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Journal articles on the topic 'Data quality ‪(DQ)‬'

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Published: 7 June 2025
Last updated: 2 August 2025

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1

Aljumaili, Mustafa, Ramin Karim, and Phillip Tretten. "Metadata-based data quality assessment." VINE Journal of Information and Knowledge Management Systems 46, no. 2 (2016): 232–50. http://dx.doi.org/10.1108/vjikms-11-2015-0059.

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Purpose The purpose of this paper is to develop data quality (DQ) assessment model based on content analysis and metadata analysis. Design/methodology/approach A literature review of DQ assessment models has been conducted. A study of DQ key performances (KPIs) has been done. Finally, the proposed model has been developed and applied in a case study. Findings The results of this study shows that the metadata data have important information about DQ in a database and can be used to assess DQ to provide decision support for decision makers. Originality/value There is a lot of DQ assessment in th
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Syed, Rehan, Rebekah Eden, Tendai Makasi, et al. "Digital Health Data Quality Issues: Systematic Review." Journal of Medical Internet Research 25 (March 31, 2023): e42615. http://dx.doi.org/10.2196/42615.

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Background The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. Objective The aim of this study was to develop a
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Batini, C., T. Blaschke, S. Lang, et al. "DATA QUALITY IN REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 447–53. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-447-2017.

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The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb “DQ” identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO
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Anantharama, Nandini, Wray Buntine, and Andrew Nunn. "A Systematic Approach to Reconciling Data Quality Failures: Investigation Using Spinal Cord Injury Data." ACI Open 05, no. 02 (2021): e94-e103. http://dx.doi.org/10.1055/s-0041-1735975.

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Abstract Background Secondary use of electronic health record's (EHR) data requires evaluation of data quality (DQ) for fitness of use. While multiple frameworks exist for quantifying DQ, there are no guidelines for the evaluation of DQ failures identified through such frameworks. Objectives This study proposes a systematic approach to evaluate DQ failures through the understanding of data provenance to support exploratory modeling in machine learning. Methods Our study is based on the EHR of spinal cord injury inpatients in a state spinal care center in Australia, admitted between 2011 and 20
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Nazaire, Mare. "Integrating Data Quality Feedback: a Data Provider's Perspective." Biodiversity Information Science and Standards 2 (June 13, 2018): e26007. https://doi.org/10.3897/biss.2.26007.

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The Herbarium of Rancho Santa Ana Botanic Garden [RSA-POM] is the third largest herbarium in California and consists of >1.2 million specimens, of which ~50% are digitized. As a data provider, RSA-POM publishes its data with several aggregators, including the Consortium of California Herbaria, JSTOR, Symbiota (which is subsequently pulled into iDigBio and GBIF), as well as its own local webportal. Each submission of data needs to be prepared and formatted according to the aggregator's specifications for publication. Feedback on data quality (DQ) ranges from an individual user (often only a
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Paul, Deborah, and Nicole Fisher. "Challenges For Implementing Collections Data Quality Feedback: synthesizing the community experience." Biodiversity Information Science and Standards 2 (June 13, 2018): e26003. https://doi.org/10.3897/biss.2.26003.

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Much data quality (DQ) feedback is now available to data providers from aggregators of collections specimen and related data. Similarly, transcription centres and crowdsourcing platforms also provide data that must be assessed and often manipulated before uploading to a local database and subsequently published with aggregators. In order to facilitate broader DQ information use aggregators (GBIF, ALA, iDigBio, VertNet) and others, through the TDWG BDQ Interest Group, are harmonizing the DQ information provided - transforming part of the DQ feedback process. But, collections sharing data face c
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7

Veiga, Allan, and Antonio Saraiva. "Defining a Data Quality (DQ) profile and DQ report using a prototype of Node.js module of the Fitness for Use Backbone (FFUB)." Biodiversity Information Science and Standards 1 (August 14, 2017): e20275. https://doi.org/10.3897/tdwgproceedings.1.20275.

