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.

This data package contains quality measures such as Air Quality, Austin Airport, LBB Performance Report, School Survey, Child Poverty, System International Units, Weight Measures, etc.

This repository contains the raw data and analysis scripts supporting the associated publication which introduces a framework to help researchers select fit-for-purpose microbial cell counting methods and optimize protocols for quantification of microbial total cells and viable cells. Escherichia coli cells were enumerated using four methods (colony forming unit assay, impedance flow cytometry - Multisizer 4, impedance flow cytometry - BactoBox, and fluorescent flow cytometry - CytoFLEX LX) and repeated on multiple dates. The experimental design for a single date starts with a cell stock that is divided into 18 sample replicates (3 each for 6 different dilution factors), and each sample is assayed one or two times for a total of 30 observations. Raw data files are provided from the Multisizer 4 (.#m4) and CytoFLEX LX (.fcs 3.0). The colony forming unit assay and BactoBox readings are recorded for each date as are the derived results from the Multisizer 4 and CytoFLEX LX. Also provided are an example analysis script for the *.fcs files and the statistical analysis that was performed.

Illustration of how quality flags and quality metrics are determined for a data product with n measurements and f quality flags.

Data quality metrics and data sources reviewed.

Home Health Care Agency Ratings

Quality Measurements, Types of Services and More

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides a list of all Home Health Agencies registered with Medicare. Contained within this dataset is information on each agency's address, phone number, type of ownership, quality measure ratings and other associated data points. With this valuable insight into the operations of each Home Health Care Agency, you can make informed decisions about your care needs. Learn more about the services offered at each agency and how they are rated according to their quality measure ratings. From dedicated nursing care services to speech pathology to medical social services - get all the information you need with this comprehensive look at U.S.-based Home Health Care Agencies!

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How to use the dataset

Are you looking to learn more about Home Health Care Agencies registered with Medicare? This dataset can provide quality measure ratings, addresses, phone numbers, types of services offered and other information that may be helpful when researching Home Health Care Agencies.

This guide will explain how to use the data in this dataset to gain a better understanding of Home Health Care Agencies registered with Medicare.

First, you will need to become familiar with the columns in the dataset. A list of all columns and their associated descriptions is provided above for your reference. Once you understand each column’s purpose, it will be easier for you to decide what metrics or variables are most important for your own research.

Next, use this data to compare various facets between different Home Health Care Agencies such as type of ownership, services offered and quality measure ratings like star rating or CMS certification number (from 0-5 stars). Collecting information from multiple sources such as public reviews or customer feedback can help supplement these numerical metrics in order to paint a more accurate picture about each agency's performance and customer satisfaction level.

Finally once you have collected enough data points on one particular agency or a comparison between multiple agencies then conduct more analysis using statistical methods like correlation matrices in order to determine any patterns that exist within the data set which may reveal valuable insights into topic of research at hand

Research Ideas

• Using the data to compare quality of care ratings between agencies, so people can make better informed decisions about which agency to hire for home health services.
• Analyzing the costs associated with different types of home health care services, such as nursing care and physical therapy, in order to determine where money could be saved in health care budgets.
• Evaluating the performance of certain agencies by analyzing the number of episodes billed to Medicare compared to their national averages, allowing agencies with lower numbers of billing episodes to be identified and monitored more closely if necessary

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:----------------------------------------...

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.

Water quality measurements taken in the Great Lakes region of the United States.

This is the raw pollutant data collected on August 6, 2014 in the greater Denver, Colorado area by three mobile air pollution platforms. Data was collected by Aclima, Inc. This is the raw data used to explore different statistical methods for assessing platform performance and comparability in the publication "Uncertainty in collocated mobile measurements of air quality" by Andrew R. Whitehill et al., 2020.

