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.

Dataset for "Comparing Transaction Logs to ILL requests to Determine the Persistence of Library Patrons In Obtaining Materials" article. Excel file contains all data in four worksheets Zip file contains four csv files, one for each worksheet: - Comparing Transaction Logs to ILL - 2016 ILL Raw ...Data.csv - Comparing Transaction Logs to ILL - 2015 ILL Raw Data.csv - Comparing Transaction Logs to ILL - 2016 Zero Search Raw Data.csv - Comparing Transaction Logs to ILL - 2015 Zero Search Raw Data.csv [more]

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.

The top table shows the average classifier performance for cross-validation on the 9-locus public STR data. The bottom table is the performance for the same test, but on a 9-locus subset of our ground-truth training data. While overall performance is lower than the 15-locus cross-validation test on our ground-truth data (Table 1), the two data sets perform similarly here, indicating that increasing the number of markers in the data set can significantly improve performance.

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.

Euro Area Growth Signals from Industrial Production: Warnings from a Comparison of Gross Value Added and Production

This study compares industrial production and gross value added in volume terms in the euro area and euro area countries, because real GDP growth
signals from industrial production growth might be misleading and earlier released industrial production growth is not one-to-one translated into industrial value added growth. This is an important issue for analysts and policy makers, because industrial production is a standard element in tools for nowcasting real GDP in real time. It also raises the question about the factors explaining these differences. Differences in terms of (changes in) quarterly growth between production and gross value added include sign reversals and can last for consecutive quarters. Persistent level differences might also exist. The explanatory factors for these differences are the treatment of prices, seasonality and coverage. Data limitations prevent a detailed analysis of the price factor, but the other two factors are more closely evaluated. It turns out that the relative importance of these factors varies over time and thus is difficult to assess ex ante for a specific quarter. A remedy is that statisticians further harmonize national accounts and short-term statistics as well as national practices for seasonal adjustment.

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.

Earlier this year, the TfWM Data Insight team applied for a research grant to work with a university masters student at the University of Essex through the Local Government Data Research Centre to understand the functionality and output differences between pneumatic tube counters and video-based camera data and develop a machine learning model to reduce the difference found.The TfWM Data Insight team worked with the student over the course of the summer of 2022 to undertake the project. The following is a summary of the report delivered to TfWM, analysing data from a number of week pairs between October and November 2021 across 5 locations in the West Midlands, totalling around one thousand observations aggregated to 15-minute intervals.Analysing traffic volume data enables improvements in traffic control decision making to be made to achieve a healthier, happier, better connected and more prosperous West Midlands. In this project we looked at two methods of gathering, computing, and transferring data: pneumatic tubes (commonly referred to as an ATC in the industry) placed parallel to the direction of traffic on the surface of the roads, which are regarded as accurate but insufficient due to the high installation and maintenance costs, and traffic cameras, mounted on column infrastructure facing the flow of traffic on the road.

The NADAC Weekly Comparison identifies the drug products with current NADAC rates that are replaced with new NADAC rates. Other changes (e.g. NDC additions and terminations) to the NADAC file are not reflected in this comparison. Note: Effective Date was not recorded in the dataset until 6/7/2017

Variable selection in high-dimensional regression models is challenging. Thus, developing stable and reliable methods for variable selection is essential. Omics data, a common source of high-dimensional data, brings the added complexity of integrating diverse genomic layers into the analysis. The IPF-LASSO model has previously addressed this by applying distinct penalty parameters for each data modality. However, incorporating heterogeneous data layers into variable selection with Type I error control remains an open problem. To address this, we applied stability selection to control the number of false positives in both IPF-LASSO and standard LASSO models. Our study aimed to compare the two methods, investigating whether introducing different penalty parameters per data modality enhances statistical power while controlling false positives. Two high-dimensional data structures were investigated in simulations, one with independent data and the other with correlated data. We also applied the models to breast cancer treatment data, where IPF-LASSO identified relevant clinical variables.

