Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases.
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ABSTRACT:
SUBMITTER: Chen Q
PROVIDER: S-EPMC7646089 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases.
Chen Qingyu Q Britto Ramona R Erill Ivan I Jeffery Constance J CJ Liberzon Arthur A Magrane Michele M Onami Jun-Ichi JI Robinson-Rechavi Marc M Sponarova Jana J Zobel Justin J Verspoor Karin K
Genomics, proteomics & bioinformatics 20200401 2
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Project description:https://www.wwpdb.org/.
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Project description:BackgroundClinical trial registries have been established as a form of public accountability. Sponsors ought to register their trials promptly and accurately, but this is not always done. Some of the problems include non-registration of trials, registration of trials with incomplete information, and non-reporting of trial results on time. In this study we enumerate or quantify some quality issues with respect to Principal Investigator (PI) and Responsible Party data.MethodsWe analyzed interventional trials registered with ClinicalTrials.gov. Using certain selection criteria, we started with 112,013 records, and then applied further filters. The trial had to (a) start between 1 January 2005 and 31 December 2014, (b) include a "drug" or "biological" in the "intervention" field, (c) be registered with an American authority, and (d) list a real person's name as investigator and also his or her role in the study.ResultsWe identified four categories of errors in the ClinicalTrials.gov records. First, some data were missing. The name of the investigator, or his or her role, was missing in 12% of 35,121 trials. In examining 71,359 pairs of names and roles, 17% of the "names" were found to be not those of real persons, but instead junk information. Second, there were variations in a large number of names. We identified 19 categories of variants. We determined that 13% of the names had variants that could not be resolved using a program. Third, some trials listed many PIs each, although only one such person holds overall responsibility for the trial and therefore not more than one person should be listed as PI. Fourth, in examining whether the PI's name was available as part of the Responsible Party tag, we found that in 1221 (3.5%) of 35,121 trials, the Responsible Party tag is absent.ConclusionsWe have outlined four categories of problems with data hosted by ClinicalTrials.gov and have quantified three of them. We also suggest how these errors could be prevented in future. It is important to carry out various kinds of audits of trial registries, in order to identify lacunae in the records, that they be addressed.
| S-EPMC6591874 | biostudies-literature
Project description:BackgroundThe 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.ObjectiveThe aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?MethodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.ResultsThe digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.ConclusionsThe DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.
| S-EPMC10131725 | biostudies-literature
Project description:BACKGROUND:ChIP (Chromatin immunoprecipitation)-exo has emerged as an important and versatile improvement over conventional ChIP-seq as it reduces the level of noise, maps the transcription factor (TF) binding location in a very precise manner, upto single base-pair resolution, and enables binding mode prediction. Availability of numerous peak-callers for analyzing ChIP-exo reads has motivated the need to assess their performance and report which tool executes reasonably well for the task. RESULTS:This study has focussed on comparing peak-callers that report direct binding events with those that report indirect binding events. The effect of strandedness of reads and duplication of data on the performance of peak-callers has been investigated. The number of peaks reported by each peak-caller is compared followed by a comparison of the annotated motifs present in the reported peaks. The significance of peaks is assessed based on the presence of a motif in top peaks. Indirect binding tools have been compared on the basis of their ability to identify annotated motifs and predict mode of protein-DNA interaction. CONCLUSION:By studying the output of the peak-callers investigated in this study, it is concluded that the tools that use self-learning algorithms, i.e. the tools that estimate all the essential parameters from the aligned reads, perform better than the algorithms which require formation of peak-pairs. The latest tools that account for indirect binding of TFs appear to be an upgrade over the available tools, as they are able to reveal valuable information about the mode of binding in addition to direct binding. Furthermore, the quality of ChIP-exo reads have important consequences on the output of data analysis.
| S-EPMC7035708 | biostudies-literature
Project description:BackgroundIn recent years, online physician-rating websites have become prominent and exert considerable influence on patients' decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, there is a need to examine the various data quality issues with physician-rating websites.ObjectiveThis study's objective was to identify and categorize the data quality issues afflicting physician-rating websites by reviewing the literature on online patient-reported physician ratings and reviews.MethodsWe performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. The search was limited to quantitative, qualitative, and mixed-method papers published in the English language from 2001 to 2020.ResultsA total of 423 articles were screened. From these, 49 papers describing 18 unique data quality issues afflicting physician-rating websites were included. Using a data quality framework, we classified these issues into the following four categories: intrinsic, contextual, representational, and accessible. Among the papers, 53% (26/49) reported intrinsic data quality errors, 61% (30/49) highlighted contextual data quality issues, 8% (4/49) discussed representational data quality issues, and 27% (13/49) emphasized accessibility data quality. More than half the papers discussed multiple categories of data quality issues.ConclusionsThe results from this review demonstrate the presence of a range of data quality issues. While intrinsic and contextual factors have been well-researched, accessibility and representational issues warrant more attention from researchers, as well as practitioners. In particular, representational factors, such as the impact of inline advertisements and the positioning of positive reviews on the first few pages, are usually deliberate and result from the business model of physician-rating websites. The impact of these factors on data quality has not been addressed adequately and requires further investigation.
