The data quality monitoring system (DQMS) developed by the Satellite Oceanography Program at the NOAA National Centers for Environmental Information (NCEI) is based on the concept of a Rich Inventory developed by the previous NCEI Enterprise Data Systems Group. The principal concept of a Rich Inventory is to calculate the data Quality Assurance (QA) descriptive statistics for selected parameters in each Level-2 data file and publish the pre-generated images and NetCDF-format data to the public. The QA descriptive statistics include valid observation number, observation number over 3-sigma edited, minimum, maximum, mean, and standard deviation. The parameters include sea surface height anomaly, significant wave height, altimeter, and radiometer wind speed, radiometer water vapor content, and radiometer wet tropospheric correction from Jason-3 Level-2 Final Geophysical Data Record (GDR) and Interim Geophysical Data Record (IGDR) products.

The increasing burden of healthcare systems in most developing countries affects access to quality healthcare. The state of Quality Assurance in the Public University Hospitals remains questionable in these countries. This study investigates the level of patient satisfaction and its service quality predictors among patients accessing healthcare from the Public University Hospitals in Ghana. An empirical assessment survey using a pre-tested service quality (SERVQUAL) measurement scale was conducted among 439 patients who attended two major Public University Hospitals in Ghana. Data were obtained from patients on the five dimensions of perceived service quality including tangibles, reliability, responsiveness, assurance and empathy. Data were analysed using Stata software. Descriptive statistics and linear regression analysis were performed to identify the most defining service quality dimension of patient satisfaction. The study indicates adequate level of service satisfaction among patients accessing healthcare from the public university hospitals in Ghana, although ‘responsiveness’ was low. Therefore, the management team of these hospitals must not underestimate the crucial role of staff in inspiring trust and confidence in their clients.

As part of the Glassmeyer et al., (2023) review for the journal GeoHealth, the data from 84 journal articles was summarized. One of the metrics captured in the summary was the different types of quality assurance/ quality control parameters mentioned in each paper (see Data Template tab). The types of QA/QC parameters were: field blank, laboratory reagent blank, laboratory fortified blank (LFB- aka laboratory spike), laboratory fortified matrix sample (LFM- aka matrix spike) and duplicate sample. Also logged was if no QA/QC was mentioned, and if it was a review paper that summarized multiple studies (and therefore had no independent QA/QC). This dataset is that QA/QC summary.

Dataset Title: OpenStreetMap Quality Assurance Dataset

Dataset Description: This dataset comprises OpenStreetMap (OSM) data obtained from the Dublin area in 2023, specifically for quality assurance purposes. The dataset contains a diverse range of geospatial information, meticulously sourced from OSM through the Overpass API.

Data Source: The primary source of this dataset is OpenStreetMap, accessed via the Overpass API. It encompasses a wide array of geospatial features and attributes contributed by the OSM community.

Data Format: The dataset is formatted in GeoJSON, a widely used and versatile format for representing geospatial data.

Data Size: The dataset encompasses 471 individual records, collectively forming a comprehensive representation of the Dublin area within the scope of the year 2023.

Data License: The dataset is released under the Open Database License (ODbL), ensuring openness and accessibility to users while respecting OSM's data sharing principles.

Temporal and Spatial Coverage: The dataset captures geospatial information within the vibrant city of Dublin, offering a snapshot of the region during the year 2023. It provides valuable insights into the dynamic nature of the city's geographical data.

This dataset serves as a valuable resource for quality assurance and evaluation of geospatial data within the Dublin area. Researchers, GIS professionals, and the broader OSM community can utilize it for a variety of spatial analysis and data quality assessment tasks.

ABSTRACT Objective: To evaluate the incidents spontaneously notified in a general hospital in Minas Gerais. Method: Retrospective, descriptive, quantitative study performed at a general hospital in Montes Claros - Minas Gerais State. The sample comprised 1,316 incidents reported from 2011 to 2014. The data were submitted to descriptive statistical analysis using Statistical Package for the Social Sciences version 18.0. Results: The prevalence of incidents was 33.8 per 1,000 hospitalizations, with an increase during the investigation period and higher frequency in hospitalization units, emergency room and surgical center. These occurred mostly with adult clients and relative to the medication supply chain. The main causes were noncompliance with routines/protocols, necessitating changes in routines and training. Conclusion: There was a considerable prevalence of incidents and increase in notifications during the period investigated, which requires the attention of managers and hospital staff. Nevertheless, we observed development of the patient safety culture.

