally, the rise in digital transformation across various industries has necessitated the need for robust testing frameworks, further propelling the market growth.

The proliferation of big data and the growing emphasis on data privacy and security are also significant contributors to market expansion. With the introduction of stringent regulations like GDPR and CCPA, organizations are compelled to ensure that their test data is compliant with these laws. Test Data Generation Tools that offer features like data masking and data subsetting are increasingly being adopted to address these compliance requirements. Furthermore, the increasing instances of data breaches have underscored the importance of using synthetic data for testing purposes, thereby driving the demand for these tools.

Another critical growth factor is the technological advancements in artificial intelligence and machine learning. These technologies have revolutionized the field of test data generation by enabling the creation of more realistic and comprehensive test data sets. Machine learning algorithms can analyze large datasets to generate synthetic data that closely mimics real-world data, thus enhancing the effectiveness of software testing. This aspect has made AI and ML-powered test data generation tools highly sought after in the market.

Regional outlook for the Test Data Generation Tools market shows promising growth across various regions. North America is expected to hold the largest market share due to the early adoption of advanced technologies and the presence of major software companies. Europe is also anticipated to witness significant growth owing to strict regulatory requirements and increased focus on data security. The Asia Pacific region is projected to grow at the highest CAGR, driven by rapid industrialization and the growing IT sector in countries like India and China.

Synthetic Data Generation has emerged as a pivotal component in the realm of test data generation tools. This process involves creating artificial data that closely resembles real-world data, without compromising on privacy or security. The ability to generate synthetic data is particularly beneficial in scenarios where access to real data is restricted due to privacy concerns or regulatory constraints. By leveraging synthetic data, organizations can perform comprehensive testing without the risk of exposing sensitive information. This not only ensures compliance with data protection regulations but also enhances the overall quality and reliability of software applications. As the demand for privacy-compliant testing solutions grows, synthetic data generation is becoming an indispensable tool in the software development lifecycle.

Component Analysis

The Test Data Generation Tools market is segmented into software and services. The software segment is expected to dominate the market throughout the forecast period. This dominance can be attributed to the increasing adoption of automated testing tools and the growing need for robust test data management solutions. Software tools offer a wide range of functionalities, including data profiling, data masking, and data subsetting, which are essential for effective software testing. The continuous advancements in software capabilities also contribute to the growth of this segment.

In contrast, the services segment, although smaller in market share, is expected to grow at a substantial rate. Services include consulting, implementation, and support services, which are crucial for the successful deployment and management of test data generation tools. The increasing complexity of IT inf

The global automated industrial quality control market, valued at $653.8 million in 2025, is projected to experience robust growth, driven by increasing automation in manufacturing, rising demand for improved product quality, and stringent regulatory compliance requirements across various industries. The 5.8% CAGR from 2019-2033 signifies a substantial expansion, particularly fueled by the automotive, electronics, and metal industries' adoption of advanced quality control solutions. Hardware solutions currently dominate the market share, but software and service segments are experiencing accelerated growth, driven by the need for data-driven insights and predictive maintenance capabilities. This shift towards integrated, intelligent quality control systems allows for real-time monitoring, faster defect detection, and reduced production downtime, ultimately boosting efficiency and profitability for manufacturers. Growth is expected to be geographically diverse, with North America and Europe maintaining significant market shares due to established industrial bases and technological advancements. However, the Asia-Pacific region is poised for rapid expansion, driven by increasing manufacturing activities in countries like China and India. While the initial investment in automated quality control systems can be substantial, the long-term return on investment (ROI) is compelling, justifying the adoption even among smaller businesses. The market's continued evolution will likely involve further integration with AI and machine learning technologies, leading to more sophisticated and proactive quality control processes. Competition among established players like GOM, Honeywell, and Keyence, alongside innovative entrants, will further drive market dynamics and technological innovation, ensuring continued advancements in automated industrial quality control.

The Florida State University Center for Ocean-Atmospheric Predictions Studies (COAPS) has been operating a data assembly center (DAC) to collect, quality evaluate, and distribute Shipboard Automated Meteorological and Oceanographic System (SAMOS) observations since 2005. A SAMOS is typically a computerized data logging system that records underway meteorological and near-surface oceanographic observations collected on research vessels. The SAMOS initiative does not provide specific instrumentation for vessels, but instead takes advantage of science quality instrumentation already deployed on research vessels and select merchant ships. The SAMOS initiative provides vessel operators with desired sampling protocols and metadata requirements that will ensure the DAC receives a consistent series of observations from each vessel. The DAC and its partners in U. S. National Oceanic and Atmospheric Administration (NOAA), the University National Oceanographic Laboratory System, the U. S. Coast Guard, and the U. S. Antarctic Program have implemented a series of daily data transmissions from ship-to-shore using an email protocol. A set of observations recorded at one-minute intervals for the previous day arrive at the DAC soon after 0000 UTC and undergo automated quality evaluation. A trained data analyst reviews data and responds directly to vessels at sea when problems are identified. A secondary level of visual quality control is completed after all data from a single ship and day are merged into a common daily file (allowing for delayed data receipts). All quality-evaluated data are freely available to the user community and are distributed to national archive centers. This dataset contains all of these data.