This data set presents annual enrollment counts of Medicaid and CHIP beneficiaries by program type (Medicaid or CHIP). There are three metrics presented: (1) the number of beneficiaries ever enrolled in each program type over the year (duplicated count); (2) the number of beneficiaries enrolled in each program type as of an individual’s last month of enrollment (unduplicated count); and (3) average monthly enrollment in each program type.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues, making the data unusable for calculating these measures. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state and year are considered unusable or of high concern based on DQ Atlas thresholds for the topics Medicaid-only enrollment and M-CHIP and S-CHIP Enrollment. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

The tabular data sets and associated maps in this data release represent water-quality data that were collected between April and November of 2017 and between July and November of 2019 to describe baseline conditions prior to or sometimes following treatments using herbicides or other methods to reduce the biomass of non-native water primrose (Ludwigia) within off-channel water bodies of the Willamette River near Albany and Keizer, Oregon. The water-quality parameters measured in this study included water temperature, specific conductance, pH, dissolved oxygen, turbidity, total chlorophyll, phycocyanin (blue-green algae pigment), and fluorescing dissolved organic matter in surface water.

ObjectiveMedical record abstraction (MRA) is often cited as a significant source of error in research data, yet MRA methodology has rarely been the subject of investigation. Lack of a common framework has hindered application of the extant literature in practice, and, until now, there were no evidence-based guidelines for ensuring data quality in MRA. We aimed to identify the factors affecting the accuracy of data abstracted from medical records and to generate a framework for data quality assurance and control in MRA.MethodsCandidate factors were identified from published reports of MRA. Content validity of the top candidate factors was assessed via a four-round two-group Delphi process with expert abstractors with experience in clinical research, registries, and quality improvement. The resulting coded factors were categorized into a control theory-based framework of MRA. Coverage of the framework was evaluated using the recent published literature.ResultsAnalysis of the identified articles yielded 292 unique factors that affect the accuracy of abstracted data. Delphi processes overall refuted three of the top factors identified from the literature based on importance and five based on reliability (six total factors refuted). Four new factors were identified by the Delphi. The generated framework demonstrated comprehensive coverage. Significant underreporting of MRA methodology in recent studies was discovered.ConclusionThe framework generated from this research provides a guide for planning data quality assurance and control for studies using MRA. The large number and variability of factors indicate that while prospective quality assurance likely increases the accuracy of abstracted data, monitoring the accuracy during the abstraction process is also required. Recent studies reporting research results based on MRA rarely reported data quality assurance or control measures, and even less frequently reported data quality metrics with research results. Given the demonstrated variability, these methods and measures should be reported with research results.

Spectroscopic data for measurements of carbon quality for adaptation experiment.

The dataset was originally published in DiVA and moved to SND in 2024.

Analyzing metabolites using mass spectrometry provides valuable insight into an individual’s health or disease status. However, various sources of experimental variation can be introduced during sample handling, preparation, and measurement, which can negatively affect the data. Quality assurance and quality control practices are essential to ensuring accurate and reproducible metabolomics data. These practices include measuring reference samples to monitor instrument stability, blank samples to evaluate the background signal, and strategies to correct for changes in instrumental performance. In this context, we introduce mzQuality, a user-friendly, open-source R-Shiny app designed to assess and correct technical variations in mass spectrometry-based metabolomics data. It processes peak-integrated data independently of vendor software and provides essential quality control features, including batch correction, outlier detection, and background signal assessment, and it visualizes trends in signal or retention time. We demonstrate its functionality using a data set of 419 samples measured across six batches, including quality control samples. mzQuality visualizes data through sample plots, PCA plots, and violin plots, which illustrate its ability to reduce the effect of experiment variation. Compound quality is further assessed by evaluating the relative standard deviation of quality control samples and the background signal from blank samples. Based on these quality metrics, compounds are classified into confidence levels. mzQuality provides an accessible solution to improve the data quality without requiring prior programming skills. Its customizable settings integrate seamlessly into research workflows, enhancing the accuracy and reproducibility of the metabolomics data. Additionally, with an R-compatible output, the data are ready for statistical analysis and biological interpretation.

This data set presents annual enrollment counts of Medicaid and CHIP beneficiaries by managed care participation (comprehensive managed care, primary care case management, MLTSS, including PACE, behavioral health organizations, nonmedical prepaid health plans, medical-only prepaid health plans, and other). There are three metrics presented: (1) the number of beneficiaries ever enrolled in each managed care plan type over the year (duplicated count); (2) the number of beneficiaries enrolled in each managed care plan type as of an individual’s last month of enrollment (duplicated count); and (3) average monthly enrollment in each managed care plan type.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some cells have a value of “DS”. Some states have serious data quality issues, making the data unusable for calculating these measures. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state and year are considered unusable or of high concern based on DQ Atlas thresholds for the topics Enrollment in CMC, Enrollment in PCCM Programs, and Enrollment in BHO Plans. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

This data set includes monthly enrollment counts of Medicaid and CHIP beneficiaries by program type (Medicaid or CHIP).

