Data Quality Analysis Research Articles

Improving injury surveillance data quality: a study based on hospitals contributing to the Victorian Emergency Minimum Dataset

ObjectiveIn this paper, we describe the design and baseline data of a study aimed at improving injury surveillance data quality of hospitals contributing to the Victorian Emergency Minimum Dataset (VEMD). MethodsThe sequential study phases include a baseline analysis of data quality, direct engagement and communication with each of the emergency department (ED) hospital sites, collection of survey and interview data and ongoing monitoring. ResultsIn 2019/20, there were 371,683 injury‐related ED presentations recorded in the VEMD. Percentage unspecified, the indicator of (poor) data quality, was lowest for ‘body region’ (2.7%) and ‘injury type’ (7.4%), and highest for ‘activity when injured’ (29.4%). In the latter, contributing hospitals ranged from 3.0–99.9% unspecified. The ‘description of event’ variable had a mean word count of 10; 16/38 hospitals had a narrative word count of <5. ConclusionsBaseline hospital injury surveillance data vary vastly in data quality, leaving much room for improvement and justifying intervention as described. Implications for public healthHospital engagement and feedback described in this study is expected to have a marked effect on data quality from 2021 onwards. This will ensure that Victorian injury surveillance data can fulfil their purpose to accurately inform injury prevention policy and practice.

Seismic Data Quality Analysis Based on Image Recognition Using Convolutional Neural Network

Seismometer monitoring and evaluation activities at the Indonesia Tsunami Early Warning System (InaTEWS) station can be carried out through a seismometer sensor calibration system with the use of the software of Seismic Data Quality Analysis. The software output is in the form of a spectrum image that represents the conditions of the seismometer following the spectrum results. The identification of the seismometer condition can be made by pattern recognition in the spectrum image. This study employed a neural network, specifically the Convolutional Neural Network (CNN), to analyse the pattern condition. The test results show that the performance of the system will be excellent if 1024 hidden layers are used. In addition, the epoch test shows that the system works well when given a maximum epoch value of 50. The test of image size gives the result that the system performance will result in good using input with a size of 30x20 pixels. The final results of the classification of spectrum images using CNN will exhibit the identification of seismometer. For the validation, the confusion matrix test shows that the corresponding findings are 80%, while the conflicting results are 20%.

0020 Investigation of Seasonal Changes in Self-Reported Sleep Quality and Psychomotor Vigilance Task Outcomes: Results From the Ultra Long-Term Sleep (ULTS) Study

Abstract Introduction Although sleep is fundamental for human well-being, factors that contribute to an individual’s experience and report of sleep quality remain poorly understood. Utilizing that sleepiness is known to impact vigilance performance, this study sets out to explore how self-reported sleep quality changes with behavioral performance and how this variation is affected by seasonal changes. Methods This work is an interim analysis of self-reported sleep quality and behavioral performance data collected in the Ultra Long-term Sleep (ULTS) study (ClinicalTrials.gov Identifier: NCT04513743). In the study 20 healthy participants (average 33±13 years of age) were enrolled for 365 continuous days to observe the seasonal variation in sleep and cognitive performance. The outcome from the daily psychomotor vigilance task (PVT) and sleep questionnaire is analyzed and reported. The sleep questionnaire is a composite of questions from the Sleep Satisfaction Tool, the Karolinska Sleep Diary, questions regarding feelings of pain and outside disturbances, easiness of waking up, and the Karolinska Sleepiness Scale (KSS). The PVT was designed for self-administered high-frequency testing and has a short 3-minute test period. Monthly changes were examined from June to November. This period was chosen since all subjects were active. Results The first eight participants have now completed the study. Repeated measures correlations between KSS and mean PVT reaction time showed moderate but highly robust associations between the two measures over time (r=0.2; 95% CI 0.18-0.23). From the data collected thus far from the entire population, fastest PVT mean reaction times were found on Saturdays and slowest on Tuesdays. Similarly, KSS had best scores in weekends. There was an overall increase in mean PVT reaction time during the investigated period from June to November. This was also observed in KSS. Conclusion Our findings show a moderate correlation between the mean PVT reaction time and Karolinska Sleepiness Scale with up to 365 datapoints per subject with weekly and seasonal trends observed. Support (If Any) This research is supported by Innovation Fund Denmark.

Making EHRs Trustable: A Quality Analysis of EHR-Derived Datasets for COVID-19 Research.

One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried out.

