Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

In our everyday lives, we are required to make decisions based upon our statistical intuitions. Often, these involve the comparison of two groups, such as luxury versus family cars and their suitability. Research has shown that the mean difference affects judgements where two sets of data are compared, but the variability of the data has only a minor influence, if any at all. However, prior research has tended to present raw data as simple lists of values. Here, we investigated whether displaying data visually, in the form of parallel dot plots, would lead viewers to incorporate variability information. In Experiment 1, we asked a large sample of people to compare two fictional groups (children who drank ‘Brain Juice’ versus water) in a one-shot design, where only a single comparison was made. Our results confirmed that only the mean difference between the groups predicted subsequent judgements of how much they differed, in line with previous work using lists of numbers. In Experiment 2, we asked each participant to make multiple comparisons, with both the mean difference and the pooled standard deviation varying across data sets they were shown. Here, we found that both sources of information were correctly incorporated when making responses. Taken together, we suggest that increasing the salience of variability information, through manipulating this factor across items seen, encourages viewers to consider this in their judgements. Such findings may have useful applications for best practices when teaching difficult concepts like sampling variation.

In situations where the cost/benefit analysis of using physics-based damage propagation algorithms is not favorable and when sufficient test data are available that map out the damage space, one can employ data-driven approaches. In this investigation, we evaluate different algorithms for their suitability in those circumstances. We are interested in assessing the trade-off that arises from the ability to support uncertainty management, and the accuracy of the predictions. We compare here a Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), and a Neural Network-based approach and employ them on relatively sparse training sets with very high noise content. Results show that while all methods can provide remaining life estimates although different damage estimates of the data (diagnostic output) changes the outcome considerably. In addition, we found that there is a need for performance metrics that provide a comprehensive and objective assessment of prognostics algorithm performance.

K-12 and higher education - enrollment, graduates, expenditures, institutions.

This online application gives manufacturers the ability to compare Iowa to other states on a number of different topics including: business climate, education, operating costs, quality of life and workforce.

File Comparison Tool Market Outlook

According to our latest research, the global file comparison tool market size reached USD 1.72 billion in 2024, driven by the increasing demand for efficient data management and version control across diverse industries. The market is exhibiting a robust growth trajectory, registering a CAGR of 11.4% from 2025 to 2033. By the end of 2033, the file comparison tool market is forecasted to attain a value of USD 4.55 billion. This growth is primarily fueled by the rapid digital transformation initiatives, increasing adoption of cloud-based solutions, and the necessity for sophisticated tools to manage and compare large volumes of data and code in real-time.

The burgeoning need for data accuracy and integrity across enterprises is a significant growth factor for the file comparison tool market. Organizations are increasingly relying on these tools to identify discrepancies, ensure compliance, and streamline workflows, especially as digital data volumes surge exponentially. In sectors such as BFSI and healthcare, where data consistency and regulatory compliance are paramount, file comparison tools are becoming indispensable. These tools not only enhance operational efficiency by automating tedious manual comparison tasks but also reduce the risk of human error, thereby safeguarding critical business information. As data-driven decision-making becomes central to business strategies, the demand for advanced file comparison solutions is expected to escalate further.

Another crucial driver propelling the file comparison tool market is the proliferation of software development and DevOps practices across enterprises. With the rise of agile methodologies, continuous integration, and deployment pipelines, developers and IT professionals require robust solutions to compare code, track changes, and manage multiple versions seamlessly. File comparison tools enable teams to collaborate efficiently, resolve conflicts, and maintain code quality throughout the software development lifecycle. As organizations increasingly embrace digital transformation, the integration of file comparison tools with popular version control systems and cloud platforms is becoming a standard practice, further amplifying market growth.

The growing complexity of enterprise IT environments is also contributing to the expansion of the file comparison tool market. As organizations adopt hybrid and multi-cloud strategies, the need for tools that can operate across heterogeneous systems and platforms is more pronounced than ever. File comparison tools equipped with AI and machine learning capabilities are gaining traction, offering intelligent insights and automated recommendations for resolving data and code discrepancies. The ongoing emphasis on cybersecurity and data governance further underscores the importance of these tools in detecting unauthorized changes, ensuring data integrity, and mitigating risks associated with data breaches or compliance violations.

