The global data validation services market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period. Growing concerns over data inaccuracy and the increasing volume of data being generated by organizations are the key factors driving the market growth. Additionally, the adoption of cloud-based data validation solutions is expected to further fuel the market expansion. North America and Europe are the largest markets for data validation services, with a significant presence of large enterprises and stringent data regulations. The market is fragmented with several established players and a number of emerging vendors offering specialized solutions. Key market participants include TELUS Digital, Experian Data Quality, Flatworld Solutions Inc., Precisely, LDC, InfoCleanse, Level Data, Damco Solutions, Environmental Data Validation Inc., DataCaptive, Process Fusion, Ann Arbor Technical Services, Inc., and others. These companies are focusing on expanding their geographical reach, developing new products and features, and offering value-added services to gain a competitive edge in the market. The growing demand for data privacy and security solutions is also expected to drive the adoption of data validation services in the coming years.

This dataset includes the MIPS Data Validation Criteria. The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) streamlines a patchwork collection of programs with a single system where provider can be rewarded for better care. Providers will be able to practice as they always have, but they may receive higher Medicare payments based on their performance.

Three indicators reported by the ASSIST project for its Zika activities in the Dominican Republic were evaluated using the following approaches : 1) external Evaluators re-assessed the same patient’s records that were originally reviewed by facility quality improvement teams ; 2) external evaluators selected a new systematic random sample of records; and 3) external evaluators tallied totals for the indicators of interest from facility registers to determine differences between indicator values reported by the USAID ASSIST Project and the values for the universe of clients seen at these facilities.

Data for method validation on detecting pmp-glucose by HPLC

WeatherLogistics has 10-years experience in climate data science. Its previous validation solutions include an inter-comparison assessment of numerical weather prediction models, assessment of meteorological data against soil moisture measurements, and pioneering research and development of a seasonal climate forecast system. More recently, its team developed software to score GFS and ECMWF products on a decentralised climate data marketplace.

The BATS (Bermuda Atlantic Time-series Study) discrete HPLC pigment validation dataset is time-series spanning from 1996 to 2022. The dataset contains the 21 separate in-situ pigment measurements along with sampling depth and the BATS Cruise ID.

This description has been reproduced using https://www.dropbox.com/s/6ajl545hyua8ot8/bval_pigments.txt?dl=0

The Data Validation Services market is experiencing robust growth, driven by the increasing reliance on data-driven decision-making across various industries. The market's expansion is fueled by several key factors, including the rising volume and complexity of data, stringent regulatory compliance requirements (like GDPR and CCPA), and the growing need for data quality assurance to mitigate risks associated with inaccurate or incomplete data. Businesses are increasingly investing in data validation services to ensure data accuracy, consistency, and reliability, ultimately leading to improved operational efficiency, better business outcomes, and enhanced customer experience. The market is segmented by service type (data cleansing, data matching, data profiling, etc.), deployment model (cloud, on-premise), and industry vertical (healthcare, finance, retail, etc.). While the exact market size in 2025 is unavailable, a reasonable estimation, considering typical growth rates in the technology sector and the increasing demand for data validation solutions, could be placed in the range of $15-20 billion USD. This estimate assumes a conservative CAGR of 12-15% based on the overall IT services market growth and the specific needs for data quality assurance. The forecast period of 2025-2033 suggests continued strong expansion, primarily driven by the adoption of advanced technologies like AI and machine learning in data validation processes. Competitive dynamics within the Data Validation Services market are characterized by the presence of both established players and emerging niche providers. Established firms like TELUS Digital and Experian Data Quality leverage their extensive experience and existing customer bases to maintain a significant market share. However, specialized companies like InfoCleanse and Level Data are also gaining traction by offering innovative solutions tailored to specific industry needs. The market is witnessing increased mergers and acquisitions, reflecting the strategic importance of data validation capabilities for businesses aiming to enhance their data management strategies. Furthermore, the market is expected to see further consolidation as larger players acquire smaller firms with specialized expertise. Geographic expansion remains a key growth strategy, with companies targeting emerging markets with high growth potential in data-driven industries. This makes data validation a lucrative market for both established and emerging players.

