This file is used by the SampleQC tableau workbook to provide insights on which samples passed QC. It is a subset of the file that is generated by the RNASeq pipeline where all the genes are dropped out.

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 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').

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).

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.

In June and July of 2020, 45 groundwater wells in McHenry County, Illinois, were sampled for water quality (field properties, major ions, nutrients, and trace metals) and 12 wells were sampled for contaminants of emerging concern (pharmaceuticals, pesticides, and wastewater indicator compounds). Quality-assurance and quality-control samples were collected during the June and July 2020 sampling that included equipment blanks, field blanks, and replicates. The results of these samples were used to understand the sources of bias and variability associated with sample collection, processing, storage, and shipping. This data release contains one comma separated values files containing the results of the quality-control sample collection for general water quality (metals, nutrients, and major ions) and contaminants of emerging concern (wastewater indicator compounds and pharmaceuticals). Water-quality data from the associated groundwater monitoring well data are available at the Nationa ...

This dataset contains data tables of laboratory quality-control data associated with environmental samples analyzed for microbiological constituents at the Ohio Water Microbiology Laboratory of the U.S. Geological Survey (USGS). The environmental samples were collected across the United States by USGS National Projects and projects in Water Science Centers. These quality-control data can be used to assess the quality of microbiological data for the associated environmental samples.

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.

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

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.

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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 growth of the global market during the for

This paper presents the importance of simple spatial statistics techniques applied in positional quality control of spatial data. To this end, Analysis methods of point data spatial distribution pattern are presented, as well as bias analysis in the positional discrepancies samples. To evaluate the points spatial distribution Nearest Neighbor and Ripley's K function methods were used. As for bias analysis, the average directional vectors of discrepancies and the circular variance were used. A methodology for positional quality control of spatial data is proposed, in which includes sampling planning and its spatial distribution pattern evaluation, analyzing the data normality through the application of bias tests, and positional accuracy classification according to a standard. For the practical experiment, an orthoimage generated from a PRISM scene of the ALOS satellite was evaluated. Results showed that the orthoimage is accurate on a scale of 1:25,000, being classified as Class A according to the Brazilian standard positional accuracy, not showing bias at the coordinates. The main contribution of this work is the incorporation of spatial statistics techniques in cartographic quality control.

This dataset contains replicate samples collected in the field by community technicians. No field replicates were collected in 2012. Replicate constituents with differences less than 10 percent are considered acceptable.

