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.

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.

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.

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.

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

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.

Bộ dữ liệu bao gồm các dữ liệu hình ảnh được ghép cặp và gán nhãn từ bộ dữ liệu Vietnamese Celebrity Faces với 1 là cùng người và 0 là không cùng người. Folder test bao gồm dữ liệu của 100 khuôn mặt từ bộ dữ liệu Vietnamese Celebrity Faces.

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.

Structured comparison data for Industrial Coating Systems

The displayed data on the ways consumers compare personal luxury items shows the results of a Statista survey conducted in the United States in 2018. Some ** percent of respondents stated that they compared prices of personal luxury items.The Survey Data Table for the Statista survey Luxury in the United States 2018 contains the complete tables for the survey including various column headings.

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.

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.

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results – and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.

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

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored. Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions. Methods Distance measure test results were produced using R and can be replicated using scripts available on GitHub at https://github.com/shawndove/Trend_compare. Detailed information on wading bird trends can be found in Jellesmark et al. (2021) below. Jellesmark, S., Ausden, M., Blackburn, T. M., Gregory, R. D., Hoffmann, M., Massimino, D., McRae, L., & Visconti, P. (2021). A counterfactual approach to measure the impact of wet grassland conservation on U.K. breeding bird populations. Conservation Biology, 35(5), 1575–1585. https://doi.org/10.1111/cobi.13692

The Veterans Health Administration (VHA) has now collaborated with the Centers for Medicare & Medicaid Services (CMS) to present information to consumers about the quality and safety of health care in VHA. VHA has approximately 50 percent of Veterans enrolled in the healthcare system who are eligible for Medicare and, therefore, have some choice in how and where they receive inpatient services. VHA has adopted healthcare transparency as a strategy to enhance public trust and to help Veterans make informed choices about their health care.VHA currently reports the following types of quality measures on Hospital Compare:Timely and effective care.Behavioral health.Readmissions and deaths.Patient safety.*Experience of care.

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.

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

Blockchain data query: compare number of holders