NSF information quality guidelines designed to fulfill the OMB guidelines.

Metrics used to give an indication of data quality between our test’s groups. This includes whether documentation was used and what proportion of respondents rounded their answers. Unit and item non-response are also reported.

Data Quality Tools Market size was valued at USD 2.71 Billion in 2024 and is projected to reach USD 4.15 Billion by 2032, growing at a CAGR of 5.46% from 2026 to 2032.Global Data Quality Tools Market DriversGrowing Data Volume and Complexity: Sturdy data quality technologies are necessary to guarantee accurate, consistent, and trustworthy information because of the exponential increase in the volume and complexity of data supplied by companies.Growing Knowledge of Data Governance: Businesses are realizing how critical it is to uphold strict standards for data integrity and data governance. Tools for improving data quality are essential for advancing data governance programs.Needs for Regulatory Compliance: Adoption of data quality technologies is prompted by strict regulatory requirements, like GDPR, HIPAA, and other data protection rules, which aim to ensure compliance and reduce the risk of negative legal and financial outcomes.Growing Emphasis on Analytics and Business Intelligence (BI): The requirement for accurate and trustworthy data is highlighted by the increasing reliance on corporate intelligence and analytics for well-informed decision-making. Tools for improving data quality contribute to increased data accuracy for analytics and reporting.Initiatives for Data Integration and Migration: Companies engaged in data integration or migration initiatives understand how critical it is to preserve data quality throughout these procedures. The use of data quality technologies is essential for guaranteeing seamless transitions and avoiding inconsistent data.Real-time data quality management is in demand: Organizations looking to make prompt decisions based on precise and current information are driving an increased need for real-time data quality management systems.The emergence of cloud computing and big data: Strong data quality tools are required to manage many data sources, formats, and environments while upholding high data quality standards as big data and cloud computing solutions become more widely used.Pay attention to customer satisfaction and experience: Businesses are aware of how data quality affects customer happiness and experience. Establishing and maintaining consistent and accurate customer data is essential to fostering trust and providing individualized services.Preventing Fraud and Data-Related Errors: By detecting and fixing mistakes in real time, data quality technologies assist firms in preventing errors, discrepancies, and fraudulent activities while lowering the risk of monetary losses and reputational harm.Linking Master Data Management (MDM) Programs: Integrating with MDM solutions improves master data management overall and guarantees high-quality, accurate, and consistent maintenance of vital corporate information.Offerings for Data Quality as a Service (DQaaS): Data quality tools are now more widely available and scalable for companies of all sizes thanks to the development of Data Quality as a Service (DQaaS), which offers cloud-based solutions to firms.

Data on long-form data quality indicators for 2021 Census commuting content, Canada, provinces and territories, census divisions and census subdivisions.

The Data Quality Software and Solutions market is experiencing robust growth, driven by the increasing volume and complexity of data generated by businesses across all sectors. The market's expansion is fueled by a rising demand for accurate, consistent, and reliable data for informed decision-making, improved operational efficiency, and regulatory compliance. Key drivers include the surge in big data adoption, the growing need for data integration and governance, and the increasing prevalence of cloud-based solutions offering scalable and cost-effective data quality management capabilities. Furthermore, the rising adoption of advanced analytics and artificial intelligence (AI) is enhancing data quality capabilities, leading to more sophisticated solutions that can automate data cleansing, validation, and profiling processes. We estimate the 2025 market size to be around $12 billion, growing at a compound annual growth rate (CAGR) of 10% over the forecast period (2025-2033). This growth trajectory is being influenced by the rapid digital transformation across industries, necessitating higher data quality standards. Segmentation reveals a strong preference for cloud-based solutions due to their flexibility and scalability, with large enterprises driving a significant portion of the market demand. However, market growth faces some restraints. High implementation costs associated with data quality software and solutions, particularly for large-scale deployments, can be a barrier to entry for some businesses, especially SMEs. Also, the complexity of integrating these solutions with existing IT infrastructure can present challenges. The lack of skilled professionals proficient in data quality management is another factor impacting market growth. Despite these challenges, the market is expected to maintain a healthy growth trajectory, driven by increasing awareness of the value of high-quality data, coupled with the availability of innovative and user-friendly solutions. The competitive landscape is characterized by established players such as Informatica, IBM, and SAP, along with emerging players offering specialized solutions, resulting in a diverse range of options for businesses. Regional analysis indicates that North America and Europe currently hold significant market shares, but the Asia-Pacific region is projected to witness substantial growth in the coming years due to rapid digitalization and increasing data volumes.