Electronic transfusion consent and blood delivering pattern improve the management of blood bank in China

BackgroundThe aim of this study was to improve the blood transfusion treatment consent accuracy, simplify the verification process, prolong the temperature control time before the blood transfusion, and save the blood transportation labor cost.MethodsWe designed the blood transfusion consent electronic signing process, which can generate personalized the text content and can automatically check the filling accuracy. The signal can be transmitted to the blood transfusion management system (TMS) to relieving the blood distribution. For blood delivering pattern, we established the blood transport center, recruited full-time nurses and used temperature-controlled blood transfer boxes to deliver blood in batches on a regular basis.ResultsA quarterly data analysis of blood transfusion quality showed a 100% blood transfusion consent accuracy after an electronic signing process was implemented. The average confirmation time savings between the electronic content and paper content was 26 min for the Department of Emergency (estimated difference 95% CI = 26 (20 to 36), p < 0.05). The blood delivering pattern reduced the time for each unit by leaving the average temperature control by 7.24 min (estimated difference 95% CI = 7.24 (6.92 to 7.56), p < 0.05). Furthermore, $3.67 was saved for the blood transportation labor cost for each unit as well.ConclusionBlood transfusion consent electronic signing process not only ensures the accuracy, but also saves the verification time. Moreover, the blood delivering pattern prolongs the blood temperature control time and saves blood transportation labor costs. Thus, these two improvements could enhance transfusion management.

Application of Decision Tree Algorithm in Teaching Quality Analysis of Physical Education

Physical education is not only an important part of national education, but also one of the important means to improve the physical quality of students and citizens. With the rapid innovation of information technology in China, the decision tree algorithm is introduced into the teaching quality analysis of physical education, which becomes the new reform thinking direction of PE teaching, and promotes the teaching method and the training way of coaches. Under the background of new era, in this paper, the basic principle and realization process of decision tree algorithm is analyzed, applying it to the data analysis of the teaching quality of physical education. The computer is used to excavate the factors that influence the teaching of physical education. And the results show that the method can classify the data well, and the acquired knowledge is of great significance to the teaching reform of the physical education class in the future. Through the rational evaluation of teachers’ overall teaching quality, teachers can optimize the teaching process according to the evaluation results, find the weak links of students’ learning in time, remind students to review and consolidate relevant knowledge, help students improve their study and examination scores, and further improve their impact on physical education teaching behavior.

Data Quality Analysis of China Permanent Seismic Network by Repeating Earthquakes

Abstract Highly similar waveforms recorded from repeating earthquakes can be utilized to evaluate the data quality of a seismic station. We used a hypothesis testing method to establish a data quality detection model based on repeating earthquakes. The model effectiveness was verified by using waveforms of a pair of repeating earthquakes, which occurred in northeastern Japan on 20 March 2021 (Mw=7.0) and 1 May 2021 (Mw=6.9), from 109 stations in the Global Seismographic Network. A total of 842 permanent broadband stations in mainland China were evaluated using this model. Eighteen anomalies were found mainly attributed to calibration, instrument noise, mass recentering, and regional long-period interference. We found that most of the stations function well. Moreover, utilizing repeating earthquakes to analyze the waveform quality can circumvent the need for extensive forward calculations, as well as greatly reduce the influence of source parameter uncertainties and structural complexity on the seismogram. Additionally, the need for detection in other datasets in different regional networks has broadened the scope of these applications.

An adaptive data cleaning framework: a case study of the water quality monitoring system in China

Robust detection of patterns at low signal-to-noise ratios (SNR) is a fundamental challenge of analyzing high-frequency data, particularly in water quality monitoring. When conducting water quality data analysis, an indispensable step is to clean the data noise. In this paper, a new method named ADAPTIVE-EWT-MFE based on empirical wavelet transform (EWT) and multi-scale fuzzy entropy (MFE) is proposed to implement time series data cleaning. EWT-MFE can decompose the spectrum into different inherent mode functions (IMFs). According to different characteristics of the IMFs, an adaptive and adjustable parameter based on MFE, which reflects the intrinsic characteristics is introduced into the threshold function to improve the performance of noise-cleaning. Finally, this hybrid data cleaning method is designed to filter the high-frequency noise on the IMFs. Results show that our proposed method not only has great potential to improve the better noise-cleaning performance but also does not distort noise-free data.

A graph-based data quality analysis in distributed telemedicine systems

Abstract Telemedicine is one of the most rapidly developing areas of healthcare and it plays an increasing role in modern medicine. As the amount of data and demand for features increase, the data paths are becoming ever-more complex. Owing to this, it is vital in telemedicine to find a proper balance between consistency and availability under any given circumstances. However, making a trade-off can significantly influence the quality of the data. This study seeks to get an in-depth view of the problem by considering a real-world telemedicine use-case and elaborating the formal system specification of the scenario. After evaluating the specification, the constructed state graph is examined using graph coloring and other graph algorithms.