This summary dataset provides the underlying data for the Agency Performance – Child Welfare Dashboard and the Agency Performance – Child Welfare Quality Assurance Dashboards. Data are pre-aggregated and reported at the level of fiscal year and region. The following metrics are also summarized by race/ethnicity on the Agency Performance Child Welfare Dashboard: - Median Length of Stay - Re-Entry The following metrics are summarized by race/ethnicity on the Agency Performance Quality Assurance Dashboard: - Permanency in 12 Months - Kin Placement Rate The following metrics are also summarized by age on the Agency Performance Child Welfare Dashboard: - Out-of-Home Exits - Out-of-Home Entries - Out-of-Home: Single Point in Time CW Agency Performance: Agency Performance CW Dashboard Final_v1 | Tableau Public CW Supplemental Quality Assurance: Supplemental Agency Performance CW Dashboard Final_v1 | Tableau Public

Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory’s routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

We conducted a benchmarking analysis of 16 summary-level data-based MR methods for causal inference with five real-world genetic datasets, focusing on three key aspects: type I error control, the accuracy of causal effect estimates, replicability, and power.

The datasets used in the MR benchmarking study can be downloaded here:

"dataset-GWASATLAS-negativecontrol.zip": the GWASATLAS dataset for evaluation of type I error control in confounding scenario (a): Population stratification

"dataset-NealeLab-negativecontrol.zip": the Neale Lab dataset for evaluation of type I error control in confounding scenario (a): Population stratification;

"dataset-PanUKBB-negativecontrol.zip": the Pan UKBB dataset for evaluation of type I error control in confounding scenario (a): Population stratification;

"dataset-Pleiotropy-negativecontrol": the dataset used for evaluation of type I error control in confounding scenario (b): Pleiotropy;

"dataset-familylevelconf-negativecontrol.zip": the dataset used for evaluation of type I error control in confounding scenario (c): Family-level confounders;

"dataset_ukb-ukb.zip": the dataset used for evaluation of the accuracy of causal effect estimates;

"dataset-LDL-CAD_clumped.zip": the dataset used for evaluation of replicability and power;

Each of the datasets contains the following files:

"Tested Trait pairs": the exposure-outcome trait pairs to be analyzed;

"MRdat" refers to the summary statistics after performing IV selection (p-value < 5e-05) and PLINK LD clumping with a clumping window size of 1000kb and an r^2 threshold of 0.001.

"bg_paras" are the estimated background parameters "Omega" and "C" which will be used for MR estimation in MR-APSS.

Note:

Supplemental Tables S1-S7.xlxs provide the download link for the original GWAS summary-level data for the traits used as exposures or outcomes.

The formatted dataset after quality control can be accessible at our GitHub website (https://github.com/YangLabHKUST/MRbenchmarking).

The details on quality control of GWAS summary statistics, formatting GWASs, and LD clumping for IV selection can be found on the MR-APSS software tutorial on the MR-APSS website (https://github.com/YangLabHKUST/MR-APSS).

R code for running MR methods is also available at https://github.com/YangLabHKUST/MRbenchmarking.

Descriptive statistics (mean, SD, minimum, and maximum) of patient satisfaction levels across seven domains within newly established, statistically distinct socio-demographic and clinical groups.

