The Quality Evaluation of Rare Disease Registries—An Assessment of the Essential Features of a Disease Registry

Rare disease (RD) registries aim to promote data collection and sharing, and facilitate multidisciplinary collaboration with the overall aim of improving patient care. Recommendations relating to the minimum standards necessary to develop and maintain high quality registries are essential to ensure high quality data and sustainability of registries. The aim of this international study was to survey RD registry leaders to ascertain the level of consensus amongst the RD community regarding the quality criteria that should be considered essential features of a disease registry. Of 35 respondents representing 40 RD registries, over 95% indicated that essential quality criteria should include establishment of a good governance system (ethics approval, registry management team, standard operating protocol and long-term sustainability plan), data quality (personnel responsible for data entry and procedures for checking data quality) and construction of an IT infrastructure complying with Findable, Accessible, Interoperable and Reusable (FAIR) principles to maintain registries of high quality, with procedures for authorized user access, erasing personal data, data breach procedures and a web interface. Of the 22 registries that performed a self-assessment, over 80% stated that their registry had a leader, project management group, steering committee, active funding stream, website, and user access policies. This survey has acceptability amongst the RD community for the self-quality evaluation of RD registries with high levels of consensus for the proposed quality criteria.

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A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data

10.31234/osf.io/asc67 ◽

2021 ◽

Author(s):

Victoria Leong ◽

Kausar Raheel ◽

Sim Jia Yi ◽

Kriti Kacker ◽

Vasilis M. Karlaftis ◽

...

Keyword(s):

Data Quality ◽

Response Times ◽

Psychological Research ◽

Cognitive Testing ◽

Quality Data ◽

High Quality ◽

Online Data ◽

Test Environment ◽

Web Based ◽

Cognitive Data

Background. The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted both safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the fore as a promising solution for rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. Here, we examine the opportunities and challenges afforded by the societal shift toward web-based testing, highlight an urgent need to establish a standard data quality assurance framework for online studies, and develop and validate a new supervised online testing methodology, remote guided testing (RGT). Methods. A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (N=41) or online using remote guided testing (N=44), delivered using identical web-based platforms (CANTAB, Inquisit and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, response times), as well as overall task performance measures. Results. The results indicated that, across all measures of data quality and performance, RGT data was statistically-equivalent to data collected in person in the lab. Moreover, RGT participants out-performed the lab group on measured verbal intelligence, which could reflect test environment differences, including possible effects of mask-wearing on communication. Conclusions. These data suggest that the RGT methodology could help to ameliorate concerns regarding online data quality and - particularly for studies involving high-risk or rare cohorts - offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.

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A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data (Preprint)

10.2196/preprints.28368 ◽

2021 ◽

Author(s):

Victoria Leong ◽

Kausar Raheel ◽

Jia Yi Sim ◽

Kriti Kacker ◽

Vasilis M Karlaftis ◽

...

Keyword(s):

Data Quality ◽

Response Times ◽

Psychological Research ◽

Cognitive Testing ◽

Quality Data ◽

High Quality ◽

Online Data ◽

Test Environment ◽

Web Based ◽

Cognitive Data

BACKGROUND The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted both safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the fore as a promising solution for rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. OBJECTIVE Here, we examine the opportunities and challenges afforded by the societal shift toward web-based testing, highlight an urgent need to establish a standard data quality assurance framework for online studies, and develop and validate a new supervised online testing methodology, remote guided testing (RGT). METHODS A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (N=41) or online using remote guided testing (N=44), delivered using identical web-based platforms (CANTAB, Inquisit and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, response times), as well as overall task performance measures. RESULTS The results indicated that, across all measures of data quality and performance, RGT data was statistically-equivalent to data collected in person in the lab. Moreover, RGT participants out-performed the lab group on measured verbal intelligence, which could reflect test environment differences, including possible effects of mask-wearing on communication. CONCLUSIONS These data suggest that the RGT methodology could help to ameliorate concerns regarding online data quality and - particularly for studies involving high-risk or rare cohorts - offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance. CLINICALTRIAL N.A.

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The New Data Quality Task Group (DQTG): ensuring high quality data today and in the future

Metabolomics ◽

10.1007/s11306-014-0679-1 ◽

2014 ◽

Vol 10 (4) ◽

pp. 539-540 ◽

Cited By ~ 7

Author(s):

Daniel W. Bearden ◽

Richard D. Beger ◽

David Broadhurst ◽

Warwick Dunn ◽

Arthur Edison ◽

...