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Despite the increasing availability of biodiversity data, determing the quality of data and informing would-be data consumers and users remains a significant issue. In order for data users and data owners to perform a satisfactory assessment and management of data fitness for use, they require a Data Quality (DQ) report, which presents a set of relevant DQ measures, validations, and amendments assigned to data. Determining the meaning of "fitness for use" is essential to best manage and assess DQ. To tackle the problem, the TDWG Biodiversity Data Quality (BDQ) - Interest Group (IG) (https://gi
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Bian, Jiang, Tianchen Lyu, Alexander Loiacono, et al. "Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data." Journal of the American Medical Informatics Association 27, no. 12 (2020): 1999–2010. http://dx.doi.org/10.1093/jamia/ocaa245.

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Abstract Objective To synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet). Materials and Methods We started with 3 widely cited DQ literature—2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)—and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment
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9

Blake, Roger, and Ganesan Shankaranarayanan. "Discovering Data and Information Quality Research Insights Gained through Latent Semantic Analysis." International Journal of Business Intelligence Research 3, no. 1 (2012): 1–16. http://dx.doi.org/10.4018/jbir.2012010101.

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In the recent decade, the field of data and information quality (DQ) has grown into a research area that spans multiple disciplines. The motivation here is to help understand the core topics and themes that constitute this area and to determine how those topics and themes from DQ relate to business intelligence (BI). To do so, the authors present the results of a study which mines the abstracts of articles in DQ published over the last decade. Using Latent Semantic Analysis (LSA) six core themes of DQ research are identified, as well as twelve dominant topics comprising them. Five of these top
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Veiga, Allan, and Antonio Saraiva. "Toward a Biodiversity Data Fitness for Use Backbone (FFUB): A Node.js module prototype." Biodiversity Information Science and Standards 1 (August 14, 2017): e20300. https://doi.org/10.3897/tdwgproceedings.1.20300.

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Introduction: The Biodiversity informatics community has made important achievements regarding digitizing, integrating and publishing standardized data about global biodiversity. However, the assessment of the quality of such data and the determination of the fitness for use of those data in different contexts remain a challenge. To tackle such problem using a common approach and conceptual base, the TDWG Biodiversity Data Quality Interest Group - BDQ-IG (https://github.com/tdwg/bdq) has proposed a conceptual framework to define the necessary components to describe Data Quality (DQ) needs, DQ
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Nazaire, Mare. "Integrating Data Quality Feedback: a Data Provider's Perspective." Biodiversity Information Science and Standards 2 (June 13, 2018): e26007. http://dx.doi.org/10.3897/biss.2.26007.

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The Herbarium of Rancho Santa Ana Botanic Garden [RSA-POM] is the third largest herbarium in California and consists of >1.2 million specimens, of which ~50% are digitized. As a data provider, RSA-POM publishes its data with several aggregators, including the Consortium of California Herbaria, JSTOR, Symbiota (which is subsequently pulled into iDigBio and GBIF), as well as its own local webportal. Each submission of data needs to be prepared and formatted according to the aggregator’s specifications for publication. Feedback on data quality (DQ) ranges from an individual user (often onl
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12

Ehsani-Moghaddam, Behrouz, Ken Martin, and John A. Queenan. "Data quality in healthcare: A report of practical experience with the Canadian Primary Care Sentinel Surveillance Network data." Health Information Management Journal 50, no. 1-2 (2019): 88–92. http://dx.doi.org/10.1177/1833358319887743.

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Data quality (DQ) is the degree to which a given dataset meets a user’s requirements. In the primary healthcare setting, poor quality data can lead to poor patient care, negatively affect the validity and reproducibility of research results and limit the value that such data may have for public health surveillance. To extract reliable and useful information from a large quantity of data and to make more effective and informed decisions, data should be as clean and free of errors as possible. Moreover, because DQ is defined within the context of different user requirements that often change, DQ
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Weatherburn, Christopher J. "Data quality in primary care, Scotland." Scottish Medical Journal 66, no. 2 (2021): 66–72. http://dx.doi.org/10.1177/0036933021995965.