Laundry greywater treatment by anaerobic filters and vertical flow constructed wetlands

AbstractLaundry processes are huge water consumers and contribute to wastewaters in high volume. In the current report, treatment of greywater (GW) was studied from commercial laundry in BITS Pilani, KK Birla Goa Campus, India, which produces around 2–2.5 m3/day of GW. Source laundry GW showed physicochemical parameters like chemical oxygen demand (COD), biochemical oxygen demand (BOD5), ammoniacal N (NH4–N), nitrate (NO3–N), nitrite (NO2–N), orthophosphate (PO4–P) and pH in the range of 600–650 mg/L, 350–370 mg/L, 9–12 mg/L, 15–21 mg/L, 14–22 mg/L, 28–35 mg/L and 7.48–8.10, respectively. The treatment system included French‐type two‐stage vertical flow constructed wetland (VFCW) preceded by anaerobic filter tank filled with 3D plastic support. Wetland beds were made up of sand and gravel as filter media, and Canna indica was used as macrophyte. Pretreatment by anaerobic filter resulted in the increase in residence time of GW and reduction of pollutant load to wetland beds, thus preventing clogging of Stage 1 wetland. Sampling and water quality data analysis was performed weekly for 26 weeks. Outlet pollutant levels showed 96.35% removal of organic matter, 79.93% removal of nitrogen and 95.04% removal of orthophosphates. The pH of outlet water was 7.35, which is well below discharge permissible limits. Treated water quality was below Central Pollution Control Board (CPCB), India, permissible limits for discharge, and the cost of treating GW by the system is 1.11 USD/m3 which is very feasible in country like India. Treated water is suitable for nonpotable options like gardening of central lawns in BITS Pilani Goa, floor cleaning of laundry and construction purpose.

Analysis of OpenStreetMap Data Quality for Selected Counties in Poland in Terms of Sustainable Development

One potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators is OpenStreetMap (OSM). The purpose of this paper is to provide a comprehensive evaluation of the spatial data quality elements of OSM against the national official data—the database of topographic objects at a scale of 1:10,000. Such spatial data quality elements as location accuracy, data completeness and attribute compatibility were analysed. In the conducted OpenStreetMap tests, basic land-cover classes such as roads, railroads, river network, buildings, surface waters and forests were analysed. The test area of the study consisted of five counties in Poland, which differ in terms of location, relief, surface area and degree of urbanization. The best results of the quality of OSM spatial data were obtained for highly urbanized areas with developed infrastructure and a high degree of affluence. The highest degree of completeness of OSM linear and area objects in the studied counties was acquired in Piaseczyński County (82%). The lowest degree of completeness of the line and area objects of OSM in the studied counties was obtained in the Ostrowski County (51%). The calculated correlation coefficient between the quality of OSM data and the income per capita in the county was 0.96. The study complements the previous research results in the field of quantitative analysis of the quality of OSM data, and the obtained results confirm their dependence on the geometric type of the analysed objects and characteristics of test areas, i.e., in this case counties in Poland. The obtained results of OSM data quality analysis indicate that OSM data may provide strong support for other spatial data, including official and state data. OSM stores significant amounts of geospatial data with relatively high data quality that can be a valuable source for monitoring some SDG indicators.

Pengaruh Kualitas Pelayanan, Sanksi dan Tarif Pajak Terhadap Kepatuhan Wajib Pajak UMKM di Jepara

The research conducted is quantitative. The data used in this research are subjective data. The source of data in this research is primary data. This study uses a quantitative method in the form of survey assessment in KPP Pratama Jepara. This study aims to determine the effect of service quality, tax sanctions and tax rates on UMKM taxpayer compliance in Jepara. The number of population used is based on MSME taxpayers registered at KPP Pratama Jepara, which is as many as 11,548 taxpayers. While the sample used is UMKM taxpayers who report the Annual SPT which amounted to 3,974. From the results of calculations using the Slovin formula, the number of taxpayers used as a sample is 97.54 or 100 Individual Taxpayers of Micro, Small and Medium Enterprises (UMKM). In this study, the method used to take samples using purposive sampling technique. The data analysis method used is multiple linear regression analysis with data analysis test consisting of data quality analysis and classical assumption test. In testing the hypothesis using the coefficient of determination test and statistical test f and statistical test t. Service quality has a positive and significant effect on taxpayer compliance in paying taxes at the Jepara KPP Pratama. Tax sanctions have a positive and significant effect on taxpayer compliance in paying taxes at KPP Pratama Jepara. Tax rates have a significant negative effect on taxpayer compliance in paying taxes at KPP Pratama Jepara.