Keyword(s):

Data Quality ◽

Task Group ◽

Quality Data ◽

High Quality ◽

High Quality Data ◽

The Future

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A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression (Preprint)

10.2196/preprints.18366 ◽

2020 ◽

Author(s):

Maryam Zolnoori ◽

Mark D Williams ◽

William B Leasure ◽

Kurt B Angstman ◽

Che Ngufor

Keyword(s):

Data Quality ◽

Quality Data ◽

Patient Centered ◽

High Quality ◽

Data Set ◽

High Quality Data ◽

Research Questions ◽

Quality Issues ◽

The Impact ◽

Systematic Framework

BACKGROUND Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. OBJECTIVE This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. METHODS The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. RESULTS Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. CONCLUSIONS The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/18366

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A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression

JMIR Research Protocols ◽

10.2196/18366 ◽

2020 ◽

Vol 9 (10) ◽

pp. e18366

Author(s):

Maryam Zolnoori ◽

Mark D Williams ◽

William B Leasure ◽

Kurt B Angstman ◽

Che Ngufor

Keyword(s):

Data Quality ◽

Quality Data ◽

Patient Centered ◽

High Quality ◽

Data Set ◽

High Quality Data ◽

Research Questions ◽

Quality Issues ◽

The Impact ◽

Systematic Framework

Background Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. Objective This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. Methods The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. Results Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. Conclusions The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. International Registered Report Identifier (IRRID) DERR1-10.2196/18366

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eMaintenance ontologies for data quality support

Journal of Quality in Maintenance Engineering ◽

10.1108/jqme-09-2014-0048 ◽

2015 ◽

Vol 21 (3) ◽

pp. 358-374 ◽

Cited By ~ 8

Author(s):

Mustafa Aljumaili ◽

Karina Wandt ◽

Ramin Karim ◽

Phillip Tretten

Keyword(s):

Data Quality ◽

Production Process ◽

Quality Data ◽

Literature Study ◽

High Quality ◽

Control Data ◽

Management Tools ◽

High Quality Data ◽

Content Type ◽

Data Production

Purpose – The purpose of this paper is to explore the main ontologies related to eMaintenance solutions and to study their application area. The advantages of using these ontologies to improve and control data quality will be investigated. Design/methodology/approach – A literature study has been done to explore the eMaintenance ontologies in the different areas. These ontologies are mainly related to content structure and communication interface. Then, ontologies will be linked to each step of the data production process in maintenance. Findings – The findings suggest that eMaintenance ontologies can help to produce a high-quality data in maintenance. The suggested maintenance data production process may help to control data quality. Using these ontologies in every step of the process may help to provide management tools to provide high-quality data. Research limitations/implications – Based on this study, it can be concluded that further research could broaden the investigation to identify more eMaintenance ontologies. Moreover, studying these ontologies in more technical details may help to increase the understandability and the use of these standards. Practical implications – It has been concluded in this study that applying eMaintenance ontologies by companies needs additional cost and time. Also the lack or the ineffective use of eMaintenance tools in many enterprises is one of the limitations for using these ontologies. Originality/value – Investigating eMaintenance ontologies and connecting them to maintenance data production is important to control and manage the data quality in maintenance.

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Data Quality Assessment

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch041 ◽

2009 ◽

pp. 378-384 ◽

Cited By ~ 2

Author(s):

Juliusz L. Kulikowski

Keyword(s):

Information Processing ◽

Data Quality ◽

Quality Assessment ◽

Quality Data ◽

Quality Of Data ◽

High Quality ◽

Data Quality Assessment ◽

Systems Effectiveness ◽

And Control ◽

Education Science

For many years the fact that for a high information processing systems’ effectiveness high quality of data is not less important than high systems’ technological performance was not widely understood and accepted. The way to understanding the complexity of data quality notion was also long, as it will be shown below. However, a progress in modern information processing systems development is not possible without improvement of data quality assessment and control methods. Data quality is closely connected both with data form and value of information carried by the data. High-quality data can be understood as data having an appropriate form and containing valuable information. Therefore, at least two aspects of data are reflected in this notion: 1st - technical facility of data processing, and 2nd - usefulness of information supplied by the data in education, science, decision making, etc.

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Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI

Remote Sensing ◽

10.3390/rs10111739 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1739 ◽

Cited By ~ 3

Author(s):

Xianxian Guo ◽

Le Wang ◽

Jinyan Tian ◽

Dameng Yin ◽

Chen Shi ◽

...