Data Quality Rule Generation AI Market Outlook

According to our latest research, the global Data Quality Rule Generation AI market size reached USD 1.42 billion in 2024, reflecting the growing adoption of artificial intelligence in data management across industries. The market is projected to expand at a compound annual growth rate (CAGR) of 26.8% from 2025 to 2033, reaching an estimated USD 13.29 billion by 2033. This robust growth trajectory is primarily driven by the increasing need for high-quality, reliable data to fuel digital transformation initiatives, regulatory compliance, and advanced analytics across sectors.

One of the primary growth factors for the Data Quality Rule Generation AI market is the exponential rise in data volumes and complexity across organizations worldwide. As enterprises accelerate their digital transformation journeys, they generate and accumulate vast amounts of structured and unstructured data from diverse sources, including IoT devices, cloud applications, and customer interactions. This data deluge creates significant challenges in maintaining data quality, consistency, and integrity. AI-powered data quality rule generation solutions offer a scalable and automated approach to defining, monitoring, and enforcing data quality standards, reducing manual intervention and improving overall data trustworthiness. Moreover, the integration of machine learning and natural language processing enables these solutions to adapt to evolving data landscapes, further enhancing their value proposition for enterprises seeking to unlock actionable insights from their data assets.

Another key driver for the market is the increasing regulatory scrutiny and compliance requirements across various industries, such as BFSI, healthcare, and government sectors. Regulatory bodies are imposing stricter mandates around data governance, privacy, and reporting accuracy, compelling organizations to implement robust data quality frameworks. Data Quality Rule Generation AI tools help organizations automate the creation and enforcement of complex data validation rules, ensuring compliance with industry standards like GDPR, HIPAA, and Basel III. This automation not only reduces the risk of non-compliance and associated penalties but also streamlines audit processes and enhances stakeholder confidence in data-driven decision-making. The growing emphasis on data transparency and accountability is expected to further drive the adoption of AI-driven data quality solutions in the coming years.

The proliferation of cloud-based analytics platforms and data lakes is also contributing significantly to the growth of the Data Quality Rule Generation AI market. As organizations migrate their data infrastructure to the cloud to leverage scalability and cost efficiencies, they face new challenges in managing data quality across distributed environments. Cloud-native AI solutions for data quality rule generation provide seamless integration with leading cloud platforms, enabling real-time data validation and cleansing at scale. These solutions offer advanced features such as predictive data quality assessment, anomaly detection, and automated remediation, empowering organizations to maintain high data quality standards in dynamic cloud environments. The shift towards cloud-first strategies is expected to accelerate the demand for AI-powered data quality tools, particularly among enterprises with complex, multi-cloud, or hybrid data architectures.

From a regional perspective, North America continues to dominate the Data Quality Rule Generation AI market, accounting for the largest share in 2024 due to early adoption, a strong technology ecosystem, and stringent regulatory frameworks. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in AI and analytics by enterprises and governments. Europe is also a significant market, driven by robust data privacy regulations and a mature enterprise landscape. Latin America and the Middle East & Africa are emerging as promising markets, supported by growing awareness of data quality benefits and the proliferation of cloud and AI technologies. The global outlook remains highly positive as organizations across regions recognize the strategic importance of data quality in achieving business objectives and competitive advantage.