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Metal, hydrocarbon, or nutrient data have not been recorded for the Arctic coastal plain 1002 area of the Arctic National Wildlife Refuge (Arctic Refuge) in areas of prospective oil and corridor development. Pre-development baseline data for contaminants are necessary to enable general characterization of water quality and contaminant residues, as well as to provide site-specific pre-development information in the event of a Congressional decision to open the Arctic coastal plain to oil and gas exploration and development. This study examines 1988-1989 samples of sediments, water, sedge, birds, invertebrates, and fishes from the 1002 area. Volume 1 of the three volumes in this report describes the study area, study sites, methods, and objectives, and provides summary statistics (geometric mean, arithmetic mean, arithmetic standard deviation, maximum, minimum, and median) for those analytes with more than 2/3 of the concentrations greater than the limit of detection. Volume 2contains the raw metal and hydrocarbon contaminant data, and the raw water quality data. Volume 3summarizes quality assurance/quality control (QA/QC) results which include mean relative percent differences (RPD's) from duplicate analyses, mean percent recoveries from spiked analyses, mean recoveries and Z scores from standard reference material analyses, and maximum concentrations from blank analyses. For a comprehensive description of all quality assurance/quality control methods, also see Volume 1. These reports provide a database on a sufficient number of aquatic, terrestrial, and lagoon samples to enable general characterization of water quality and contaminant residues, as well as to provide site specific pre-development information. The reader is strongly encouraged to use the QA/QC data in Volume 3 to assess data quality on an analyte-by-analyte basis for each sample matrix. This information will be used by Refuge management and State and Federal regulators to assess any post development changes that result from any oil and gas exploratory or production activities. The data will also be useful in evaluating special use permits, Clean Water Act Sections 402 and 404 permits, and State wastewater permits, and in recommending appropriate mitigation measures if development occurs on the 1002 area.

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Quality assurance is the process of establishing and fulfilling quality standards with consistent, systematic and sustainable management, to meet the needs of stakeholders. Universities are qualified if they are able to realize their vision through the implementation of their mission, and able to meet the needs of stakeholders.The research was conducted in 3 (three) universities including Gadjah Mada University Yogyakarta, Tulungagung State Islamic Institute and Balitar Blitar Islamic University. The purpose of this study is to describe in detail the internal quality assurance management system of Gajah Mada University Yogyakarta, Tulungagung State Islamic Institute and Univertas Islam Balitar Blitar, specific "Implementation of internal quality assurance of universities described through a cycle of internal quality assurance system management activities which includes Standard Setting, Standard Implementation, Standard Evaluation, Standard Control and Standard Enhancement.This type of research is qualitative with multi case study design and descriptive study method. Source data using person or respondent as informant. Data collection, with Interview / Interview technique, Observation, and Documentation. Data analysis using interactive model analysis of idea of Miles and Huberman consists of three activities, namely; data reduction, data presentation, and conclusion / verification.Conclusion The results of research at 3 (three) colleges of Gadjah Mada University Yogyakarta, Tulungagung State Islamic Institute and Balit Islam Univertas Balitar Islamic University of Balitar Blitar has run internal assurance of internal quality that is regulated integrally through the realization of Tri Darma Perguruan Tinggi. The implementation of the quality of education is run on an ongoing basis with the stipulation of quality standards of education refers to the Vision & n Missions, and legislation of education regulation enforced nationally and internationally. Implementation of Quality Standard runs academic and non academic field. Evaluation of Education Quality Standards in the process of realization of Tri Darma Higher Education through monitoring with its activities Internal Quality Audit, Control of Quality Standards of education through self-evaluation activities, Improving the quality standard of education conducted on the basis of the results of internal audits and self-evaluation.

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Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

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This document briefly describes quality assurance and quality control procedures followed to generate IMOS Ships of Opportunity Bioacoustics sub-Facility data. Ships of Opportunity is a facility under Australia’s Integrated Marine Observing System (IMOS). Bioacoustic data files are available for public download through the Australian Ocean Data Network (AODN) portal (https://portal.aodn.org.au/search?uuid=8edf509b-1481-48fd-b9c5-b95b42247f82).

Report of Data Quality Management is covering the summarized study of several factors encouraging the growth of the market such as market size, market type, major regions and end user applications. By using the report customer can recognize the several drivers that impact and govern the market. The report is describing the several types of Data Quality Management Industry. Factors that are playing the major role for growth of specific type of product category and factors that are motivating the status of the market.

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