Automated Data Quality Research Articles

A Technical Overview of the Kentucky Mesonet

The Kentucky Mesonet is a research-grade weather and climate observing network with redundant sensors that monitors the near-surface atmosphere at 71 locations across Kentucky. The network measures temperature, precipitation, solar radiation, relative humidity, barometric pressure, and wind speed and direction every 5 min, with soil moisture and soil temperature measured every 30 min. In addition, it operates a camera at selected locations. All observations are transmitted via cellular modem every 5 min and become available to the general public through the World Wide Web within seconds after arrival at Kentucky Mesonet’s Network Operations Center. In between arriving at the IT division and dissemination to general public, the data also go through automated data quality assurance (QA) procedures. Within that time, the data can be viewed through various graphical/visualization formats, developed based on feedback from the user community. The Kentucky Mesonet produces twice-daily QA reports and its technicians respond to these reports, making site visits when necessary to address issues. Mesonet technicians make regular site visits to all stations during spring, summer, and fall seasons. The network maintains a detailed database of station metadata that includes instrument and site maintenance history. The Mesonet delivers the data to the National Weather Service to aid forecasting. It also works closely with a variety of local, state, and federal entities so that the network can meet diverse needs.

Harmonizing National Topographic Data Using an Automated Quality Validation Process

Harmonizing National Topographic Data Using an Automated Quality Validation Process

China Kidney Disease Network (CK-NET) 2015 Annual Data Report

China Kidney Disease Network (CK-NET) 2015 Annual Data Report

The Pediatric Emergency Care Applied Research Network Registry: A Multicenter Electronic Health Record Registry of Pediatric Emergency Care.

Electronic health record (EHR)-based registries allow for robust data to be derived directly from the patient clinical record and can provide important information about processes of care delivery and patient health outcomes. A data dictionary, and subsequent data model, were developed describing EHR data sources to include all processes of care within the emergency department (ED). ED visit data were deidentified and XML files were created and submitted to a central data coordinating center for inclusion in the registry. Automated data quality control occurred prior to submission through an application created for this project. Data quality reports were created for manual data quality review. The Pediatric Emergency Care Applied Research Network (PECARN) Registry, representing four hospital systems and seven EDs, demonstrates that ED data from disparate health systems and EHR vendors can be harmonized for use in a single registry with a common data model. The current PECARN Registry represents data from 2,019,461 pediatric ED visits, 894,503 distinct patients, more than 12.5 million narrative reports, and 12,469,754 laboratory tests and continues to accrue data monthly. The Registry is a robust harmonized clinical registry that includes data from diverse patients, sites, and EHR vendors derived via data extraction, deidentification, and secure submission to a central data coordinating center. The data provided may be used for benchmarking, clinical quality improvement, and comparative effectiveness research.

Argo workstation: a key component of operational oceanography

Operational oceanography requires the quantity, quality, and availability of data set and the timeliness and effectiveness of data products. Without steady and strong operational system supporting, operational oceanography will never be proceeded far. In this paper we describe an integrated platform named Argo Workstation. It operates as a data processing and management system, capable of data collection, automatic data quality control, visualized data check, statistical data search and data service. After it is set up, Argo workstation provides global high quality Argo data to users every day timely and effectively. It has not only played a key role in operational oceanography but also set up an example for operational system.

Автоматизированный контроль качества данных в информационно-измерительных и управляющих системах MES-уровня

Автоматизированный контроль качества данных в информационно-измерительных и управляющих системах MES-уровня

Health Science Research with Primary Care Routine Data From Electronic Patient Records: the BeoNet Registry