The excel file contains time series data of flow rates, concentrations of alachlor , atrazine, ammonia, total phosphorus, and total suspended solids observed in two watersheds in Indiana from 2002 to 2007. The aggregate time series data corresponding or representative to all these parameters was obtained using a specialized, data-driven technique. The aggregate data is hypothesized in the published paper to represent the overall health of both watersheds with respect to various potential water quality impairments. The time series data for each of the individual water quality parameters were used to compute corresponding risk measures (Rel, Res, and Vul) that are reported in Table 4 and 5. The aggregation of the risk measures, which is computed from the aggregate time series and water quality standards in Table 1, is also reported in Table 4 and 5 of the published paper. Values under column heading "uncertainty" reports uncertainties associated with reconstruction of missing records of the water quality parameters. Long-term records of the water quality parameters were reconstructed in order to estimate the (R-R-V) and corresponding aggregate risk measures. This dataset is associated with the following publication: Hoque, Y., S. Tripathi, M. Hantush , and R. Govindaraju. Aggregate Measures of Watershed Health from Reconstructed Water Quality Data with Uncertainty. Ed Gregorich JOURNAL OF ENVIRONMENTAL QUALITY. American Society of Agronomy, MADISON, WI, USA, 45(2): 709-719, (2016).

Water is the most precious and essential resource among all-natural resources. Some organism survives without oxygen and food such as Tardigrades. But no one can survive without water. The increase in the development of industries and human activities over the previous century is having an overwhelming impact on our environment. Most cities in the world have started to implement the aqua management system. The development of cloud computing, artificial intelligence, remote sensing, big data and the Internet of Things provide new opening and move toward the improvement and application of aqua resource monitoring system. For predicting water quality of rivers, dams and lakes in India, water quality parameter dataset is created. The name of the data set is Aquaattributes. Completely 1360 samples are presented in the Aquaattributes. The data set size is 190 KB. Attributes of the dataset location name along with its longitude and latitude values and water quality parameters.

The Gridded Population of the World, Version 4 (GPWv4): Data Quality Indicators, Revision 11 consists of three data layers created to provide context for the population count and density rasters, and explicit information on the spatial precision of the input boundary data. The Data Context raster explains pixels with a "0" population estimate in the population count and density rasters based on information included in the census documents, such as areas that are part of a national park, areas that have no households, etc. The Water Mask raster distinguishes between pixels that are completely water and/or ice (Total Water Pixels), pixels that contain water and land (Partial Water Pixels), pixels that are completely land (Total Land Pixels), and pixels that are completely ocean water (Ocean Pixels). The Mean Administrative Unit Area raster represents the mean input Unit size in square kilometers and provides a quantitative surface that indicates the size of the input Unit(s) from which population count and density rasters are created. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

Retail Data Quality Platform Market Outlook

As per our latest research, the global retail data quality platform market size in 2024 stands at USD 1.62 billion, with a robust compound annual growth rate (CAGR) of 17.8% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 6.01 billion. The primary growth driver for this market is the accelerating digital transformation across the retail sector, which has amplified the need for reliable, actionable data to optimize operations, enhance customer experiences, and ensure regulatory compliance.

The increasing complexity of retail operations, driven by omnichannel strategies and the proliferation of digital touchpoints, is compelling retailers to invest in advanced data quality platforms. These platforms facilitate the integration, cleansing, and enrichment of data from disparate sources, ensuring that business decisions are based on accurate and up-to-date information. Retailers are recognizing that poor data quality can lead to significant revenue losses, customer dissatisfaction, and compliance risks. As a result, the demand for robust retail data quality solutions is surging, particularly among enterprises seeking to leverage advanced analytics, artificial intelligence, and machine learning for personalized customer engagement and operational efficiency.

Another significant growth factor is the evolving regulatory landscape, with stringent data governance and privacy requirements such as GDPR, CCPA, and other region-specific mandates. Retailers are under mounting pressure to maintain high data quality standards to avoid hefty penalties and reputational damage. This has spurred investments in platforms that offer automated data validation, auditing, and monitoring capabilities. Furthermore, the rise of cloud-based solutions is democratizing access to sophisticated data quality tools, enabling small and medium enterprises (SMEs) to compete effectively with larger players by harnessing high-quality data for strategic decision-making and customer-centric innovation.