The Impact of PMU Data Precision and Accuracy on Event Classification in Distribution Systems

The use of synchrophasor data is recurrent in electrical power systems and has an increasing demand in distribution networks. The quality of this data can directly influence later stages related to decision-making. In this sense, this work presents the impact analysis of synchrophasor data quality on the event classification in distribution networks. We propose a controlled environment, simulating 37 200 events and including errors in the data with different levels, distributions, and biases, enabling evaluations according to the data’s precision and accuracy. Additionally, we present 2 160 classification results involving six machine learning methods, three different groups with 4, 8, and 62 events (classes) per group, and several characteristics of phasor errors. In addition to comparative analyzes, the research scope makes it possible to map the influence of multiple sources of errors, disclosing the data quality relevance and how it affects the event recognition in the distribution systems – a subject underexplored in the recent literature.

Data Quality of Automated Comorbidity Lists in Patients With Mental Health and Substance Use Disorders.

EHRs provide an opportunity to conduct research on underrepresented oncology populations with mental health and substance use disorders. However, a lack of data quality may introduce unintended bias into EHR data. The objective of this article is describe our analysis of data quality within automated comorbidity lists commonly found in EHRs. Investigators conducted a retrospective chart review of 395 oncology patients from a safety-net integrated healthcare system. Statistical analysis included κ coefficients and a condition logistic regression. Subjects were racially and ethnically diverse and predominantly used Medicaid insurance. Weak κ coefficients ( κ = 0.2-0.39, P < .01) were noted for drug and alcohol use disorders indicating deficiencies in comorbidity documentation within the automated comorbidity list. Further, conditional logistic regression analyses revealed deficiencies in comorbidity documentation in patients with drug use disorders (odds ratio, 11.03; 95% confidence interval, 2.71-44.9; P = .01) and psychoses (odds ratio, 0.04; confidence interval, 0.02-0.10; P < .01). Findings suggest deficiencies in automatic comorbidity lists as compared with a review of provider narrative notes when identifying comorbidities. As healthcare systems increasingly use EHR data in clinical studies and decision making, the quality of healthcare delivery and clinical research may be affected by discrepancies in the documentation of comorbidities.

Comparative Analysis of Disaster and Safety Datasets, Data Quality, and Services in Korean and Australian Public Data Portals

The objective of this study is to design improved plans for disaster and safety management through comparative analysis of datasets comprising details regarding the disaster and safety sector that are available in Korean and Australian public data portals and thereby examine the associated portal strategies and national operating systems To this end, based on previous studies and national public data portal evaluation standards, public data portal comparison standards were established. Furthermore, quantitative configuration analysis, data mining analysis, data quality analysis, and various service analyses were conducted. Consequently, it was found that compared to the Australian public data portal, the number and proportion of public data openings for disaster and safety management are significantly smaller in the Korean public data portal. The number of prevention datasets available in both countries accounts for the highest proportion in disaster and safety management. In addition, “supply management and resource support” accounts for the majority of Korean disaster response collaborations, whereas in the case of Australia, “comprehensive situation management” has a high proportion. In terms of data quality, Australian datasets include 69.54% of the total insoluble data, which implies inefficient data quality management. However, there are merely few Korean insoluble datasets. Through comparative analysis of the national operating systems, it was found that detailed guidelines for public data are available in Australia. However, the Korean public data portal is operated in accordance with the comprehensive Framework Act on Intelligence Informatization. Hence, a promotion strategy operation policy has to devise for the Korean public data portal to expand the usage of public data in the disaster and safety sector, along with quantitative growth policies that will increase the number and proportion of public data in the disaster and safety sector.