The global data validation services market is anticipated to grow exponentially over the coming years. The market is projected to reach a value of USD 25.47 billion by 2033, expanding at a CAGR of 14.2% from 2025 to 2033. The increasing volume of data, growing need for data accuracy, and stringent regulatory compliance are major drivers fueling the market growth. Moreover, the adoption of cloud-based data validation solutions, growing adoption of AI and ML technologies, and increasing investments in data governance initiatives are anticipated to create lucrative opportunities for market players. The market is segmented based on type, application, enterprise size, and region. The cloud-based segment is expected to hold the largest market share due to its scalability, cost-effectiveness, and accessibility. The SMEs segment is projected to grow at a higher CAGR, driven by the increasing adoption of data validation solutions among small and medium-sized businesses. The North American region is anticipated to dominate the market, followed by Europe and Asia Pacific. Key market players include TELUS Digital, Experian Data Quality, Flatworld Solutions Inc., Precisely, LDC, InfoCleanse, Level Data, Damco Solutions, Environmental Data Validation Inc., DataCaptive, Process Fusion, Ann Arbor Technical Services, Inc., among others.

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the map feature extraction challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

dfap/df-translate-data-validation dataset hosted on Hugging Face and contributed by the HF Datasets community

Steady-state experimental validation data collected from Newcastle University's S100 research flume, studying the effect of partial barriers to flow.

This Data contains the PEN-Predictor-Keras-Model as well as the 100 validation data sets.

Scientists and engineers from the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) Cal/Val Center of Excellence (ECCOE) collected in situ measurements using field spectrometers to support the validation of surface reflectance products derived from Earth observing remote sensing imagery. Data provided in this data release were collected during select Earth observing satellite overpasses during the months of March through November 2024 at the USGS EROS facility in Minnehaha County, South Dakota. Each field collection file includes the calculated surface reflectance of each wavelength collected using a dual field spectrometer methodology. The dual field spectrometer methodology allows for the calculated surface reflectance of each wavelength to be computed using one or both of the spectrometers. The use of the dual field spectrometers system reduces uncertainty in the field measurements by accounting for changes in solar irradiance. Both single and dual spectrometer calculated surface reflectance are included with this dataset. The differing methodologies of the calculated surface reflectance data are denoted as "Single Spectrometer" and "Dual Spectrometer". Field spectrometer data are provided as Comma Separated Values (CSV) files and GeoPackage files.

Cross-validation is one of the most popular model and tuning parameter selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, due to the ignorance of the uncertainty in the testing sample. We develop a novel statistically principled inference tool based on cross-validation that takes into account the uncertainty in the testing sample. This method outputs a set of highly competitive candidate models containing the optimal one with guaranteed probability. As a consequence, our method can achieve consistent variable selection in a classical linear regression setting, for which existing cross-validation methods require unconventional split ratios. When used for tuning parameter selection, the method can provide an alternative trade-off between prediction accuracy and model interpretability than existing variants of cross-validation. We demonstrate the performance of the proposed method in several simulated and real data examples. Supplemental materials for this article can be found online.

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.

Validation to ensure data and identity integrity. DAS will also ensure security compliant standards are met.

This dataset consists of the synthetic electron backscatter diffraction (EBSD) maps generated for the paper, titled "Hybrid Algorithm for Filling in Missing Data in Electron Backscatter Diffraction Maps" by Emmanuel Atindama, Conor Miller-Lynch, Huston Wilhite, Cody Mattice, Günay Doğan, and Prashant Athavale. The EBSD maps were used to train, test, and validate a neural network algorithm to fill in missing data points in a given EBSD map.The dataset includes 8000 maps for training, 1000 maps for testing, 2000 maps for validation. The dataset also includes noise-added versions of the maps, namely, one more map per each clean map.

The data use for validating our model

This dataset provides a common validation set for estimates of gross primary productivity. This data represents a subset of all predictions made in the model inputs and outputs that were converted to GPP based on a light use efficiency. The data was subsetted for only days were all light estimates could be produced. This dataset is part of a larger data release of inputs and outputs from a model to predict light at the stream surface and within the water column for 173 streams and rivers across the continental United States. The complete release contains model input data, modeled estimates of light at the stream surface and within the water column, and modeled estimates of gross primary productivity.

once it is accepted.