Groundwater samples were collected and analyzed from 1,015 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and the water-quality data and quality-control data are included in this data release. The samples were collected from three types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; and major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including nutrients, major ions, trace elements, volatile organic compounds (VOCs), pesticides, radionuclides, and microbial indicators. Data from samples collected between 2012 and 2019 are associated with networks described in a collection of data series reports and associated data releases (Arnold and others, 2016a,b, 2017a,b, 2018a,b, 2020a,b; Kingsbury and others, 2020 and 2021). This data release includes data from networks sampled in 2019 through 2023. For some networks, certain constituent group data were not completely reviewed and released by the analyzing laboratory for all network sites in time for publication of this data release. For networks with incomplete data, no data were published for the incomplete constituent group(s). Datasets excluded from this data release because of incomplete results will be included in the earliest data release published after the dataset is complete. NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 4.0. First posted - December 12, 2021 (available from author) Revised - January 27, 2023 (version 2.0: available from author) Revised - November 2, 2023 (version 3.0: available from author) Revised - April 18, 2025 (version 4.0) The compressed file (NWQP_GW_QW_DataRelease_v4.zip) contains 24 files: 23 files of groundwater-quality, quality-control data, and general information in ASCII text tab-delimited format, and one corresponding metadata file in xml format that includes descriptions of all the tables and attributes. A shapefile containing study areas for each of the sampled groundwater networks also is provided in folder NWQP_GW_QW_Network_Boundaries_v4 of this data release and is described in the metadata (Network_Boundaries_v4.zip). The 23 data files are as follows: Description_of_Data_Fields_v4.txt: Information for all constituents and ancillary information found in Tables 3 through 21. Network_Reference_List_v4.txt: References used for the description of the networks sampled by the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project. Table_1_site_list_v4.txt: Information about wells that have environmental data. Table_2_parameters_v4.txt: Constituent primary uses and sources; laboratory analytical schedules and sampling period; USGS parameter codes (pcodes); comparison thresholds; and reporting levels. Table_3_qw_indicators_v4.txt: Water-quality indicators in groundwater samples collected by the USGS NAWQA Project. Table_4_nutrients_v4.txt: Nutrients and dissolved organic carbon in groundwater samples collected by the USGS NAWQA Project. Table_5_major_ions_v4.txt: Major and minor ions in groundwater samples collected by the USGS NAWQA Project. Table_6_trace_elements_v4.txt: Trace elements in groundwater samples collected by the USGS NAWQA Project. Table_7_vocs_v4.txt: Volatile organic compounds (VOCs) in groundwater samples collected by the USGS NAWQA Project. Table_8_pesticides_v4.txt: Pesticides in groundwater samples collected by the USGS NAWQA Project. Table_9_radchem_v4.txt: Radionuclides in groundwater samples collected by the USGS NAWQA Project. Table_10_micro_v4.txt: Microbiological indicators in groundwater samples collected by the USGS NAWQA Project. Table_11_qw_ind_QC_v4.txt: Water-quality indicators in groundwater replicate samples collected by the USGS NAWQA Project. Table_12_nuts_QC_v4.txt: Nutrients and dissolved organic carbon in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_13_majors_QC_v4.txt: Major and minor ions in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_14_trace_element_QC_v4.txt: Trace elements in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_15_vocs_QC_v4.txt: Volatile organic compounds (VOCs) in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_16_pesticides_QC_v4.txt: Pesticide compounds in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_17_radchem_QC_v4.txt: Radionuclides in groundwater replicate samples collected by the USGS NAWQA Project. Table_18_micro_QC_v4.txt: Microbiological indicators in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_19_TE_SpikeStats_v4.txt: Statistics for trace elements in groundwater spike samples collected by the USGS NAWQA Project. Table_20_VOCLabSpikeStats_v4.txt: Statistics for volatile organic compounds (VOCs) in groundwater spike samples collected by the USGS NAWQA Project. Table_21_PestFieldSpikeStats_v4.txt: Statistics for pesticide compounds in groundwater spike samples collected by the USGS NAWQA Project. References Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017a, Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey Data Series 1063, 83 p., https://doi.org/10.3133/ds1063. Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017b, Datasets from Groundwater quality data from the National Water Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey data release, https://doi.org/10.5066/F7W0942N. Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124. Arnold, T.L., Bexfield, L.M., Musgrove, M., Lindsey, B.D., Stackelberg, P.E., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., and Belitz, K., 2018b, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2015 and Previously Unpublished Data from 2013-2014, U.S. Geological Survey data release, https://doi.org/10.5066/F7XK8DHK. Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and Belitz, K., 2018a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014: U.S. Geological Survey Data Series 1087, 68 p., https://doi.org/10.3133/ds1087. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016a, Groundwater quality data from the National Water-Quality Assessment Project, May 2012 through December 2013 (ver. 1.1, November 2016): U.S. Geological Survey Data Series 997, 56 p., https://doi.org/10.3133/ds997. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016b, Groundwater quality data from the National Water Quality Assessment Project, May 2012 through December 2014 and select quality-control data from May 2012 through December 2013: U.S. Geological Survey data release, https://doi.org/10.5066/F7HQ3X18. Arnold, T.L., Sharpe, J.B., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020b, Datasets from groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9W4RR74. Kingsbury, J.A., Sharpe, J.B., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Kulongoski, J.T., Lindsey, B.D., and Belitz, K., 2020, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9XATXV1. Kingsbury, J.A., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Tesoriero, A.J., Lindsey B.D., and Belitz, K., 2021, Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019: U.S. Geological Survey Data Series 1136, 97 p., https://doi.org/10.3133/ds1136.

In 2018, groundwater samples were collected from aquifers in the Williston Basin in parts of eastern Montana, western North Dakota, and northwestern South Dakota. This dataset includes quality-control data for volatile organic compounds that include data for source-solution blanks and field blanks. The dataset also includes data for sulfur hexafluoride in environmental samples of groundwater.