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Background and aims This project explores primary care data quality (DQ) across Scotland. Methods and results A survey was sent to primary care staff in winter 2019. National data regarding Quality and Outcomes Framework (QOF) performance indicators and the GP software system used was obtained, analysed with T-tests and Chi-squared tests. Overall QOF performance with non-financial incentives from 918 practices was 77%. There was no significant difference with overall QOF performance against GP system ( p = 0.46) or if the practice had a coder ( p = 0.06). From the survey, search systems that m
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Storz, Maximilian Andreas. "Does Self-Perceived Diet Quality Align with Nutrient Intake? A Cross-Sectional Study Using the Food Nutrient Index and Diet Quality Score." Nutrients 15, no. 12 (2023): 2720. http://dx.doi.org/10.3390/nu15122720.

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A reliable diet quality (DQ) assessment is critical to empower individuals to improve their dietary choices. Controversies persist as to whether self-perceived DQ is accurate and correlated with actual DQ as assessed by validated nutrient intake indexes. We used National Health and Nutrition Examination Surveys data to examine whether a higher self-perceived DQ was positively associated with a more optimal nutrient intake as reflected by the Food Nutrient Index (FNI) and Diet Quality Score (DQS). Comparative analyses were performed for three self-perceived DQ groups: (I) “excellent or very goo
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Paul, Deborah, and Nicole Fisher. "Challenges For Implementing Collections Data Quality Feedback: synthesizing the community experience." Biodiversity Information Science and Standards 2 (June 13, 2018): e26003. http://dx.doi.org/10.3897/biss.2.26003.

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Much data quality (DQ) feedback is now available to data providers from aggregators of collections specimen and related data. Similarly, transcription centres and crowdsourcing platforms also provide data that must be assessed and often manipulated before uploading to a local database and subsequently published with aggregators. In order to facilitate broader DQ information use aggregators (GBIF, ALA, iDigBio, VertNet) and others, through the TDWG BDQ Interest Group, are harmonizing the DQ information provided - transforming part of the DQ feedback process. But, collections sharing data face c
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Si, Shuyan, Wen Xiong, and Xingliang Che. "Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System." Applied Sciences 13, no. 19 (2023): 11020. http://dx.doi.org/10.3390/app131911020.

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Due to the rapid development of the mobile Internet and the Internet of Things, the volume of generated data keeps growing. The topic of data quality has gained increasing attention recently. Numerous studies have explored various data quality (DQ) problems across several fields, with corresponding effective data-cleaning strategies being researched. This paper begins with a comprehensive and systematic review of studies related to DQ. On the one hand, we classify these DQ-related studies into six types: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse dat
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Albrecht, F., T. Blaschke, S. Lang, et al. "PROVIDING DATA QUALITY INFORMATION FOR REMOTE SENSING APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 15–22. http://dx.doi.org/10.5194/isprs-archives-xlii-3-15-2018.

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The availability and accessibility of remote sensing (RS) data, cloud processing platforms and provided information products and services has increased the size and diversity of the RS user community. This development also generates a need for validation approaches to assess data quality. Validation approaches employ quality criteria in their assessment. Data Quality (DQ) dimensions as the basis for quality criteria have been deeply investigated in the database area and in the remote sensing domain. Several standards exist within the RS domain but a general classification – estab
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Sáez, Carlos, Oscar Zurriaga, Jordi Pérez-Panadés, Inma Melchor, Montserrat Robles, and Juan M. García-Gómez. "Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories." Journal of the American Medical Informatics Association 23, no. 6 (2016): 1085–95. http://dx.doi.org/10.1093/jamia/ocw010.

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Abstract Objective To assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ). Materials and Methods Novel probabilistic DQ control methods based on information theory and geometry are applied to the Public Health Mortality Registry of the Region of Valencia, Spain, with 512 143 entries from 2000 to 2012, disaggregated into 24 health departments. The methods provide DQ metrics and exploratory visualizations for (1) assessing the variability among multiple sources and (2) mon
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Rahimi, Alireza, Nandan Parameswaran, Pradeep Kumar Ray, Jane Taggart, Hairong Yu, and Siaw-Teng Liaw. "Development of a Methodological Approach for Data Quality Ontology in Diabetes Management." International Journal of E-Health and Medical Communications 5, no. 3 (2014): 58–77. http://dx.doi.org/10.4018/ijehmc.2014070105.