MRI is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large data sets for potential poor quality outliers can be a challenge. We present AIDAqc, a machine learning-assisted automated Python-based command-line tool for the quality assessment of small animal MRI data. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection is based on the combination of interquartile range and the machine learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. AIDAqc was challenged in a large heterogeneous dataset collected from 18 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater variability (mean Fleiss Kappa score 0.17) is high when identifying poor quality data. A direct comparison of AIDAqc results therefore showed only low to moderate concordance. In a manual post-hoc validation of AIDAqc output, agreement was high (>70%). The outlier data can have a significant impact on further post-processing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility.

Industrial Screw Driving Datasets

Overview

This repository contains a collection of real-world industrial screw driving datasets, designed to support research in manufacturing process monitoring, anomaly detection, and quality control. Each dataset represents different aspects and challenges of automated screw driving operations, with a focus on natural process variations and degradation patterns.

Dataset Collection

The datasets were collected from operational industrial environments, specifically from automated screw driving stations used in manufacturing. Each scenario investigates specific mechanical phenomena that can occur during industrial screw driving operations:

Currently Available Datasets:

1. S01_thread-degradation

• Focus: Investigation of thread degradation through repeated fastening
• Samples: 5,000 screw operations (4,089 normal, 911 faulty)
• Features: Natural degradation patterns, no artificial error induction
• Equipment: Delta PT 40x12 screws, thermoplastic components
• Process: 25 cycles per location, two locations per workpiece
• First published in: HICSS 2024 (West & Deuse, 2024)

2. S02_surface-friction

• Focus: Surface friction effects on screw driving operations
• Samples: 12,500 screw operations (9,512 normal, 2,988 faulty)
• Features: Eight distinct surface conditions (baseline to mechanical damage)
• Equipment: Delta PT 40x12 screws, thermoplastic components, surface treatment materials
• Process: 25 cycles per location, two locations per workpiece
• First published in: CIE51 2024 (West & Deuse, 2024) [DOI will be added after publication]

Upcoming Datasets:

3. S03_screw-error-collection-1 (recorded but unpublished)

• Focus: Varius manipulations of the screw driving process
• Features: More than 20 different errors recorded
• First published in: Publication planned
• Status: In preparation

4. S04_screw-error-collection-2 (recorded but unpublished)

• Focus: Varius manipulations of the screw driving process
• Features: 25 distinct errors recorded over the course of a week
• First published in: Publication planned
• Status: In preparation

5. S05_upper-workpiece-manipulations (recorded but unpublished)

• Manipulations of the injection molding process with no changes during tightening

6. S06_lower-workpiece-manipulations (recorded but unpublished)

• Manipulations of the injection molding process with no changes during tightening

Additional scenarios may be added to this collection as they become available.

Data Format

Each dataset follows a standardized structure:

• JSON files containing individual screw operation data
• CSV files with operation metadata and labels
• Comprehensive documentation in README files
• Example code for data loading and processing is available in the companion library PyScrew

Research Applications

These datasets are suitable for various research purposes:

• Machine learning model development and validation
• Process monitoring and control systems
• Quality assurance methodology development
• Manufacturing analytics research
• Anomaly detection algorithm benchmarking

Usage Notes

• All datasets include both normal operations and natural process anomalies
• Complete time series data for torque, angle, and additional parameters available
• Detailed documentation of experimental conditions and setup
• Data collection procedures and equipment specifications available

Access and Citation

These datasets are provided under an open-access license to support research and development in manufacturing analytics. When using any of these datasets, please cite the corresponding publication as detailed in each dataset's README file.

Related Tools

We recommend using our library PyScrew to load and prepare the data. However, the the datasets can be processed using standard JSON and CSV processing libraries. Common data analysis and machine learning frameworks may be used for the analysis. The .tar file provided all information required for each scenario.