Reliable current information is needed for daily patient care and the health system, but comprehensive data, especially from primary care, are scarce and/or hard to access. The BeoNet Register-Database (BNR) aims to depict quality of medical care in Germany using routine data and with no additional documentation or costs for GPs. Registry design, data structure and database use will be demonstrated using chronic obstructive lung disease (COPD) as an example. The BNR is moving through pilot- and roll-out phases. Participating practices provide standardized information about (1) master data (2) practice profile (3) infrastructure and documentation systems. The routinely documented data will be pseudonymized before transmission via standardized secure interfaces to the registry database. Practices can participate in studies in cooperation with BNR if informed consent is obtained from the patients. The registry data will be linked with questionnaires on health care utilization, quality of life and disease-specific parameters. Researchers and physicians can access quasi anonymized data in specific datasets extracted from the registry. Regular automatic data quality checks and feedback from GPs, patients and researchers enhance the quality of the registry. Performance and data analysis are continuously optimized. The data security concept has been approved and ethical approval is on hand. GPs, pediatricians and pneumologists from Hanover, Munich and Heidelberg participated in the pilot phase. The registry database currently holds 98497 patient IDs and related information. BNR contains more than the standard billing data collected by health insurers. The data are applicable to a wide range of health practices, health care and economics and disease development questions. Questionnaires have been generated, tested and are being distributed. First descriptive analyses of prevalence, age and gender have been performed. Quality and validity checks have been implemented. 2016 marks the roll-out phase of the BNR. Further recruitment of practices throughout Germany, data linkage with additional questionnaires, recruitment of patients for health care research projects, quality checks and data analysis of disease-specific costs and health-related quality of life in COPD are progressing with the overall aim of improving pulmonary healthcare strategies. The floor is now open for retro- and prospective cross-sectional and longitudinal studies.

Identifying priorities for data quality improvement within Haiti׳s iSanté EMR system: Comparing two methods

ObjectivesThe aim of this case study was to compare two alternative strategies for prioritizing data elements for data quality assessment (DQA) in a routine health management information system. The study used data from iSanté, a multi-site electronic medical record implemented by the Haitian Ministry of Health. We described and compared two prioritization strategies: (1) a Delphi process drawing iterative feedback from clinicians and stakeholders responsible for monitoring and evaluation (M&E) of health programs to identify consensus priorities for data on HIV patients; and (2) a process using burden of disease estimates from Haiti to establish priorities for data on primary care patients. MethodsThe Delphi process included 26 individuals across 6 institutions, including clinicians and M&E specialists. Through three rounds of questionnaires, the stakeholders provided input for prioritization of 13 indicators for completeness, accuracy and timeliness of HIV data. The burden of disease prioritization process revealed that cardiovascular disease contributed to the greatest number of disability-adjusted life-years (DALYs). This resulted in the selection of 16 data quality indicators for primary care data. ResultsBoth methods informed the definition of a set of automated data quality queries to assess internal validity, completeness, and timeliness using logic and clinical plausibility. The Delphi process benefited from stakeholder input, but was lengthy in process. The burden of disease prioritization process was objective and easier to implement, but lacked stakeholder buy-in. ConclusionsA hybrid approach guided by both disease burden and stakeholder input may be most beneficial for prioritizing data elements for DQA.

Developmental validation of a custom panel including 273 SNPs for forensic application using Ion Torrent PGM

Utilizing massively parallel sequencing (MPS) technology for SNP testing in forensic genetics is becoming attractive because of the shortcomings of STR markers, such as their high mutation rates and disadvantages associated with the current PCR-CE method as well as its limitations regarding multiplex capabilities. MPS offers the potential to genotype hundreds to thousands of SNPs from multiple samples in a single experimental run. In this study, we designed a customized SNP panel that includes 273 forensically relevant identity SNPs chosen from SNPforID, IISNP, and the HapMap database as well as previously related studies and evaluated the levels of genotyping precision, sequence coverage, sensitivity and SNP performance using the Ion Torrent PGM. In a concordant study of the custom MPS-SNP panel, only four MPS callings were missing due to coverage reads that were too low (<20), whereas the others were fully concordant with Sanger's sequencing results across the two control samples, that is, 9947A and 9948. The analyses indicated a balanced coverage among the included loci, with the exception of the 16 SNPs that were used to detect an inconsistent allele balance and/or lower coverage reads among 50 tested individuals from the Chinese HAN population and the above controls. With the exception of the 16 poorly performing SNPs, the sequence coverage obtained was extensive for the bulk of the SNPs, and only three Y-SNPs (rs16980601, rs11096432, rs3900) showed a mean coverage below 1000. Analyses of the dilution series of control DNA 9948 yielded reproducible results down to 1ng of DNA input. In addition, we provide an analysis tool for automated data quality control and genotyping checks, and we conclude that the SNP targets are polymorphic and independent in the Chinese HAN population. In summary, the evaluation of the sensitivity, accuracy and genotyping performance provides strong support for the application of MPS technology in forensic SNP analysis, and the assay offers a straightforward sample-to-genotype workflow that could be beneficial in forensic casework with respect to both individual identification and complex kinship issues.