The rapid expansion of e-commerce and the increasing adoption of artificial intelligence and big data analytics in retail are further propelling the market. Retailers are leveraging data quality platforms to gain deeper insights into customer behavior, optimize inventory management, and streamline supply chain operations. The integration of these platforms with existing retail management systems ensures seamless data flow and consistency across all business functions. Additionally, the growing emphasis on personalized marketing and customer relationship management is making data quality an indispensable asset for retailers aiming to differentiate themselves in a highly competitive landscape.

Regionally, North America leads the retail data quality platform market, followed closely by Europe and Asia Pacific. North America's dominance is attributed to the early adoption of advanced technologies, a mature retail ecosystem, and the presence of leading market players. However, Asia Pacific is poised for the highest growth rate over the forecast period, fueled by rapid digitalization, expanding e-commerce, and increasing investments in data-driven retail strategies. Latin America and the Middle East & Africa are also witnessing steady growth, driven by the modernization of retail infrastructure and the adoption of cloud-based solutions. These regional trends underscore the global momentum towards data-driven retail transformation.

Component Analysis

The component segment of the retail data quality platform market is bifurcated into software and services, each playing a pivotal role in shaping the market dynamics. Software solutions form the backbone of data quality platforms by providing the necessary tools for data profiling, cleansing, matching, enrichment, and monitoring. These solutions are increasingly leveraging artificial intelligence and

Data on short-form data quality indicators for 2021 Census, Canada, provinces and territories, census metropolitan areas, census agglomerations and census subdivisions.

The data quality monitoring system (DQMS) developed by the Satellite Oceanography Program at the NOAA National Centers for Environmental Information (NCEI) is based on the concept of a Rich Inventory developed by the previous NCEI Enterprise Data Systems Group. The principal concept of a Rich Inventory is to calculate the data Quality Assurance (QA) descriptive statistics for selected parameters in each Level-2 data file and publish the pre-generated images and NetCDF-format data to the public. The QA descriptive statistics include valid observation number, observation number over 3-sigma edited, minimum, maximum, mean, and standard deviation. The parameters include sea surface height anomaly, significant wave height, altimeter, and radiometer wind speed, radiometer water vapor content, and radiometer wet tropospheric correction from Jason-3 Level-2 Final Geophysical Data Record (GDR) and Interim Geophysical Data Record (IGDR) products.

**23 April 2021: A new version of this data set has been published. It includes data on 4 parameters (Ammoniacal nitrogen (adjusted), _Escherichia coli**_**, Macroinvertebrate Community Index and Total Phosphorus) that had been missing from the file that was published as part of the Our freshwater 2020 release in April 16 2020. The updated data set also includes data on DRP for all 593,337 REC segments, since the file from April 16 2020 only had data for 255,860 of these segments.**

16 April 2020: Subsequent to publication in April 2019 we discovered two small errors with this dataset. These included:

• Errors in the coordinates of some sites and their associated metadata (such as landcover and elevation).
• Errors in our calculation of dominant landcover.

In addition, flow data from TopNet has also been updated.

These changes have a minor impact on overall results. These changes have have been corrected, and are republished here, as part of the Our freshwater 2020 release.

​

IMPORTANT INFORMATION

_1) The main (cleaned) dataset is structured by each row having a nzsegment and np_id combination. A large dataset (~ 1 GB) has resulted, due to the inclusion of the ANZG/NOF columns and the 10 different np_id values. There are ~ 6 million rows to this dataset, however a 32-bit version of Microsoft Excel will only display/download ~ 1 million rows. A DBMS, statistical or GIS application is needed to view the entire dataset._

2) A smaller raw dataset (see attachments) is provided which structures each row relating to a river segment and drops the ANZG/NOF columns.

3) The attached metadata/date quality report provides further information on the NOF, ANZG and the "McDowell meet/doesnt meet" attachment.