Documentation

Each dataset includes:

• Detailed README files
• Data format specifications
• Equipment and process parameters
• Experimental setup documentation
• Citation information

Contact and Support

For questions, issues, or collaboration interests regarding these datasets, please:

1. Open an issue in the respective GitHub repository
2. Contact the authors through the provided institutional channels

Acknowledgments

These datasets were collected and prepared from:

• RIF Institute for Research and Transfer e.V.
• Technical University Dortmund, Institute for Production Systems

The research was supported by:

• German Ministry of Education and Research (BMBF)
• European Union's "NextGenerationEU" program
• The research is part of this funding program
• More information regarding the research project is available here

Developing software test code can be as or more expensive than developing software production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code quality. Nonetheless, unit tests usually do not have a quality check layer above testing code, which might be hard to guarantee the quality of the generated tests. An emerging strategy to verify the tests quality is to analyze the presence of test smells in software test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. The presence of test smells in unit test code could be used as an indicator of unit test quality. In this paper, we present an empirical study aimed to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and EvoSuite) with the existing unit test suite of open-source software projects. We analyze the unit test code of twenty-one open-source Java projects and detected the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from both automated unit test generators and existing unit test suites.

Hard QA/QC flags used in National Data Buoy Center, from NDBC, 2003: NDBC Technical Document 03-02, Handbook of Automated Data Quality Control Checks and Procedures of the National Data Buoy Center

NOAA Ship Oscar Dyson Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z." in your query. '=~' indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '' says to match the previous character 0 or more times. (Don't include backslashes in your query.) See the tutorial for regular expressions at https://www.vogella.com/tutorials/JavaRegularExpressions/article.html

The near real-time data presented here is intended to provide a 'snapshot' of current conditions within Narragansett Bay and has been subjected to automated QC pipelines. QA of data is performed following manufacturer guidelines for calibration and servicing of each sensor. QC'd datasets that have undergone additional manual inspection by researchers is provided in a 3 month lagging interval. Following the publication of human QC'd data, automated QC'd data from the previous 3 month window will be removed. See the 'Buoy Telemetry: Manually Quality Controlled' dataset for the full quality controlled dataset.The Automated QC of measurements collected from buoy platforms is performed following guidelines established by the Ocean Observatories Initiative (https://oceanobservatories.org/quality-control/) and implemented in R. Spike Test: To identify spikes within collected measurements, data points are assessed for deviation against a 'reference' window of measurement generated in a sliding window (k=7) . If a data point exceeds the deviation threshold (N=2), the spike is replaced with the 'reference' data point, which is determined using a median smoothing approach in the R package 'oce'. Despiked data is then written into the instrument table as 'Measurement_Despike'. Global Range Test: Data points are checked against the maximum and minimum measurements using a dataset of global measurements provided by IOOC (https://github.com/oceanobservatories/qc-lookup). QC Flags from global range tests are stored in the instrument table as 'Measurement_Global_Range_QC'. QC Flags: Measurement within global threshold= 0, Below minimum global threshold =1, Above maximum global threshold =2. Local Range Test: Data point values are checked against historical seasonal ranges for each parameter, using data provided by URI GSO's Narragansett Bay Long-Term Plankton Time Series (https://web.uri.edu/gso/research/plankton/). QC Flags from local range tests are stored in the instrument table as 'Measurement_Local_Range_QC'. Local Range QC Flags: Measurement within local seasonal threshold= 0, Below minimum local seasonal threshold =1, Above maximum local seasonal threshold =2. Stuck Value Test: To identify potential stuck values from a sensor, each data point is compared to subsequent values using sliding 3 and 5 frame windows. QC Flags from stuck value tests are stored in the instrument table as 'Measurement_Stuck_Value_QC' QC Flags: No stuck value detected= 0, Suspect Stuck Sensor (3 consecutive identical values) =1, Stuck Sensor (5 consecutive identical values) =2. Instrument Range Test: Data point values for meterological measurements are checked against the manufacturer's specified measurement ranges. QC Flags: Measurement within instrument range= 0, Measurement below instrument range =1, Measurement above instrument range =2. cdm_data_type=Other Conventions=COARDS, CF-1.6, ACDD-1.3 Easternmost_Easting=0.0 geospatial_lat_max=0.0 geospatial_lat_min=0.0 geospatial_lat_units=degrees_north geospatial_lon_max=0.0 geospatial_lon_min=0.0 geospatial_lon_units=degrees_east infoUrl=riddc.brown.edu institution=Rhode Island Data Discovery Center keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=0.0 sourceUrl=(local files) Southernmost_Northing=0.0 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=station_name testOutOfDate=now-26days time_coverage_end=2024-12-04T06:00Z time_coverage_start=2024-12-04T05:40Z Westernmost_Easting=0.0