Supporting Business Intelligence Usage: An Integrated Framework with Automatic Weighting

ABSTRACTThis paper presents an integrated framework that comprises an automatic weighting method for assessing data quality (DQ) of the framework so as to better support the business intelligence (BI) usage. Specifically, we utilize business process modeling (BPM) notation and information product map and frame them into a hierarchical mapping structure. Furthermore, we develop and demonstrate an automatic weight-assignment method for evaluating critical dimensions (i.e., completeness and accuracy) of DQ of the integrated framework. Through a design science paradigm, the effectiveness of the framework and the associated DQ weighting method has been rigorously validated by faculty management users of a university. The framework together with the DQ weighting method builds user confidence by enhancing the traceability of a BI product. The automatic DQ weight assignment also provides better time efficiency because the weight of each data attribute is determined automatically based on its usage on the BI dashboard.

Automatic Data Quality Control of Observations in Wireless Sensor Network

Emerging multisource earth observation technologies represented by wireless sensor network (WSN) technology are widely used in land surface observation and simulation studies. Consequently, data quality control of massive observation data has brought challenges to researchers. This letter describes a comprehensive approach applied to automatic data quality control of WSN data. First, summarize the quality element of WSN observation data which can be achieved through automated methods by analyzing the characteristics of WSN observation data, and develop a decision algorithm for each quality element. Then, associate the data type and algorithm through data quality control rules. Finally, establish an automatic data quality control system based on data quality control rules. As a matter of fact, this system has run for one and a half years and processed more than 500 million observation data records without human intervention. Application results show that this method system can effectively control the data quality of WSN data automatically.

SGNoise - a tool for the ambient noise level analysis at superconducting gravimeter stations

The SGNoise represents a web tool for the near real-time analysis of data from superconducting gravimeters (SGs). Gravity residuals are computed on daily basis from raw SG data (1-s sampling rate) and analysed/visualized in time and frequency domain. It fulfils the main goal of the SGNoise — automatic data quality control of continuously operating geophysical instruments which provides a helpful service for operators and data users. The data quality quantification is represented by the evaluation of ambient noise level at SG stations by spectral analysis of gravity residuals and its visualization through spectrograms and probability density functions. Among others, it provides a possibility for comparison of noise levels at SG stations as demonstrated for Pecný, Wettzell and Strasbourg stations which are included in the SGNoise service at http://oko.asu.cas.cz/grav/. The SGNoise program package is written in PHP5 scripting language using the GD Graphics Library. Procedures used for data processing and analysis are consistent with previous works on noise level analysis at SG stations.

Simultaneous analysis of hundreds of Y-chromosomal SNPs for high-resolution paternal lineage classification using targeted semiconductor sequencing.