​

This dataset contains ten parameters of water quality based on measurements made at monitored river sites:

• Nitrate-nitrogen
• Ammoniacal nitrogen
• Ammoniacal nitrogen (adjusted)
• Total nitrogen
• Total phosphorus
• Dissolved reactive phosphorus
• Water clarity
• Turbidity
• Escherichia coli
• Macroinvertebrate community index These parameters are used to measure:
• Modelled median values for all of New Zealand’s river length for the period 2013 to 2017
• For selected indicators, how the modelled values compare to the National Objectives Framework (NOF) (MfE, 2017) bands related to ecosystem health and human health for recreation, and to expected concentrations in natural conditions, as shown by the default guideline values in the Australian and New Zealand guidelines for fresh and marine water quality (ANZG, 2018)

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Summary report available at http://www.mfe.govt.nz/publications/fresh-water/spatial-modelling-of-river-water-quality-state-incorporating-monitoring

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between water years 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network The following selected graphics are included in this data release in .pdf format. Also included in this data release are web pages accessible for people with disabilities provided in compressed .zip format. The web pages present the same information as the .pdf files: Annual and seasonal discharge trends.pdf -- Graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Annual_and_seasonal_discharge_trends_htm.zip -- Compressed web page presenting graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of simulated quarterly sampling frequency trends.pdf -- Graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_simulated_quarterly_sampling_frequency_trends_htm.zip -- Compressed web page presenting graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of median parameter values.pdf -- Graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_median_parameter_values_htm.zip -- Compressed web page presenting graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus time.pdf -- Scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_time_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus discharge.pdf -- Scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_discharge_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by season.pdf -- Seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_season_htm.zip -- Compressed web page presenting seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of sampled discharge compared with mean daily discharge.pdf -- Boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_sampled_discharge_compared_with_mean_daily_discharge_htm.zip -- Compressed web page presenting boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by month.pdf -- Monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_month_htm.zip -- Compressed web page presenting monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report.

Water Quality Metrics and Filter Performance Dataset

Description

This dataset provides simulated data on various water quality parameters and their impact on the performance of water filtration systems. The dataset includes 19K+ samples, with attributes such as Total Dissolved Solids (TDS), turbidity, pH, water depth, and flow discharge. These parameters are used to estimate the filter life span (in hours) and filter efficiency (in percentage) under different conditions.

All the conditions for each feature is based on the data found on the Internet.

The dataset is ideal for exploring relationships between water quality metrics and filter performance, building predictive models, or conducting data analysis for environmental and engineering studies.

Features

• TDS (mg/l): Total Dissolved Solids in milligrams per liter (values < 500 mg/l).
• Turbidity (NTU): Measurement of water clarity in Nephelometric Turbidity Units (values < 10 NTU).
• pH: Measure of acidity/alkalinity (range: 6.0 to 8.5).
• Depth (m): Water depth in meters (range: 0.5 to 5 m).
• Flow Discharge (L/min): Water flow rate in liters per minute (range: 1 to 100 L/min).
• Filter Life Span (hours): Estimated lifespan of the filter based on input parameters (minimum value capped at 500 hours).
• Filter Efficiency (%): Estimated filtration efficiency (minimum value capped at 75%).

Applications

• Predicting filter performance based on water quality parameters.
• Analyzing the impact of water quality on filter lifespan and efficiency.
• Training machine learning models for environmental monitoring.

Note: This dataset is entirely synthetic and created for educational and research purposes. It does not represent real-world measurements but can be used to simulate scenarios for water filtration system analysis.

