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).

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.

A dashboard used by government agencies to monitor key performance indicators (KPIs) and communicate progress made on strategic outcomes with the general public and other interested stakeholders.

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.

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.

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 National Water Information System (NWIS) web database (https://doi.org/10.5066/F7P55KJN). Results and discussion of the water quality and contaminants of emerging concern can also be found in the associated scientific investigation report referenced.

PurposeThe aim of this study was to compare the functional characteristics of two computer-based systems for quality control of cancer registry data through analysis of their output differences.MethodsThe study used cancer incidence data from 22 of the 49 registries of the Italian Network of Cancer Registries registered between 1986 and 2017. Two different data checking systems developed by the WHO International Agency for Research on Cancer (IARC) and the Joint Research Center (JRC) with the European Network of Cancer Registries (ENCR) and routinely used by registrars were used to check the quality of the data. The outputs generated by the two systems on the same dataset of each registry were analyzed and compared.ResultsThe study included a total of 1,305,689 cancer cases. The overall quality of the dataset was high, with 86% (81.7-94.1) microscopically verified cases and only 1.3% (0.03-3.06) cases with a diagnosis by death certificate only. The two check systems identified a low percentage of errors (JRC-ENCR 0.17% and IARC 0.003%) and about the same proportion of warnings (JRC-ENCR 2.79% and IARC 2.42%) in the dataset. Forty-two cases (2% of errors) and 7067 cases (11.5% of warnings) were identified by both systems in equivalent categories. 11.7% of warnings related to TNM staging were identified by the JRC-ENCR system only. The IARC system identified mainly incorrect combination of tumor grade and morphology (72.5% of warnings).ConclusionBoth systems apply checks on a common set of variables, but some variables are checked by only one of the systems (for example, checks on patient follow-up and tumor stage at diagnosis are included by the JRC-ENCR system only). Most errors and warnings were categorized differently by the two systems, but usually described the same issues, with warnings related to “morphology” (JRC-ENCR) and “histology” (IARC) being the most frequent. It is important to find the right balance between the need to maintain high standards of data quality and the workability of such systems in the daily routine of the cancer registry.

Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study’s outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.

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.

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

2. Time and resource savings: Concentrate on your core business goals while we efficiently handle the data extraction process at an affordable cost.

3. Adaptable solutions: Share your specific data requirements, and we'll craft a customized data collection approach to meet your objectives.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

Our location data powers the most advanced address validation solutions for enterprise backend and frontend systems.

A global, standardized, self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries.

All geospatial data for address data validation is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Address Validation at Zip Code Level Database (Geospatial data)

• Address capture and address validation

• Address autocomplete

• Address verification

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Product Features

• Dedicated features to deliver best-in-class user experience

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data for address validation are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why do companies choose our location databases

• Enterprise-grade service

• Full control over security, speed, and latency

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

• Seamlessly integrated into your software

Note: Custom address validation packages are available. Please submit a request via the above contact button for more details.

RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments.

This example displays the quality report and quality summary information for 15 sensor measurements and 3 arbitrary quality analyses. The quality report contains the individual quality flag outcomes for each sensor measurement, i.e., rows 1–15. The quality summary includes the corresponding quality metrics and the final quality flag information, i.e., the bottom row.Overview of the information contained in the quality summary and quality report.

This dataset contains data collected from field blanks. Field blanks are deionized water processed in the field by community technicians using processing methods identical to those for surface water samples. Field blanks are then analyzed in the laboratory following procedures identical to those for surface water samples.

Surface water grab sample collected periodically and analyzed for a broad spectrum of physical and chemical constituents

Red Wine Quality Dataset

Introduction

This dataset contains physicochemical properties of red wine samples. The goal is to analyze how these features influence the quality of wine. It can be used for exploratory data analysis, statistical modeling, and machine learning tasks such as regression and classification.

Dataset Information

The dataset consists of multiple red wine samples with their respective chemical compositions. Each row represents a different wine sample, and the columns correspond to specific properties that impact its taste and quality.