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The role of ontologies in chronic disease management and associated challenges such as defining data quality (DQ) and its specification is a current topic of interest. In domains such as Diabetes Management, a robust Data Quality Ontology (DQO) is required to support the automation of data extraction semantically from Electronic Health Record (EHR) and access and manage DQ, so that the data set is fit for purpose. A five steps strategy is proposed in this paper to create the DQO which captures the semantics of clinical data. It consists of: (1) Knowledge acquisition; (2) Conceptualization; (3)
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Khraiwesh, Husam, Buthaina Alkhatib, Hanan Hasan, Iman F. Mahmoud, and Lana M. Agraib. "The impact of sleep quality, meal timing, and frequency on diet quality among remote learning university students during the COVID-19 pandemic." International Journal of ADVANCED AND APPLIED SCIENCES 10, no. 5 (2023): 166–76. http://dx.doi.org/10.21833/ijaas.2023.05.020.

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Our objective is to assess the relationship between meal timing, frequency, sleep quality, and diet quality (DQ) among university students engaged in remote learning during the COVID-19 pandemic. To achieve this, a cross-sectional study was conducted in April 2021. We employed a self-administered electronic questionnaire to gather data. Participants self-reported their anthropometric and sociodemographic information. Physical activity (PA) levels were evaluated using the International PA Questionnaire (IPAQ), while sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). DQ
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Byabazaire, John, Gregory O’Hare, and Declan Delaney. "Data Quality and Trust: Review of Challenges and Opportunities for Data Sharing in IoT." Electronics 9, no. 12 (2020): 2083. http://dx.doi.org/10.3390/electronics9122083.

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Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard to access data quality using traditional assessments methods. This is exacerbated by the need to share data across different IoT domains as it increases the heterogeneity of the data. Data-shared IoT defines a new perspective of IoT applications which benefit from sharing data among different domains of IoT to create new use-case app
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Zaccaria, Gian Maria, Simone Ferrero, Samanta Rosati, et al. "Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of Clinical Outcomes." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–15. http://dx.doi.org/10.1200/cci.19.00049.

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PURPOSE Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points. METHODS Dat
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Chao, Li, Zhou Hui, and Zhou Xiaofeng. "Data quality assessment in hydrological information systems." Journal of Hydroinformatics 17, no. 4 (2015): 640–61. http://dx.doi.org/10.2166/hydro.2015.042.

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The hydrological data fed to hydrological decision support systems might be untimely, incomplete, inconsistent or illogical due to network congestion, low performance of servers, instrument failures, human errors, etc. It is imperative to assess, monitor and even control the quality of hydrological data residing in or acquired from each link of a hydrological data supply chain. However, the traditional quality management of hydrological data has focused mainly on intrinsic quality problems, such as outlier detection, nullity interpolation, consistency, completeness, etc., and could not be used
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Demuth, Anna, Joanna Ratajczak, Urszula Czerniak, and Katarzyna Antosiak-Cyrak. "Is Health Education among the Decisive Factors for the Diet Quality of Pregnant Women in Poland?" Nutrients 15, no. 11 (2023): 2627. http://dx.doi.org/10.3390/nu15112627.

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Health education (HE), an educational process that leads to increased nutritional awareness and improved health, is one of the factors influencing diet quality (DQ) during pregnancy. The aim was to evaluate the DQ of pregnant women and its determinants considering their HE. The study included 122 pregnant women aged 20–40 years. DQ was assessed using the Kom-PAN® questionnaire and the Pro-Healthy Diet Index (pHDI). Data collected included dietary habits, socio-demographic data, education level, place of residence, and maternal lifestyle-related characteristics, namely, pre-pregnancy weight, tr
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Suleman, Danladi, Rania Shibl, and Keyvan Ansari. "Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions." Smart Cities 6, no. 5 (2023): 2680–705. http://dx.doi.org/10.3390/smartcities6050121.