Data DescriptionWater Quality Parameters: Ammonia, BOD, DO, Orthophosphate, pH, Temperature, Nitrogen, Nitrate.Countries/Regions: United States, Canada, Ireland, England, China.Years Covered: 1940-2023.Data Records: 2.82 million.Definition of ColumnsCountry: Name of the water-body region.Area: Name of the area in the region.Waterbody Type: Type of the water-body source.Date: Date of the sample collection (dd-mm-yyyy).Ammonia (mg/l): Ammonia concentration.Biochemical Oxygen Demand (BOD) (mg/l): Oxygen demand measurement.Dissolved Oxygen (DO) (mg/l): Concentration of dissolved oxygen.Orthophosphate (mg/l): Orthophosphate concentration.pH (pH units): pH level of water.Temperature (°C): Temperature in Celsius.Nitrogen (mg/l): Total nitrogen concentration.Nitrate (mg/l): Nitrate concentration.CCME_Values: Calculated water quality index values using the CCME WQI model.CCME_WQI: Water Quality Index classification based on CCME_Values.Data Directory Description:Category 1: DatasetCombined Data: This folder contains two CSV files: Combined_dataset.csv and Summary.xlsx. The Combined_dataset.csv file includes all eight water quality parameter readings across five countries, with additional data for initial preprocessing steps like missing value handling, outlier detection, and other operations. It also contains the CCME Water Quality Index calculation for empirical analysis and ML-based research. The Summary.xlsx provides a brief description of the datasets, including data distributions (e.g., maximum, minimum, mean, standard deviation).Combined_dataset.csvSummary.xlsxCountry-wise Data: This folder contains separate country-based datasets in CSV files. Each file includes the eight water quality parameters for regional analysis. The Summary_country.xlsx file presents country-wise dataset descriptions with data distributions (e.g., maximum, minimum, mean, standard deviation).England_dataset.csvCanada_dataset.csvUSA_dataset.csvIreland_dataset.csvChina_dataset.csvSummary_country.xlsxCategory 2: CodeData processing and harmonization code (e.g., Language Conversion, Date Conversion, Parameter Naming and Unit Conversion, Missing Value Handling, WQI Measurement and Classification).Data_Processing_Harmonnization.ipynbThe code used for Technical Validation (e.g., assessing the Data Distribution, Outlier Detection, Water Quality Trend Analysis, and Vrifying the Application of the Dataset for the ML Models).Technical_Validation.ipynbCategory 3: Data Collection SourcesThis category includes links to the selected dataset sources, which were used to create the dataset and are provided for further reconstruction or data formation. It contains links to various data collection sources.DataCollectionSources.xlsxOriginal Paper Title: A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted ResearchAbstractAssessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.Note: Cite this repository and the original paper when using this dataset.

(please upvote if you found this useful) Water is a fundamental resource for life, and its quality is a critical factor for public health, environmental sustainability, and economic development. In a country as vast and diverse as India, monitoring the quality of water bodies is a monumental yet essential task. This dataset provides a valuable snapshot of the state of various water bodies across India, offering insights into the levels of different pollutants and key water quality parameters.

The data is sourced from the Central Pollution Control Board (CPCB) of India, which is the national organization responsible for the prevention and control of water and air pollution. This dataset is a part of their ongoing efforts to monitor the health of India's water resources under the National Water Quality Monitoring Programme (NWMP). Excellent! Here's a well-structured description for your Kaggle dataset, incorporating the context, content, and your inspiration. You can copy and paste this directly into the description section on Kaggle.

Context Water is a fundamental resource for life, and its quality is a critical factor for public health, environmental sustainability, and economic development. In a country as vast and diverse as India, monitoring the quality of water bodies is a monumental yet essential task. This dataset provides a valuable snapshot of the state of various water bodies across India, offering insights into the levels of different pollutants and key water quality parameters.

The data is sourced from the Central Pollution Control Board (CPCB) of India, which is the national organization responsible for the prevention and control of water and air pollution. This dataset is a part of their ongoing efforts to monitor the health of India's water resources under the National Water Quality Monitoring Programme (NWMP).

Content This dataset contains water quality data collected from various monitoring stations across 17 different states in India between the years 2021 and 2023. The data covers different types of water bodies, primarily Rivers and Drains.