Columns Description

• Fixed Acidity: Concentration of non-volatile acids (e.g., tartaric acid) in g/dm³.
• Volatile Acidity: Amount of acetic acid in g/dm³, which can affect the wine’s aroma and taste. High levels can lead to an unpleasant vinegar-like taste.
• Citric Acid: Presence of citric acid in g/dm³, which adds freshness and flavor to the wine.
• Residual Sugar: The amount of sugar remaining after fermentation, measured in g/dm³. Affects the wine's sweetness.
• Chlorides: Amount of salt (sodium chloride) in the wine, measured in g/dm³. Higher values can negatively affect taste.
• Free Sulfur: The level of free sulfur dioxide (SO₂), which acts as an antioxidant and antimicrobial agent, helping preserve the wine’s freshness.

Possible Use Cases

• Exploratory Data Analysis (EDA): Understanding the distribution and correlation between wine features.
• Wine Quality Prediction: Using machine learning models to predict wine quality based on physicochemical attributes.
• Feature Importance Analysis: Identifying which features have the most impact on wine quality.

Acknowledgments

This dataset is inspired by wine composition studies and can be used for educational and research purposes.

Examples from the analysis of qualitative responses to the question “Are data quality processes sufficiently rigorous to provide a ‘fit-for-purpose’ dataset?”.

Data Set Information

This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.), while the target/output variable is a numerical score based on sensory data—median of at least 3 evaluations made by wine experts. Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Due to privacy and logistic issues, there is no data about grape types, wine brand, and wine selling price.

This data set is a combined version of the two separate files (distinct red and white wine data sets) originally shared in the UCI Machine Learning Repository.

The following are some existing data sets on Kaggle from the same source (with notable differences from this data set): - Red Wine Quality (contains red wine data only) - Wine Quality (combination of red and white wine data but with some values randomly removed) - Wine Quality (red and white wine data not combined)

Contents

Input variables:

1 - type of wine: type of wine (categorical: 'red', 'white')

(continuous variables based on physicochemical tests)

2 - fixed acidity: The acids that naturally occur in the grapes used to ferment the wine and carry over into the wine. They mostly consist of tartaric, malic, citric or succinic acid that mostly originate from the grapes used to ferment the wine. They also do not evaporate easily. (g / dm^3)

3 - volatile acidity: Acids that evaporate at low temperatures—mainly acetic acid which can lead to an unpleasant, vinegar-like taste at very high levels. (g / dm^3)

4 - citric acid: Citric acid is used as an acid supplement which boosts the acidity of the wine. It's typically found in small quantities and can add 'freshness' and flavor to wines. (g / dm^3)

5 - residual sugar: The amount of sugar remaining after fermentation stops. It's rare to find wines with less than 1 gram/liter. Wines residual sugar level greater than 45 grams/liter are considered sweet. On the other end of the spectrum, a wine that does not taste sweet is considered as dry. (g / dm^3)

6 - chlorides: The amount of chloride salts (sodium chloride) present in the wine. (g / dm^3)

7 - free sulfur dioxide: The free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine. All else constant, the higher the free sulfur dioxide content, the stronger the preservative effect. (mg / dm^3)

8 - total sulfur dioxide: The amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine. (mg / dm^3)

9 - density: The density of wine juice depending on the percent alcohol and sugar content; it's typically similar but higher than that of water (wine is 'thicker'). (g / cm^3)

10 - pH: A measure of the acidity of wine; most wines are between 3-4 on the pH scale. The lower the pH, the more acidic the wine is; the higher the pH, the less acidic the wine. (The pH scale technically is a logarithmic scale that measures the concentration of free hydrogen ions floating around in your wine. Each point of the pH scale is a factor of 10. This means a wine with a pH of 3 is 10 times more acidic than a wine with a pH of 4)

11 - sulphates: Amount of potassium sulphate as a wine additive which can contribute to sulfur dioxide gas (S02) levels; it acts as an antimicrobial and antioxidant agent.(g / dm3)

12 - alcohol: How much alcohol is contained in a given volume of wine (ABV). Wine generally contains between 5–15% of alcohols. (% by volume)

Output variable:

13 - quality: score between 0 (very bad) and 10 (very excellent) by wine experts

Acknowledgements

Source: P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

Data credit goes to UCI. Visit their website to access the original data set directly: https://archive.ics.uci.edu/ml/datasets/wine+quality

Context

So much about wine making remains elusive—taste is very subjective, making it extremely challenging to predict exactly how consumers will react to a certain bottle of wine. There is no doubt that winemakers, connoisseurs, and scientists have greatly contributed their expertise to ...