The increasing complexity of software development environments has led to the emergence of innovative solutions like Code Merge Conflict Prediction AI. This technology leverages artificial intelligence to predict potential conflicts in code merges before they occur, thereby enhancing the efficiency of development workflows. By analyzing patterns in code changes and developer interactions, this AI-driven tool can provide early warnings and recommendations to resolve conflicts proactively. This not only saves valuable time but also helps maintain code quality and reduces the risk of errors in production. As more organizations adopt agile and DevOps practices, the integration of Code Merge Conflict Prediction AI into file comparison tools is becoming a key differentiator, enabling teams to collaborate more effectively and deliver high-quality software faster.

From a regional perspective, North America continues to dominate the file comparison tool market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of leading technology companies, early adoption of advanced IT solutions, and stringent regulatory frameworks have positioned North America at the forefront of market growth. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, expanding IT infrastructures, and increasing investm

This data package contains information about hospital readmission and deaths as well as hospital excess readmission reduction program. It also includes data over hospital value based purchasing program for years 2017 and 2018. It comprises of datasets about readmission rates by age, gender, patient residence, payer, zip code and median income.

Statistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually independent time series, and empirical results for dependent time series are only limited to comparing two time series. We propose scalable methods based on a new algorithm that enables us to compare the spectral density of a large number of time series. The new algorithm helps us efficiently obtain all pairwise feature differences in frequency patterns between M time series, which plays an essential role in our methods. When all M time series are independent of each other, we derive the joint asymptotic distribution of their pairwise feature differences. The asymptotic dependence structure between the feature differences motivates our proposed test for multiple mutually independent time series. We then adapt this test to the case of multiple dependent time series by partially accounting for the underlying dependence structure. Additionally, we introduce a global test to further enhance the approach. To examine the finite sample performance of our proposed methods, we conduct simulation studies. The new approaches demonstrate the ability to compare a large number of time series, whether independent or dependent, while exhibiting competitive power. Finally, we apply our methods to compare multiple mechanical vibrational time series.

This data package contains information about Measures of Rehospitalization, Emergency Visit and Community Discharge for Medicare Beneficiaries. It also includes Nursing Home Compare information on Deficiencies, Fire Safety Deficiencies, MDS Quality Measures, Ownership information, Fines and Payment denial, Provider Information, State Averages and Survey Summary information about nursing homes.

The dataset contains information on Physician Compare Clinician Utilization Data prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code and place of service.

This data package contains information regarding different hospitals and their quality of surgical outcomes and structural measures. It includes datasets over facility, national and state-level data for Inpatient Psychiatric Hospital Facility Quality Reporting (IPFQR) and payment measures. It also provides Timely and Effective Care information by national and state-level data for measures of heart attack care, heart failure care, pneumonia care, surgical care and emergency department care.

BackgroundIndividual participant data (IPD) meta-analyses that obtain “raw” data from studies rather than summary data typically adopt a “two-stage” approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of “one-stage” approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare “two-stage” and “one-stage” models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way. Methods and FindingsWe included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model. ConclusionsFor these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

o compare Austin’s diversion methodology and goals to those of its peers, Burns & McDonnell collected data from 13 benchmark cities regarding diversion calculation methods, recyclables processing contract terms, and policy implementation. Based on analysis on this compiled data, Burns & McDonnell determined various key findings based on a preliminary comparison, and comparisons of diversion material type considerations, methodology and policy considerations, and effective programming.

This data package contains information about the Ambulatory Surgical Center Quality Reporting (ASCQR) Program by facility as well as national and state level health care data. It also provides information regarding the hospital, national and state data for the Outpatient Imaging Efficiency Core Measures.

Lists the data updates for a scheduled quarterly refresh and as well those that are updated in between refreshes.