Data Quality Tools Market Outlook

According to our latest research, the global Data Quality Tools market size reached USD 2.65 billion in 2024, reflecting robust demand across industries for solutions that ensure data accuracy, consistency, and reliability. The market is poised to expand at a CAGR of 17.6% from 2025 to 2033, driven by increasing digital transformation initiatives, regulatory compliance requirements, and the exponential growth of enterprise data. By 2033, the Data Quality Tools market is forecasted to attain a value of USD 12.06 billion, as organizations worldwide continue to prioritize data-driven decision-making and invest in advanced data management solutions.

A key growth factor propelling the Data Quality Tools market is the proliferation of data across diverse business ecosystems. Enterprises are increasingly leveraging big data analytics, artificial intelligence, and cloud computing, all of which demand high-quality data as a foundational element. The surge in unstructured and structured data from various sources such as customer interactions, IoT devices, and business operations has made data quality management a strategic imperative. Organizations recognize that poor data quality can lead to erroneous insights, operational inefficiencies, and compliance risks. As a result, the adoption of comprehensive Data Quality Tools for data profiling, cleansing, and enrichment is accelerating, particularly among industries with high data sensitivity like BFSI, healthcare, and retail.

Another significant driver for the Data Quality Tools market is the intensifying regulatory landscape. Data privacy laws such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other country-specific mandates require organizations to maintain high standards of data integrity and traceability. Non-compliance can result in substantial financial penalties and reputational damage. Consequently, businesses are investing in sophisticated Data Quality Tools that provide automated monitoring, data lineage, and audit trails to ensure regulatory adherence. This regulatory push is particularly prominent in sectors like finance, healthcare, and government, where the stakes for data accuracy and security are exceptionally high.

Advancements in cloud technology and the growing trend of digital transformation across enterprises are also fueling market growth. Cloud-based Data Quality Tools offer scalability, flexibility, and cost-efficiency, enabling organizations to manage data quality processes remotely and in real-time. The shift towards Software-as-a-Service (SaaS) models has lowered the entry barrier for small and medium enterprises (SMEs), allowing them to implement enterprise-grade data quality solutions without substantial upfront investments. Furthermore, the integration of machine learning and artificial intelligence capabilities into data quality platforms is enhancing automation, reducing manual intervention, and improving the overall accuracy and efficiency of data management processes.

From a regional perspective, North America continues to dominate the Data Quality Tools market due to its early adoption of advanced technologies, a mature IT infrastructure, and the presence of leading market players. However, the Asia Pacific region is emerging as a high-growth market, driven by rapid digitalization, increasing investments in IT, and a burgeoning SME sector. Europe maintains a strong position owing to stringent data privacy regulations and widespread enterprise adoption of data management solutions. Latin America and the Middle East & Africa, while relatively nascent, are witnessing growing awareness and adoption, particularly in the banking, government, and telecommunications sectors.

Component Analysis

The Component segment of the Data Quality Tools market is bifurcated into software and services. Software dominates the segment, accounting for a significant share of the global market revenue in 2024. This dominance is

ESG Data Quality Management for Banks Market Outlook

According to our latest research, the global ESG Data Quality Management for Banks market size reached USD 1.37 billion in 2024, reflecting a robust and accelerating demand for high-integrity ESG data in the banking sector. The market is expected to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 5.12 billion by 2033. This growth is primarily driven by stringent regulatory requirements, increasing stakeholder pressure for transparency, and the need for reliable ESG metrics to inform risk management and investment decisions.

One of the core growth drivers for the ESG Data Quality Management for Banks market is the intensifying regulatory landscape. Governments and regulatory bodies across the globe are mandating stricter ESG disclosure norms, compelling banks to invest in sophisticated data management solutions to ensure compliance. The European Union’s Sustainable Finance Disclosure Regulation (SFDR) and the US Securities and Exchange Commission’s (SEC) proposed climate-related disclosure rules are prime examples of such regulatory frameworks. These regulations not only require banks to collect, verify, and report ESG data but also emphasize the quality and reliability of this information. As a result, banks are increasingly adopting advanced ESG data quality management platforms to streamline data collection, validation, and reporting processes, thereby mitigating compliance risks and enhancing their reputation among stakeholders.