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Networking protocols have undergone significant developments and adaptations to cater for unique communication needs within the IoT paradigm. However, meeting these requirements in the context of vehicle-to-infrastructure (V2I) communications becomes a multidimensional problem due to factors like high mobility, intermittent connectivity, rapidly changing topologies, and an increased number of nodes. Thus, examining these protocols based on their characteristics and comparative analyses from the literature has shown that there is still room for improvement, particularly in ensuring efficiency i
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Scheirich, Daniel. "The ATLAS Tile Calorimeter Tools for Data Quality Assessment." EPJ Web of Conferences 251 (2021): 03018. http://dx.doi.org/10.1051/epjconf/202125103018.

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The ATLAS Tile Calorimeter (TileCal) is the central part of the hadronic calorimeter of the ATLAS experiment and provides important information for reconstruction of hadrons, jets, hadronic decays of tau leptons and missing transverse energy. The readout is segmented into nearly 10000 channels that are calibrated by means of Cesium source, laser, charge injection, and integratorbased systems. The data quality (DQ) relies on extensive monitoring of both collision and calibration data. Automated checks are performed on a set of pre-defined histograms and results are summarized in dedicated web p
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Guerra García, César Arturo, Ismael Caballero, Marco Cardenas Juarez, and José Reyes Juárez Ramírez. "A proposal to consider aspects of quality in the software development." Journal on Advances in Theoretical and Applied Informatics 2, no. 2 (2016): 12. http://dx.doi.org/10.26729/jadi.v2i2.2103.

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 Users need trusting in data managed by software applications that are part of Information Systems (IS), which supposes that organizations should assuring adequate levels of quality in data that are managed in their IS. Therefore, the fact that an IS can manage data with an adequate level of quality should be a basic requirement for all organizations. In order to reach this basic requirement some aspects and elements related with data quality (DQ) should be taken in account from the earliest stages of development of software applications, i.e. “data quality by design”. Sinc
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K., Dharani* &. Dr. G. Abel Thangaraja**. "BIG DATA PREPROCESSING USING ENHANCED DATA QUALITY RULES DISCOVERY MODEL (EDQRM)." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 2 (2023): 33–41. https://doi.org/10.5281/zenodo.8428545.

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In the Big Data Era, data is the center for any governmental, institutional, and private organization. Endeavors were equipped towards extricating profoundly important bits of knowledge that can't occur assuming data is of low quality. Hence, data quality (DQ) is considered as a vital component in big data processing. In this stage, bad quality data isn't entered to the Big Data value chain. This paper, proposed the Enhanced data quality Rules discovery model (EDQRM) for assessment of quality and Big Data pre-processing. EDQRM discovery model to improve and precisely focus on the pre-p
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Yeh, Peter, Colin Puri, Mark Wagman, and Ajay Easo. "Accelerating the Discovery of Data Quality Rules: A Case Study." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 2 (2011): 1707–14. http://dx.doi.org/10.1609/aaai.v25i2.18865.

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Poor quality data is a growing and costly problem that af- fects many enterprises across all aspects of their business ranging from operational efficiency to revenue protection. In this paper, we present an application – Data Quality Rules Accelerator (DQRA) – that accelerates Data Quality (DQ) efforts (e.g. data profiling and cleansing) by automatically discovering DQ rules for detecting inconsistencies in data. We then present two evaluations. The first evaluation compares DQRA to existing solutions; and shows that DQRA either outperformed or achieved performance comparable with these soluti
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Kapsner, Lorenz A., Jonathan M. Mang, Sebastian Mate, et al. "Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository." Applied Clinical Informatics 12, no. 04 (2021): 826–35. http://dx.doi.org/10.1055/s-0041-1733847.

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Abstract Background Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess D
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Boulton, Christopher, Carol Harrison, Timothy Wilton, et al. "Implementing large-scale data quality validation in a national arthroplasty registry to improve compliance." Bone & Joint Open 3, no. 9 (2022): 716–25. http://dx.doi.org/10.1302/2633-1462.39.bjo-2022-0051.r1.

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Data of high quality are critical for the meaningful interpretation of registry information. The National Joint Registry (NJR) was established in 2002 as the result of an unexpectedly high failure rate of a cemented total hip arthroplasty. The NJR began data collection in 2003. In this study we report on the outcomes following the establishment of a formal data quality (DQ) audit process within the NJR, within which each patient episode entry is validated against the hospital unit’s Patient Administration System and vice-versa. This process enables bidirectional validation of every NJR entry a
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Dai, Guangyao, Songhua Wu, Wenrui Long, et al. "Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1." Atmospheric Measurement Techniques 17, no. 7 (2024): 1879–90. http://dx.doi.org/10.5194/amt-17-1879-2024.