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

Best virtual data rooms 2024 dataset is created to provide the data room users and M&A specialists with detailed information on the best virtual data rooms. The dataset contains the descriptions of each dataroom solution and their ratings.

Innovation - R&D funding, research awards, and patents.

CSV Diff Tool Market Outlook

As per our latest research, the global CSV Diff Tool market size reached USD 412.5 million in 2024, driven by the increasing need for efficient data comparison and management across various industries. The market is witnessing a robust growth trajectory, with a recorded CAGR of 13.7% from 2025 to 2033. By 2033, the market is forecasted to reach USD 1,210.8 million. This significant expansion is attributed to the rising adoption of digital transformation initiatives, the proliferation of big data analytics, and the growing demand for accurate data auditing and migration solutions across enterprises and institutions worldwide.

One of the primary growth factors propelling the CSV Diff Tool market is the surge in data-driven decision-making across all major industry verticals. Organizations are increasingly relying on structured data stored in CSV files for critical operations, making the ability to efficiently compare and manage these files essential. The proliferation of big data and the need for real-time analytics have made CSV Diff Tools indispensable for ensuring data integrity, especially during data migration, integration, and auditing processes. Furthermore, the growing emphasis on regulatory compliance and data accuracy in sectors such as finance, healthcare, and government is driving the adoption of advanced CSV Diff Tools that can seamlessly identify discrepancies and facilitate error-free data handling.

Another notable driver is the rapid shift towards cloud-based solutions, which has revolutionized the deployment and scalability of CSV Diff Tools. Cloud-based platforms offer enhanced accessibility, flexibility, and collaboration capabilities, enabling geographically dispersed teams to efficiently compare and synchronize large datasets. This trend is particularly evident among small and medium-sized enterprises (SMEs), which are increasingly leveraging cloud-based CSV Diff Tools to streamline their data management workflows without the need for significant upfront infrastructure investments. Additionally, the integration of artificial intelligence and machine learning algorithms into CSV Diff Tools is enhancing their capability to detect complex data patterns and anomalies, further boosting market growth.

The CSV Diff Tool market is also benefiting from the expanding application landscape, which now spans data analysis, version control, data migration, and auditing. As organizations continue to digitize their operations, the volume and complexity of data being handled are increasing exponentially. This has created a pressing need for robust tools that can not only compare CSV files but also provide actionable insights and automate routine data reconciliation tasks. The emergence of user-friendly interfaces and customizable features is making CSV Diff Tools accessible to a broader range of users, from IT professionals to business analysts, thereby accelerating market penetration.

Regionally, North America dominates the CSV Diff Tool market, accounting for the largest share due to its advanced IT infrastructure, high adoption of cloud technologies, and strong presence of leading technology vendors. Europe follows closely, driven by stringent data protection regulations and a growing emphasis on data quality management. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, increasing investments in IT, and the proliferation of SMEs adopting advanced data management solutions. Latin America and the Middle East & Africa are also witnessing steady growth, supported by ongoing digital transformation initiatives and the rising need for efficient data comparison tools in government and educational sectors.

Component Analysis

The CSV Diff Tool market is segmented by component into software and services, each playing a distinct role in shaping market dynamics. The software segment represents the core of the market, encompassing standalone applications and integrated solutions designed to automate and streamline the comparison of CSV files. These software solutions are increasingly being enhanced with advanced functionalities such as real-time synchronization, customizable comparison algorithms, and integration capabilities with popular data management platforms. The demand for intuitive and feature-rich software is particularly high among enterprises seeking to improve data accuracy, reduce manual effort, and facilitate seamless data migration across dispa

This is the source data used in Nature Energy manuscript to produce visualisations, authored by Yuwan Malakar and Rosie Day. This manuscript is designed to compare women's perspectives of the relationships between their wellbeing and cooking fuels. they use. The study was conducted in rural India. Qualitative data generated from focus group discussions is used for the analysis. The data was collected from November 2016 to February 2017. Lineage: This data was produced via R codes. The source data are in the *.csv format.