SNPs from the non-recombining part of the human Y chromosome (Y-SNPs) are informative to classify paternal lineages in forensic, genealogical, anthropological, and evolutionary studies. Although thousands of Y-SNPs were identified thus far, previous Y-SNP multiplex tools target only dozens of markers simultaneously, thereby restricting the provided Y-haplogroup resolution and limiting their applications. Here, we overcome this shortcoming by introducing a high-resolution multiplex tool for parallel genotyping-by-sequencing of 530 Y-SNPs using the Ion Torrent PGM platform, which allows classification of 432 worldwide Y haplogroups. Contrary to previous Y-SNP multiplex tools, our approach covers branches of the entire Y tree, thereby maximizing the paternal lineage classification obtainable. We used a default DNA input amount of 10ng per reaction but preliminary sensitivity testing revealed positive results from as little as 100pg input DNA. Furthermore, we demonstrate that sample pooling using barcodes is feasible, allowing increased throughput for lower per-sample costs. In addition to the wetlab protocol, we provide a software tool for automated data quality control and haplogroup classification. The unique combination of ultra-high marker density and high sensitivity achievable from low amounts of potentially degraded DNA makes this new multiplex tool suitable for a wide range of Y-chromosome applications.

Clustering Species Accumulation Curves to Identify Skill Levels of Citizen Scientists Participating in the eBird Project

Although citizen science projects such as eBird can compile large volumes of data over broad spatial and temporal extents, the quality of this data is a concern due to differences in the skills of volunteers at identifying bird species. Species accumulation curves, which plot the number of unique species observed over time, are an effective way to quantify the skill level of an eBird participant. Intuitively, more skilled observers can identify a greater number of species per unit time than inexperienced birders, resulting in a steeper curve. We propose a mixture model for clustering species accumulation curves. These clusters enable the identification of distinct skill levels of eBird participants, which can then be used to build more accurate species distribution models and to develop automated data quality filters.

Development of Automated Data Quality Indicators and Visualizations using Florida's ESSENCE System

The objective of this project was to develop visualizations and tools for public health users to determine the quality of their surveillance data. A series of data quality visualizations were developed and implemented in the Florida Department of Health's version of ESSENCE. The data quality portal has been active the Florida ESSENCE system since March 31st, 2012. Between that time and August the portal has been accessed over 1300 times. The presentation will include additional statistics about which specific features were most used and those features that users have found the most useful.

Operational profiling of temperature using ground-based microwave radiometry at Payerne: prospects and challenges

Abstract. The motivation of this study is to verify theoretical expectations placed on ground-based microwave radiometer (MWR) techniques and to confirm whether they are suitable for supporting key missions of national weather services, such as timely and accurate weather advisories and warnings. We evaluate reliability and accuracy of atmospheric temperature profiles retrieved continuously by the microwave profiler system HATPRO (Humidity And Temperature PROfiler) operated at the aerological station of Payerne (MeteoSwiss) in the time period August 2006–December 2009. Assessment is performed by comparing temperatures from the radiometer against temperature measurements from a radiosonde accounting for a total of 2107 quality-controlled all-season cases. In the evaluated time period, the MWR delivered reliable temperature profiles in 86% of all-weather conditions on a temporal resolution of 12–13 min. Random differences between MWR and radiosonde are down to 0.5 K in the lower boundary layer and increase to 1.7 K at 4 km height. The differences observed between MWR and radiosonde in the lower boundary layer are similar to the differences observed between the radiosonde and another in-situ sensor located on a close-by 30 m tower. Temperature retrievals from above 4 km contain less than 5% of the total information content of the measurements, which makes clear that this technique is mainly suited for continuous observations in the boundary layer. Systematic temperature differences are also observed throughout the retrieved profile and can account for up to ±0.5 K. These errors are due to offsets in the measurements of the microwave radiances that have been corrected for in data post-processing and lead to nearly bias-free overall temperature retrievals. Different reasons for the radiance offsets are discussed, but cannot be unambiguously determined retrospectively. Monitoring and, if necessary, corrections for radiance offsets as well as a real-time rigorous automated data quality control are mandatory for microwave profiler systems that are designated for operational temperature profiling. In the analysis of a subset of different atmospheric situations, it is shown that lifted inversions and data quality during precipitation present the largest challenges for operational MWR temperature profiling.