Comparative Analysis of Disaster and Safety Datasets, Data Quality, and Services in Korean and Australian Public Data Portals

The objective of this study is to design improved plans for disaster and safety management through comparative analysis of datasets comprising details regarding the disaster and safety sector that are available in Korean and Australian public data portals and thereby examine the associated portal strategies and national operating systems To this end, based on previous studies and national public data portal evaluation standards, public data portal comparison standards were established. Furthermore, quantitative configuration analysis, data mining analysis, data quality analysis, and various service analyses were conducted. Consequently, it was found that compared to the Australian public data portal, the number and proportion of public data openings for disaster and safety management are significantly smaller in the Korean public data portal. The number of prevention datasets available in both countries accounts for the highest proportion in disaster and safety management. In addition, “supply management and resource support” accounts for the majority of Korean disaster response collaborations, whereas in the case of Australia, “comprehensive situation management” has a high proportion. In terms of data quality, Australian datasets include 69.54% of the total insoluble data, which implies inefficient data quality management. However, there are merely few Korean insoluble datasets. Through comparative analysis of the national operating systems, it was found that detailed guidelines for public data are available in Australia. However, the Korean public data portal is operated in accordance with the comprehensive Framework Act on Intelligence Informatization. Hence, a promotion strategy operation policy has to devise for the Korean public data portal to expand the usage of public data in the disaster and safety sector, along with quantitative growth policies that will increase the number and proportion of public data in the disaster and safety sector.

Pengaruh Penerapan Akad Qard Dan Ijarah Terhadap Minat Nasabah Pada Produk Gadai di Bank Syariah Indonesia (BSI) KC Binjai

The purpose of this study was to find out how the effect of qard and ijarah contracts on customer interest in using pawn products at the Indonesian Islamic Bank (BSI) Kc Binjai. This study used quantitative research in its analysis. The population used in this study is all people who use pawn services at Bank Syariah Indonesia (BSI) Kc Binjai. The data analysis used is data quality analysis, multiple linear regression test, and hypothesis testing. From the results of the study, it is known that the qard and ijarah contracts have a positive and significant effect on customer interest in using pawn products at Bank Syariah Indonesia (BSI) Kc Binjai

Initial Coin Offering (ICO) Success and Post-ICO Performance

We compile a comprehensive data set of initial coin offerings (ICOs) from 19 data sources including 11 ICO aggregators. We alleviate severe limitations of available ICO data by performing the first systematic analysis of ICO data quality. We use our data set to study determinants of ICO funding success as well as post-ICO operating performance and provide evidence on some novel determinants of initial and longer-term ICO success. Finally, we show that post-ICO operating performance is positively associated with contemporaneous token returns and is generally negatively associated with token return volatility, suggesting that post-ICO operating performance has financial ramifications. This paper was accepted by Kay Giesecke, finance. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4312 .

Data accuracy and method validation of chemical soil properties in long-term experiments: Standard operating procedures for a non-certified soil laboratory in Latin America

Long-term agricultural experiments (LTE) are essential to detect cumulative treatment effects on soil properties and to design sustainable production systems. However, to ensure high quality of long-term soil data and their correct interpretation, several analytical issues regarding the accuracy and analytical laboratory bias need to be considered. This paper aims to (1) evaluate laboratory precision and trueness of analytical soil data for the evaluation of long-term trends in LTE and to (2) assess the limitations and challenges for non-certified soil laboratories that might compromise the quality of analytical soil data. A data set of internal reference soil materials (IRM) collected over 16 years and interlaboratory data from eleven years were analyzed to verify method precision, trueness, and the subsequent long-term dataset reliability for several soil quality parameters: organic carbon (SOC, determined either by wet or dry combustion), pH (water), extractable phosphorous (either Bray I or citric acid; Bray-P or citric acid-P), and exchangeable potassium (Kexch). Results showed that IRM used by the laboratory were homogenous in terms of physical and chemical composition and appropriate to confirm the precision of long-term soil survey data. The relative standard deviation for repeatability and reproducibility (RSDR) ranged from 1.5% for SOC by wet combustion to 9.5% for citric acid-P. HorRat values (the ratio of the estimated standard deviations of reproducibility and the repeatability found for individual analytical procedures) for all chemical soil properties were within the acceptable ranges of <2.0. Interlaboratory trials for soil pH and SOC showed tolerable standard Z-Scores under 2.0 (Z-Score, calculated from the difference between laboratory results and the assigned value divided by the standard deviation), verifying the trueness of data. The results of this study reinforce the validity of analytical soil data originating from the non-certified laboratory in Uruguay obtaining both precise and true soil quality data over a long period of time for most soil analytes. Nevertheless, the analytical flaws in LTE soil monitoring were revealed and can only be minimized in ongoing and future studies through the inclusion of certified reference material. These recommendations should guide future research activities in LTE studies on analytical data quality management as a requirement for long-term soil monitoring. Finally, the paper proposes a proficiency testing procedure for soil laboratories to achieve and maintain high analytical quality for LTE soil research.

Data quality, experimental artifacts, and the reactivity of the psychological subject matter

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what are experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise (and answer) the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results.