The Dataset contains the data, that was created simulating and measuring different control strategies for the impact actuator. Each table contains the time value, the set position and the actual position for the base axes as well as the actuators when executing a reference contour. There are two tables for each of the four control strategies, both simulative and experimental: EdgeStop: Reference Profile with acceleration an jerk limits ImpRef: Previous Impact Control Strategie ImpNewWithoutReset: New Strategie, but without controller reset ImpNew: New Impact Conctrol strategie

1. Introduction

Welcome to the documentation for the code, data, and plotting scripts corresponding to the paper titled "Method Comparison for Simulating Non-Gaussian Beams and Diffraction for Precision Interferometry". This repository contains resources to replicate the simulation results presented in the paper. Please carefully review the instructions below to ensure the successful execution of the code, data generation, and plotting.

2. Dependencies

Before running the code, it is necessary to install IfoCAD (version 2022/10, git commit adf19a5b) in your environment. Please note, that IfoCAD is currently prepared for publication. However, because IfoCAD is not yet publically available, we provide here likewise the data generated by IfoCAD. Here is the introduction page of IfoCAD: https://www.aei.mpg.de/ifocad. Additionally, the plotting scripts require the installation of gnuplot.

3. Directory Structure

The code, data, and plotting scripts are organized according to the structure of the paper's sections. Each section corresponds to a specific folder. Here is a brief overview of the file structure: Section2/: Code, data, and plotting scripts for Section 2 Section3/: Code, data, and plotting scripts for Section 3 ...

4. Running the Code

Step 1:

Install Dependencies Ensure IfoCAD is installed. The open-source release is anticipated soon. Also, make sure gnuplot is installed for the plotting scripts.

Step 2:

Run the Code Navigate to the specific section folder and to run the code.

5. Generating Data

After running the code, the generated data will be stored in the file path configured in the code. Utilize this data for further analysis and visualization.

6. Plotting Scripts

Each section's plotting scripts are located in their respective folders. Run these scripts sequentially to generate plots consistent with those presented in the paper. Make sure to have gnuplot installed for proper execution.

For any issues or questions during usage, feel free to contact us.

Thank you for using our resources!

Comprehensive financial comparison data between Transocean (RIG) and (), including valuation metrics, performance data, financial ratios, and market statistics.

Historical performance data comparing Alphabet and Netflix across different time periods (1 day, 1 week, 1 month, 3 months, 6 months, 1 year, 3 years, 5 years).

VA Community Care Comparison or VAC3 (formerly Why Not the Best VA) is a system for comparing Veterans Health Administration (VHA) hospital system performance with regional and U.S. national benchmarks. This report includes key quality measures available on CMS Hospital Compare and top hospital recognition programs from reporting agencies of hospital quality. VAC3 data tables are updated every quarter.

Data produced for publication titled: Benchmark Comparison of Transcranial Ultrasound Simulation: Comparing the CIVA Healthcare Platform Method with Existing Compressional Wave Models. Submitted to the Journal of the Acoustical Society of America

We studied and compared three automated FAIRness evaluation tools namely F-UJI, the FAIR Evaluator, and FAIR Checker examining three aspects: 1) tool characteristics, 2) the evaluation metrics, and 3) metrics tests for three public datasets. We find significant differences in the evaluation results for tested resources, along with differences in the design, implementation, and documentation of the evaluation metrics and platforms.

This data is the comparison results we summarized from the study. All results are reported in our manuscript. This data is the supplementary material of the manuscript.

This paper provides a review of three different advanced machine learning algorithms for anomaly detection in continuous data streams from a ground-test firing of a subscale Solid Rocket Motor (SRM). This study compares Orca, one-class support vector machines, and the Inductive Monitoring System (IMS) for anomaly detection on the data streams. We measure the performance of the algorithm with respect to the detection horizon for situations where fault information is available. These algorithms have been also studied by the present authors (and other co-authors) as applied to liquid propulsion systems. The trade space will be explored between these algorithms for both types of propulsion systems.

Historical performance data comparing American Express and gspc across different time periods (1 day, 1 week, 1 month, 3 months, 6 months, 1 year, 3 years, 5 years).