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating these measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable or of high concern based on DQ Atlas thresholds for the topics Medicaid-only Enrollment and M-CHIP and S-CHIP Enrollment. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Values are presented as number of studies reporting (%).* Category includes validation of administrative data, performance measures, or indicators (18); data quality assessment (11); and questionnaire validation (1).Frequency of reporting MRA methods.

Data Quality Scorecards Market Outlook

According to our latest research, the global Data Quality Scorecards market size in 2024 stands at USD 1.42 billion, reflecting robust demand across diverse sectors. The market is projected to expand at a CAGR of 14.8% from 2025 to 2033, reaching an estimated USD 4.45 billion by the end of the forecast period. Key growth drivers include the escalating need for reliable data-driven decision-making, stringent regulatory compliance requirements, and the proliferation of digital transformation initiatives across enterprises of all sizes. As per our latest research, organizations are increasingly recognizing the significance of maintaining high data quality standards to fuel analytics, artificial intelligence, and business intelligence capabilities.

One of the primary growth factors for the Data Quality Scorecards market is the exponential rise in data volumes generated by organizations worldwide. The digital economy has led to a surge in data collection from various sources, including customer interactions, IoT devices, and transactional systems. This data explosion has heightened the complexity of managing and ensuring data accuracy, completeness, and consistency. As a result, businesses are investing in comprehensive data quality management solutions, such as scorecards, to monitor, measure, and improve the quality of their data assets. These tools provide actionable insights, enabling organizations to proactively address data quality issues and maintain data integrity across their operations. The growing reliance on advanced analytics and artificial intelligence further amplifies the demand for high-quality data, making data quality scorecards an indispensable component of modern data management strategies.

Another significant growth driver is the increasing regulatory scrutiny and compliance requirements imposed on organizations, particularly in industries such as BFSI, healthcare, and government. Regulatory frameworks such as GDPR, HIPAA, and CCPA mandate stringent controls over data accuracy, privacy, and security. Non-compliance can result in severe financial penalties and reputational damage, compelling organizations to adopt robust data quality management practices. Data quality scorecards help organizations monitor compliance by providing real-time visibility into data quality metrics and highlighting areas that require remediation. This proactive approach to compliance not only mitigates regulatory risks but also enhances stakeholder trust and confidence in organizational data assets. The integration of data quality scorecards into enterprise data governance frameworks is becoming a best practice for organizations aiming to achieve continuous compliance and data excellence.

The rapid adoption of cloud computing and digital transformation initiatives across industries is also fueling the growth of the Data Quality Scorecards market. As organizations migrate their data infrastructure to the cloud and embrace hybrid IT environments, the complexity of managing data quality across disparate systems increases. Cloud-based data quality scorecards offer scalability, flexibility, and ease of deployment, making them an attractive option for organizations seeking to modernize their data management practices. Moreover, the proliferation of self-service analytics and business intelligence tools has democratized data access, necessitating robust data quality monitoring to ensure that decision-makers are working with accurate and reliable information. The convergence of cloud, AI, and data quality management is expected to create new opportunities for innovation and value creation in the market.

From a regional perspective, North America continues to dominate the Data Quality Scorecards market, driven by the presence of leading technology vendors, high adoption rates of advanced analytics, and stringent regulatory frameworks. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digitalization, increasing investments in IT infrastructure, and growing awareness of data quality management among enterprises. Europe also represents a significant market, characterized by strong regulatory compliance requirements and a mature data management ecosystem. Latin America and the Middle East & Africa are emerging markets, with increasing adoption of data quality solutions in sectors such as BFSI, healthcare, and government. The global market landscape is evolving rapidly, with regional

This data set includes monthly counts and rates (per 1,000 beneficiaries) of COVID-19 testing services provided to Medicaid and CHIP beneficiaries, by state.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating COVID-19 testing services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Procedure Codes - OT Professional, Claims Volume - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.