Automated Data Quality Assessment of Marine Sensors

The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance/Quality Control (QA/QC) is necessary to ensure that the data collected is fit for purpose. Current automated QA/QC approaches provide assessments based upon hard classifications of the gathered data; often as a binary decision of good or bad data that fails to quantify our confidence in the data for use in different applications. We propose a novel framework for automated data quality assessments that uses Fuzzy Logic to provide a continuous scale of data quality. This continuous quality scale is then used to compute error bars upon the data, which quantify the data uncertainty and provide a more meaningful measure of the data’s fitness for purpose in a particular application compared with hard quality classifications. The design principles of the framework are presented and enable both data statistics and expert knowledge to be incorporated into the uncertainty assessment. We have implemented and tested the framework upon a real time platform of temperature and conductivity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. Results indicate that the error bars generated from the Fuzzy QA/QC implementation are in good agreement with the error bars manually encoded by a domain expert.

Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns

The ecological sciences have benefited greatly from recent advances in wireless sensor technologies. These technologies allow researchers to deploy networks of automated sensors, which can monitor a landscape at very fine temporal and spatial scales. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. The resulting data streams often exhibit incorrect data measurements and missing values. Identifying and correcting these is time-consuming and error-prone. We present a method for real-time automated data quality control (QC) that exploits the spatial and temporal correlations in the data to distinguish sensor failures from valid observations. The model adapts to each deployment site by learning a Bayesian network structure that captures spatial relationships between sensors, and it extends the structure to a dynamic Bayesian network to incorporate temporal correlations. This model is able to flag faulty observations and predict the true values of the missing or corrupt readings. The performance of the model is evaluated on data collected by the SensorScope Project. The results show that the spatiotemporal model demonstrates clear advantages over models that include only temporal or only spatial correlations, and that the model is capable of accurately imputing corrupted values.

Automatic Data Quality Evaluation for the AVM System

This paper proposes the schemes of automatic process and metrology data-quality evaluations for the automatic virtual metrology (AVM) system. Firstly, principal component analysis is applied to extract data features of all the collected equipment process data; then Euclidean distance is utilized to unify all the principal components into a single index denoted by process data quality index (DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> ) for evaluating the quality of process data. Second, adaptive resonance theory 2 (ART2) and normalized variability are applied to define the metrology data quality index (DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> ) for appraising the quality of metrology data. The thresholds of both DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> and DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> are also defined and can be adaptively calculated. The DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> and DQI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> data quality evaluation schemes are well suited for the AVM systems of the semiconductor and thin film transistor-liquid crystal display industries to online, real-time, and automatically evaluate the quality of all the collected process and metrology data. As such, abnormal data will not be adopted for VM model training or tuning and VM conjecture accuracy can be maintained.

Cluster Analysis of Self-Monitoring Blood Glucose Assessments in Clinical Islet Cell Transplantation for Type 1 Diabetes

OBJECTIVECluster analysis was performed on the results of self-monitoring of blood glucose (SMBG) to discriminate islet graft function after islet cell transplantation (ICT) in patients with type 1 diabetes.RESEARCH DESIGN AND METHODSEleven islet recipients were included in this study. The patients visited our clinic monthly after ICT and provided blood samples for fasting C-peptide (n = 270), which were used to evaluate islet graft function. They also provided their SMBG data through an automatic data collection system. The SMBG data for 3 days immediately before each clinic visit were evaluated using the following assessments: M value, mean amplitude of glycemic excursions, J index, index of glycemic control, average daily risk range, and glycemic risk assessment diabetes equation. The cluster analysis was performed for both SMBG assessments and samples. Multivariate logistic regression analysis was used to evaluate the clusters of SMBG for assessing islet graft function.RESULTSAnalysis for SMBG assessments revealed five types of clusters, which showed similar patterns according to functional or dysfunctional islet graft phase. Two clusters, the euglycemia cluster (P < 0.001) and the hypoglycemia cluster (P = 0.001), were significant factors in the logistic model for islet graft function. The SMBG clusters had significant correlations with clinical graft indexes (P < 0.001).CONCLUSIONSCluster analysis of SMBG data as part of an automated data quality system could allow discrimination of islet graft dysfunction after ICT. This approach should be considered for islet recipients.