The dataset includes a rich set of parameters that are crucial for assessing water quality, including:

Physical Parameters: Temperature

Chemical Parameters: pH, Conductivity, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and Nitrate-N

Biological Parameters: Fecal Coliform and Total Coliform

Each row in the dataset represents a specific monitoring location and provides the minimum and maximum recorded values for these parameters over a given year.

Inspiration and Acknowledgements This dataset was compiled out of a need for comprehensive and recent water quality data for a project focused on water quality analysis and building a recommendation system. The goal is to make this valuable data more accessible to the data science community for analysis, visualization, and the development of predictive models.

These data were collected as part of the Great Lakes Restoration Initiative (GLRI) project template 678-1 entitled "Evaluate immediate and long-term BMP effectiveness of GLRI restoration efforts at urban beaches on Southern and Western Lake Michigan". This project is evaluating the effectiveness of projects that are closely associated with restoration of local habitat and contact recreational activities at two GLRI funded sites in Southern Lake Michigan and one non-GLRI site in Western Lake Michigan. Evaluation of GLRI projects will assess whether goals of recipients are on track and identify any developing unforeseen consequences. Including a third, non-GLRI project site in the evaluation allows comparison between restoration efforts in GLRI and non-GLRI funded projects. Projections and potential complications associated with climate change impacts on restoration resiliency are also being assessed. Two of the three sites to receive evaluation represent some of the most highly contaminated beaches in the United States and include restoration BMPs which could benefit urban beaches and nearshore areas throughout the Great Lakes. The urban beaches chosen for evaluation are at various stages of the restoration process and located in Indiana (Jeorse Park Beach), Illinois (63rd Street Beach), and Wisconsin (North Beach). Evaluation of effectiveness of restoration efforts and resiliency to climate change at urban beaches will provide vital information on the success of restoration efforts and identify potential pitfalls that will help maximize success of future GLRI beach and nearshore restoration projects. Data used for evaluation include continuous monitoring and synoptic mapping of nearshore currents, bathymetry, and water quality to examine nearshore transport under a variety of conditions. In addition, biological evaluations rely upon daily indicator bacteria monitoring, microbial community and shorebird surveys, recreational usage, and other ancillary water quality data. The pre- and post-restoration datasets comprised of these physical, chemical, biological, geological, and social data will allow restoration success to be evaluated using a science-based approach with quantifiable measures of progress. These data will also allow the evaluation of the resiliency of these restoration efforts under various climate change scenarios using existing climate change predictions and models. This data release is comprised of three-dimensional point measurements of basic water-quality parameters in coastal Lake Michigan at 63rd Street Beach near Chicago, Illinois, on September 22, 2016. Water-quality parameters include temperature, specific conductance, pH, dissolved oxygen, turbidity, total chlorophyll, and phycocyanin concentration. These data were collected using a YSI EcoMapper autonomous underwater vehicle (AUV) equipped with a YSI 6600 V2-4 bulkhead housing a YSI 6560FR fast response temperature/conductivity probe, YSI 6589FR fast response pH sensor, YSI 6150 ROX optical dissolved oxygen sensor, YSI 6136 turbidity sensor, YSI 6025 chlorophyll sensor, and YSI 6131 BGA-PC phycocyanin (blue-green algae) sensor. All parameters were sampled at 1-second intervals as the AUV completed the pre-programmed survey pattern of the nearshore zone. The AUV was programmed to continually undulate between the water surface and 4 feet above the bottom (dive angle of 15 degrees) as it moved at 2 knots between programmed waypoints along it survey mission path. The resulting dataset allows for analysis of the three-dimensional distributions of water-quality parameters in Lake Michigan at 63rd Street Beach.

Water Quality Data of Indian Lakes from 2017 - 2022 under the NWMP program. Data is from the CPCB government website : https://cpcb.nic.in/nwmp-data/. Data is extracted from pdf for Water Quality Data for Lakes, Ponds, Tanks and Wetlands

List of parameters 1. Dissolved Oxygen 2. pH 3. Conductivity 4. Biological Oxygen Demand 5. Nitrates 6. Fecal Coliform 7. Total Coliform