Keyword(s):

Data Quality ◽

Laser Scanning ◽

Structural Information ◽

Forest Growth ◽

Quality Data ◽

Estimation Accuracy ◽

High Quality ◽

High Quality Data ◽

Area Index ◽

Uav Image

Accurate measurement of the field leaf area index (LAI) is crucial for assessing forest growth and health status. Three-dimensional (3-D) structural information of trees from terrestrial laser scanning (TLS) have information loss to various extents because of the occlusion by canopy parts. The data with higher loss, regarded as poor-quality data, heavily hampers the estimation accuracy of LAI. Multi-location scanning, which proved effective in reducing the occlusion effects in other forests, is hard to carry out in the mangrove forest due to the difficulty of moving between mangrove trees. As a result, the quality of point cloud data (PCD) varies among plots in mangrove forests. To improve retrieval accuracy of mangrove LAI, it is essential to select only the high-quality data. Several previous studies have evaluated the regions of occlusion through the consideration of laser pulses trajectories. However, the model is highly susceptible to the indeterminate profile of complete vegetation object and computationally intensive. Therefore, this study developed a new index (vegetation horizontal occlusion index, VHOI) by combining unmanned aerial vehicle (UAV) imagery and TLS data to quantify TLS data quality. VHOI is asymptotic to 0.0 with increasing data quality. In order to test our new index, the VHOI values of 102 plots with a radius of 5 m were calculated with TLS data and UAV image. The results showed that VHOI had a strong linear relationship with estimation accuracy of LAI (R2 = 0.72, RMSE = 0.137). In addition, as TLS data were selected by VHOI less than different thresholds (1.0, 0.9, …, 0.1), the number of remaining plots decreased while the agreement between LAI derived from TLS and field-measured LAI was improved. When the VHOI threshold is 0.3, the optimal trade-off is reached between the number of plots and LAI measurement accuracy (R2 = 0.67). To sum up, VHOI can be used as an index to select high-quality data for accurately measuring mangrove LAI and the suggested threshold is 0.30.

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Data quality control and tools in passive seismic experiments exemplified on the Czech broadband seismic pool MOBNET in the AlpArray collaborative project

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-6-505-2017 ◽

2017 ◽

Vol 6 (2) ◽

pp. 505-521 ◽

Cited By ~ 9

Author(s):

Luděk Vecsey ◽

Jaroslava Plomerová ◽

Petr Jedlička ◽

Helena Munzarová ◽

Vladislav Babuška ◽

...

Keyword(s):

Quality Control ◽

Data Quality ◽

Seismic Data ◽

Quality Data ◽

Time Interval ◽

Large Set ◽

Data Quality Control ◽

High Quality ◽

High Quality Data ◽

Passive Seismic

Abstract. This paper focuses on major issues related to the data reliability and network performance of 20 broadband (BB) stations of the Czech (CZ) MOBNET (MOBile NETwork) seismic pool within the AlpArray seismic experiments. Currently used high-resolution seismological applications require high-quality data recorded for a sufficiently long time interval at seismological observatories and during the entire time of operation of the temporary stations. In this paper we present new hardware and software tools we have been developing during the last two decades while analysing data from several international passive experiments. The new tools help to assure the high-quality standard of broadband seismic data and eliminate potential errors before supplying data to seismological centres. Special attention is paid to crucial issues like the detection of sensor misorientation, timing problems, interchange of record components and/or their polarity reversal, sensor mass centring, or anomalous channel amplitudes due to, for example, imperfect gain. Thorough data quality control should represent an integral constituent of seismic data recording, preprocessing, and archiving, especially for data from temporary stations in passive seismic experiments. Large international seismic experiments require enormous efforts from scientists from different countries and institutions to gather hundreds of stations to be deployed in the field during a limited time period. In this paper, we demonstrate the beneficial effects of the procedures we have developed for acquiring a reliable large set of high-quality data from each group participating in field experiments. The presented tools can be applied manually or automatically on data from any seismic network.

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Improving Data Quality Using Amazon Mechanical Turk Through Platform Setup

Cornell Hospitality Quarterly ◽

10.1177/19389655211025475 ◽

2021 ◽

pp. 193896552110254

Author(s):

Lu Lu ◽

Nathan Neale ◽

Nathaniel D. Line ◽

Mark Bonn

Keyword(s):

Best Practices ◽

Data Quality ◽

Mechanical Turk ◽

Amazon Mechanical Turk ◽

Quality Data ◽

High Quality ◽

High Quality Data ◽

Amazon's Mechanical Turk ◽

Associated Data

As the use of Amazon’s Mechanical Turk (MTurk) has increased among social science researchers, so, too, has research into the merits and drawbacks of the platform. However, while many endeavors have sought to address issues such as generalizability, the attentiveness of workers, and the quality of the associated data, there has been relatively less effort concentrated on integrating the various strategies that can be used to generate high-quality data using MTurk samples. Accordingly, the purpose of this research is twofold. First, existing studies are integrated into a set of strategies/best practices that can be used to maximize MTurk data quality. Second, focusing on task setup, selected platform-level strategies that have received relatively less attention in previous research are empirically tested to further enhance the contribution of the proposed best practices for MTurk usage.

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