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Abstract. The new-generation atmospheric environment monitoring satellite DQ-1, launched successfully in April 2022, carries the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The ACDL/DQ-1 is a high-spectral-resolution lidar (HSRL) that separates molecular backscatter signals using an iodine filter and has 532 nm polarization detection and dual-wavelength detection at 532 and 1064 nm, which can be utilized to derive aerosol optical properties. The methods have been specifically developed for data proce
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Małachowska, Aleksandra, Jerzy Gębski, and Marzena Jeżewska-Zychowicz. "Childhood Food Experiences and Selected Eating Styles as Determinants of Diet Quality in Adulthood—A Cross-Sectional Study." Nutrients 15, no. 10 (2023): 2256. http://dx.doi.org/10.3390/nu15102256.

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Available studies suggest that childhood food experiences (CFE) may be linked with eating behaviors in adulthood, as well as eating style (ES); thus, both CFE and ES can determine dietary intake. Little is known about the role of both of these factors in explaining the diet quality (DQ) of adults. The aim was to investigate to what extent selected ESs, i.e., intuitive (IE), restrained (ResEat), and external (ExtEat) eating, and CFE related to parental feeding practices (PFPs) will predict the DQ of women and men. Data from 708 Polish adults (477 women and 231 men) aged 18–65 were collected via
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Lakshmanasamy, Rameshbabu, and Girish Ganachari. "Data Integrity Problems in High-Volume High-Velocity Data Ingestion." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem14175.

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In the era of bigdata, and never ending data push from IoT devices, the IT infrastructure are built to be scalable to handle the huge batch loads or continuous streaming live data. However, the big question is how can be establish the data integrity. How can we make sure no data is lost from Origin till the destination passing through numerous touch points enroute ? How can we ensure the quality with continuous inflow ? Should the inflow be suspended to perform the DQ checks? Or should it be a totally independent parallel activity. Let’s explore. Key words: Quality Data Management, Data Pipeli
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Benkhaled, Hamid Naceur, Djamel Berrabah, and Faouzi Boufares. "Data Warehouses and Big Data." International Journal of Organizational and Collective Intelligence 10, no. 3 (2020): 1–13. http://dx.doi.org/10.4018/ijoci.2020070101.

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Before the arrival of the Big Data era, data warehouse (DW) systems were considered the best decision support systems (DSS). DW systems have always helped organizations around the world to analyse their stored data and use it in making decisive decisions. However, analyzing and mining data of poor quality can give the wrong conclusions. Several data quality (DQ) problems can appear during a data warehouse project like missing values, duplicates values, integrity constrains issues and more. As a result, organizations around the world are more aware of the importance of data quality and invest a
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Xia Na and Syarmila Hany Haron. "Influence of Kindergarten Space Design on Development Quotient of Young Children." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 3 (2023): 534–49. http://dx.doi.org/10.37934/araset.32.3.534549.

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The design of kindergarten spaces significantly impacts young children’s physical and mental development. This research considers the influence of three dimensions of kindergarten space design, specific area, quality and use, on the development quotient (DQ) of young children. It considers this through regression and correlation analysis applied to survey data related to the spatial design of the three kindergartens in the aforementioned three dimensions, along with the DQ test scores of 270 young children. The results reveal a significant positive correlation between the three dimensions (are
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Bin Zarah, Aljazi, and Jeanette Mary Andrade. "Elevated Inflammation and Poor Diet Quality Associated with Lower eGFR in United States Adults: An NHANES 2015–2018 Analysis." Nutrients 16, no. 4 (2024): 528. http://dx.doi.org/10.3390/nu16040528.

