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.

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.

This project provides a data access file for the following study.What Matters When? Social and Dimensional Comparisons in the Context of University Major ChoiceStudents compare their achievement to different standards in order to evaluate their ability. We built upon the theoretical frameworks of situated expectancy-value theory, dimensional comparison theory, and the big-fish-little-pond effect literature to examine the role of social and dimensional comparisons for ability self-concept and subjective task value (STV) in secondary school and university major choice. We used two German longitudinal datasets from different cohorts with data collection in 12th grade and 2 years after high school graduation (Study 1: N = 2207; Study 2: N = 1710). Dimensional and social comparisons predicted students’ self-concept and domain-specific STV in school: Individual achievement was positively related to ability self-concept and STV in the corresponding domain and negatively related in the non-corresponding domain. School-level mean achievement was negatively related to ability self-concept and STV in the corresponding domain. Dimensional comparisons were directly related to university major choice, social comparisons were only indirectly related.

Spatial data from Schulp et al., 2014. Uncertainties in ecosystem service maps: A comparison on the European scale. PloS ONE 9, e109643. Safeguarding the benefits that ecosystems provide to society is increasingly included as a target in international policies. To support such policies, ecosystem service maps are made. However, there is little attention for the accuracy of these maps. We made a systematic review and quantitative comparison of ecosystem service maps on the European scale to generate insights in the uncertainty of ecosystem service maps and discuss the possibilities for quantitative validation. This data package contains maps of the ecosystem services climate regulation, erosion protection, flood regulation, pollination, and recreation. For each service, a map of the average supply according to all analyzed maps is included, as well as a map of the uncertainty of the service. The data package contains a detailed read-me.

Data underlying comparisons of UK productivity against that of the remaining G7 countries.

Comparison of OR tables between the interaction of rs7522462 and rs11945978 in the WTCCC data with the shared controls (left) and the interaction of the proxy SNPs, rs296533 and rs2089509 in the IBDGC data (right). The legend to this table is the same as that of Table 3.

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.

This zip file contains the underlying data used to create all tables and figures within the manuscript. This dataset is associated with the following publication: Iiames, J., E. Cooter, D. Pilant, and Y. Shao. Comparison of EPIC-simulated and MODIS-derived Leaf Area Index (LAI) across multiple spatial scales. Remote Sensing. MDPI AG, Basel, SWITZERLAND, 12(17): 2764, (2020).

An international comparison of productivity across the G7 nations, in terms of the level of and growth in GDP per hour and GDP per worker. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: ICP

Dataset Card for comparison-data-falcon-with-feedback

This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.

  Dataset Summary

This dataset contains:

A dataset configuration file conforming to the Argilla dataset format named argilla.cfg. This configuration file will be used to configure the dataset when using… See the full description on the dataset page: https://huggingface.co/datasets/argilla/comparison-data-falcon-with-feedback.

Comparing the UK with OECD countries or the European Union across main areas of well-being, 2019. Where available using directly comparable or proxy measure data.

Contains files pertaining to comparisons with search results from re3data.org

The data-comparison extension for CKAN provides a means to compare data from CSV or XLSX files through visualization. Targeted at CKAN 2.9, this plugin enhances data analysis capabilities by allowing users to visually compare datasets directly within the CKAN environment. This facilitates a more intuitive understanding of data variations and trends. Key Features: CSV/XLSX File Comparison: Allows direct comparison of data contained in CSV and XLSX file formats. Visualization: Leverages visualization tools to present the data comparison results. Chart.js Integration: Employs Chart.js library for creating interactive and customizable charts used in the visualization process. Technical Integration: The data-comparison extension integrates with CKAN by adding a plugin that needs to be activated via the ckan.plugins setting in the CKAN configuration file (/etc/ckan/default/ckan.ini). It also requires the installation of Chart.js using npm to render the visualizations. After installing the extension and modifying the CKAN configurations file, CKAN needs to be restarted for the changes to take effect. Benefits & Impact: Implementing the data-comparison extension offers several benefits, especially for data-driven organizations. By providing visualization-based comparison of datasets, it enables quicker insights and potentially more informed decision-making.

State comparisons data for population,age, race, Hispanic Origin, and housing information for all states. Data include a national ranking.

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.

Crime data information for the United States and all states from the Uniform Crime Reporting Program.

The Akaike Information Criterion (AIC) provides a means for ranking models based on the number of parameters used to fit the data (r) and the residual error (Merror2), [26]. AIC = nlog(error) +2(r +2), where n is the number of data points and r is the number of free parameters. Error = Merror2(n−r−1)/n.

State comparisons data for births, deaths, infant death, disease, abortion, median age, marriages, divorces, physicians, nurses, and health insurance coverage. Data include a national ranking.