| S-EPMC7551103 | biostudies-literature
Project description:Nutrient databases are a critical component of nutrition science and the basis of exciting new research in precision nutrition (PN). To identify the most critical components needed for improvement of nutrient databases, food composition data were analyzed for quality, with completeness being the most important measure, and for FAIRness, how well the data conformed with the data science criteria of findable, accessible, interoperable, and reusable (FAIR). Databases were judged complete if they provided data for all 15 nutrition fact panel (NFP) nutrient measures and all 40 National Academies of Sciences, Engineering, and Medicine (NASEM) essential nutrient measures for each food listed. Using the gold standard the USDA standard reference (SR) Legacy database as surrogate, it was found that SR Legacy data were not complete for either NFP or NASEM nutrient measures. In addition, phytonutrient measures in the 4 USDA Special Interest Databases were incomplete. To evaluate data FAIRness, a set of 175 food and nutrient data sources were collected from worldwide. Many opportunities were identified for improving data FAIRness, including creating persistent URLs, prioritizing usable data storage formats, providing Globally Unique Identifiers for all foods and nutrients, and implementing citation standards. This review demonstrates that despite important contributions from the USDA and others, food and nutrient databases in their current forms do not yet provide truly comprehensive food composition data. We propose that to enhance the quality and usage of food and nutrient composition data for research scientists and those fashioning various PN tools, the field of nutrition science must step out of its historical comfort zone and improve the foundational nutrient databases used in research by incorporating data science principles, the most central being data quality and data FAIRness.
| S-EPMC10201679 | biostudies-literature
Project description:ObjectiveWe sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy.Materials and methodsWe assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a racially and ethnically diverse population. We compared race and ethnicity data collected via administrative processes with data recorded directly by respondents via paper surveys (National Health and Nutrition Examination Survey and Hospital Consumer Assessment of Healthcare Providers and Systems). Respondent-recorded data were considered the gold standard for the collection of race and ethnicity information.ResultsAmong the 160 million patients from the Healthcare Cost and Utilization Project and Optum Labs datasets, race or ethnicity was unknown for 25%. Among the 2.4 million patients in the single New York City healthcare system's EHR, race or ethnicity was unknown for 57%. However, when patients directly recorded their race and ethnicity, 86% provided clinically meaningful information, and 66% of patients reported information that was discrepant with the EHR.DiscussionRace and ethnicity data are critical to support precision medicine initiatives and to determine healthcare disparities; however, the quality of this information in observational databases is concerning. Patient self-recording through the use of patient-facing tools can substantially increase the quality of the information while engaging patients in their health.ConclusionsPatient self-recording may improve the completeness of race and ethnicity information.
| S-EPMC6696496 | biostudies-literature
Project description:ObjectivesHigh-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions.SettingsOn 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained.ParticipantsAll COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June.Primary and secondary outcome measuresData completeness and consistency.ResultsDGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily.ConclusionsImportant quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.
| S-EPMC8649880 | biostudies-literature
Project description:Milk and dairy products are an essential food and an economic resource in many countries. Milk component synthesis and secretion by the mammary gland involve expression of a large number of genes whose nutritional regulation remains poorly defined. We aim at understanding the genomic influence on milk quality and synthesis by comparing two sheep breeds, with different milking attitude, Sarda and Gentile di Puglia, using sheep-specific microarray technology. From sheep ESTs deposited at NCBI, we generated the first annotated microarray developed for sheep with a covering of most of the genome.
2009-11-30 | GSE18619 | GEO
Project description:Milk and dairy products are an essential food and an economic resource in many countries. Milk component synthesis and secretion by the mammary gland involve expression of a large number of genes whose nutritional regulation remains poorly defined. We aim at understanding the genomic influence on milk quality and synthesis by comparing two sheep breeds, with different milking attitude, Sarda and Gentile di Puglia, using sheep-specific microarray technology. From sheep ESTs deposited at NCBI, we generated the first annotated microarray developed for sheep with a covering of most of the genome. Whole tissue samples of mammary gland were collected from 4 lactating individuals of two sheep (Ovis aries) breeds, Gentile di Puglia and Sarda. Biopsies of lactating mammary tissue were taken at two lactation stages (first record, stage 01: 6 days after lambing; second record, stage 02: 44 days after lambing) in both breeds. Tissues from mammary gland were immersed in RNAlater (Sigma) immediately after biopsy and stored at -20°C.
2010-05-19 | E-GEOD-18619 | biostudies-arrayexpress