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Chronic kidney disease is prevalent within the United States likely due to dietary habits. The purpose of this study was to examine the relationship between the high-sensitivity c-reactive protein (hs-CRP) and diet quality (DQ) and their effect on the eGFR. A cross-sectional secondary data analysis study was conducted among adults (n = 6230) using NHANES 2015–2018 data. DQ was determined by the Healthy Eating Index-2015 (HEI-2015). Multivariable linear regressions were conducted based on eGFR (≥90 or <60 mL/min/1.73 m2) after adjustments for age, race/ethnicity, hypertension, diabetes melli
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Navaz, Alramzana Nujum, Mohamed Adel Serhani, Hadeel T. El El Kassabi, and Ikbal Taleb. "Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling." Sensors 23, no. 14 (2023): 6443. http://dx.doi.org/10.3390/s23146443.

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Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that the
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Gaal, Szilvia, Maeve A. Kerr, Mary Ward, Helene McNulty, and M. Barbara E. Livingstone. "Breakfast Consumption in the UK: Patterns, Nutrient Intake and Diet Quality. A Study from the International Breakfast Research Initiative Group." Nutrients 10, no. 8 (2018): 999. http://dx.doi.org/10.3390/nu10080999.

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Breakfast consumption is associated with higher overall dietary adequacy; however, there is a lack of quantitative guidelines for optimal nutrient intakes at breakfast in the UK. This study aimed to investigate nutrient and food group intakes at breakfast and examine their relationship to overall Diet Quality (DQ). Data from the most recent National Diet and Nutrition Survey (NDNS, 2008–2014) were accessed to provide a representative sample (n = 8174) of the UK population, aged 5–96 years, mean age of 33 years. Food intake was measured by a 4-day estimated food diary and DQ was assessed by the
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Jeans, Matthew R., Fiona M. Asigbee, Matthew J. Landry, et al. "Breakfast Consumption in Low-Income Hispanic Elementary School-Aged Children: Associations with Anthropometric, Metabolic, and Dietary Parameters." Nutrients 12, no. 7 (2020): 2038. http://dx.doi.org/10.3390/nu12072038.

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Breakfast consumption is associated with lower obesity prevalence and cardiometabolic risk and higher dietary quality (DQ) in children. Low-income, Hispanic populations are disproportionately affected by obesity and cardiometabolic risks. This study examined the relationship between breakfast consumption groups (BCG) on anthropometric, metabolic, and dietary parameters in predominately low-income, Hispanic children from 16 Texas schools. Cross-sectional data were from TX Sprouts, a school-based gardening, nutrition, and cooking randomized controlled trial. Anthropometric measurements included
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Bin Zarah, Aljazi, Mary Carissa Feraudo, and Jeanette Mary Andrade. "Development and Relative Validity of the Chronic Kidney Disease Short Food Frequency Questionnaire (CKD SFFQ) to Determine Diet Quality and Dietary Habits among Adults with Chronic Kidney Disease." Nutrients 13, no. 10 (2021): 3610. http://dx.doi.org/10.3390/nu13103610.

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Limited instruments are available to determine diet quality among US adults with chronic kidney disease (CKD). The purpose of this study was two-fold: (1) to develop a food frequency questionnaire, CKD SFFQ, for adults with CKD and (2) to validate the CKD SFFQ against two 24-h recalls in determining diet quality (DQ). A 57-item CKD SFFQ was developed through a content validation method. Adults with CKD (n = 46) completed the CKD SFFQ and 2–24-h recalls. Statistical analyses included descriptive statistics, frequencies, t-tests, Pearson correlations, and Bland–Altman plots. All data were analyz
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Veiga, Allan, and Antonio Saraiva. "Defining a Data Quality (DQ) profile and DQ report using a prototype of Node.js module of the Fitness for Use Backbone (FFUB)." Proceedings of TDWG 1 (August 14, 2017): e20275. http://dx.doi.org/10.3897/tdwgproceedings.1.20275.

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Buelvas, Julio H., Danny Múnera, and Natalia Gaviria. "DQ-MAN: A tool for multi-dimensional data quality analysis in IoT-based air quality monitoring systems." Internet of Things 22 (July 2023): 100769. http://dx.doi.org/10.1016/j.iot.2023.100769.