Another significant growth factor is the rising importance of ESG factors in risk management and investment analysis. Banks are recognizing that ESG risks, such as climate change, social unrest, and governance failures, can have profound financial implications. To effectively identify, assess, and mitigate these risks, banks require high-quality ESG data that is accurate, timely, and auditable. The integration of ESG data quality management solutions enables banks to develop more robust risk models, improve credit assessments, and make informed lending and investment decisions. Furthermore, investors and clients are increasingly demanding transparency regarding banks’ ESG performance, further driving the adoption of data quality management tools that can provide granular, verifiable, and actionable ESG insights.

Technological advancements also play a pivotal role in the growth trajectory of the ESG Data Quality Management for Banks market. With the advent of artificial intelligence, machine learning, and big data analytics, banks can now automate the collection, cleansing, and analysis of large volumes of ESG data from diverse sources. These technologies enhance data accuracy, reduce manual intervention, and provide real-time insights, enabling banks to respond swiftly to evolving ESG risks and opportunities. Additionally, the proliferation of cloud-based ESG data management platforms offers scalability, flexibility, and cost-effectiveness, making it easier for banks of all sizes to implement and scale their ESG data quality initiatives.

From a regional perspective, Europe currently leads the ESG Data Quality Management for Banks market, driven by its progressive regulatory environment and strong emphasis on sustainable finance. North America follows closely, with increasing regulatory scrutiny and growing investor demand for ESG transparency propelling market growth. The Asia Pacific region is poised for the fastest growth, fueled by rapid digitalization in the banking sector and emerging ESG regulations in key markets such as China, Japan, and Australia. Latin America and the Middle East & Africa, while still nascent, are witnessing rising awareness of ESG issues and gradually strengthening regulatory frameworks, which are expected to contribute to market expansion over the forecast period.

Component Analysis

The Component segment of the ESG Data Quality Management for Banks market is primarily bifurcated into Software and

Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory’s routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

In 2021–23, the U.S. Geological Survey (USGS), in cooperation with the Ohio Division of Natural Resources, led a study to characterize baseline water quality (2021–23) in eastern Ohio, as they relate to hydraulic fracturing and (or) other oil and gas extraction-related activities. Water-quality data were collected eight times at each of eight sampling sites during a variety of flow conditions to assess baseline water quality. Quality-control (QC) samples collected before and during sampling consisted of blanks and replicates. Blank samples were used to check for contamination potentially introduced during sample collection, processing, equipment cleaning, or analysis. Replicate samples were used to determine the reproducibility or variability in the collection and analysis of environmental samples. All QC samples were collected and processed according to protocols described in the “National Field Manual for the Collection of Water-Quality Data” (USGS, variously dated). To ensure sample integrity and final quality of data, QC samples (one equipment blank, three field blanks, and five replicate samples) were collected for major ions, nutrients, and organics. This data set includes one table of blank samples and one table of field replicate samples. U.S. Geological Survey, variously dated, National field manual for the collection of water-quality data: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A10, available online at http://pubs.water.usgs.gov/twri9A.

The study of N-linked glycosylation has long been complicated by a lack of bioinformatics tools. In particular, there is still a lack of fast and robust data processing tools for targeted (relative) quantitation. We have developed modular, high-throughput data processing software, MassyTools, that is capable of calibrating spectra, extracting data, and performing quality control calculations based on a user-defined list of glycan or glycopeptide compositions. Typical examples of output include relative areas after background subtraction, isotopic pattern-based quality scores, spectral quality scores, and signal-to-noise ratios. We demonstrated MassyTools’ performance on MALDI-TOF-MS glycan and glycopeptide data from different samples. MassyTools yielded better calibration than the commercial software flexAnalysis, generally showing 2-fold better ppm errors after internal calibration. Relative quantitation using MassyTools and flexAnalysis gave similar results, yielding a relative standard deviation (RSD) of the main glycan of ∼6%. However, MassyTools yielded 2- to 5-fold lower RSD values for low-abundant analytes than flexAnalysis. Additionally, feature curation based on the computed quality criteria improved the data quality. In conclusion, we show that MassyTools is a robust automated data processing tool for high-throughput, high-performance glycosylation analysis. The package is released under the Apache 2.0 license and is freely available on GitHub (https://github.com/Tarskin/MassyTools).