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Jatiningrum, Citrawati, Fauzi Fauzi, Bernaditha H. S. Utami, and Aza Azlina Md Kassim. "MITIGATE TYPE II AGENCY CONFLICT THROUGH GOOD CORPORATE GOVERNANCE AND DISCLOSURE QUALITY." AKUNTABILITAS 17, no. 1 (2023): 1–16. http://dx.doi.org/10.29259/ja.v17i1.19695.

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Empirical evidence on type II conflicts between controlling shareholders versus minority shareholders has not been extensively explored. This study gives new evidence on the agency conflict in a scenario of highly concentrated ownership. This study aims to examine the effect of Good Corporate Governance mechanism with quality of disclosure on concentrated ownership context The sample were drawn from companies listed on the Indonesia Stock Exchange (IDX). The data were analyzed with panel data regression. The results shows that CG mechanisms negatively effects with DQ. However, looking at each
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Perez-Castillo, Ricardo, Ana Carretero, Ismael Caballero, et al. "DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data." Sensors 18, no. 9 (2018): 3105. http://dx.doi.org/10.3390/s18093105.

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The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutio
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Colby, Sarah, Wenjun Zhou, Chelsea Allison, et al. "Development and Validation of the Short Healthy Eating Index Survey with a College Population to Assess Dietary Quality and Intake." Nutrients 12, no. 9 (2020): 2611. http://dx.doi.org/10.3390/nu12092611.

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Because diet quality (DQ) is associated with risk of chronic disease and is a common construct assessed in health-related research, validated tools to assess DQ are needed that have low respondent and researcher burden. Thus, content experts develop the Short Healthy Eating Index (sHEI) tool and an associated scoring system. The sHEI scoring system was then refined using a classification and regression tree (CRT) algorithm methodology with an iterative feedback process with expert review and input. The sHEI scoring system was then validated using a concurrent criterion validation process that
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Drewnowski, Adam, Colin Rehm, and Florent Vieux. "Breakfast in the United States: Food and Nutrient Intakes in Relation to Diet Quality in National Health and Examination Survey 2011–2014. A Study from the International Breakfast Research Initiative." Nutrients 10, no. 9 (2018): 1200. http://dx.doi.org/10.3390/nu10091200.

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The contribution of breakfast to diet quality (DQ) can inform future dietary guidelines. This study examined breakfast nutrition in relation to overall DQ, using dietary data from the first reported day of the National Health and Examination Survey (NHANES) 2011–2014 (n = 14,488). Relative DQ was assessed using the Nutrient Rich Foods Index (NRF9.3) and the USDA Healthy Eating Index 2015 (HEI 2015). The sample was stratified by NRF9.3 tertiles and by age and socioeconomic groups. Four out of 5 NHANES participants had breakfast on the day of the interview. Breakfast provided 19–22% of dietary e
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Bharot, Nitesh, Priyanka Verma, Mirco Soderi, and John G. Breslin. "DQ-DeepLearn: Data Quality Driven Deep Learning Approach for Enhanced Predictive Maintenance in Smart Manufacturing." Procedia Computer Science 232 (2024): 574–83. http://dx.doi.org/10.1016/j.procs.2024.01.057.

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Shen, Junxin, Shuilan Zhou, and Fanghao Xiao. "Research on Data Quality Governance for Federated Cooperation Scenarios." Electronics 13, no. 18 (2024): 3606. http://dx.doi.org/10.3390/electronics13183606.

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Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, a
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Xie, Jingyi, and Bin Wang. "Whether joint leisure time physical activity and dietary quality alleviates metabolic syndrome and its components: evidence from the National Health and Nutrition Examination Survey (2007–2018)." PLOS One 20, no. 5 (2025): e0322608. https://doi.org/10.1371/journal.pone.0322608.

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Objectives The incidence of metabolic syndrome (MetS) is increasing, which is one of the major threats to human health. Whether joint leisure time physical activity (LTPA) and dietary quality (DQ) can reduce the risk of developing MetS and its components is worth exploring. Therefore, this study aimed to investigate the individual and combined effects of LTPA and DQ on MetS and its components. Methods Data were extracted from the National Health and Nutrition Examination Survey from 2007 to 2018. LTPA was classified as inactive, insufficiently active (IA), weekend warrior (WW), and regular act
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