Aspects Of Data Quality Research Articles

Mapping secondary data gaps for social simulation modelling: A case study of the journeys of Syrian asylum seekers to Europe

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers’ journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011–21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.

Reporting Eye‐Tracking Research in Second Language Acquisition and Bilingualism: A Synthesis and Field‐Specific Guidelines

AbstractEye tracking has taken hold in second language acquisition (SLA) and bilingualism as a valuable technique for researching cognitive processes, yet a comprehensive picture of reporting practices is still lacking. Our systematic review addressed this gap. We synthesized 145 empirical eye‐tracking studies, coding for 58 reporting features and applying a gap analysis to the codings. Although certain aspects of reporting, such as descriptions of auxiliary assumptions, equipment, and setup, were consistently implemented, we found significant variation and sometimes complete omission of crucial details surrounding aspects of data quality and of data preprocessing and cleaning. This lack of information hinders the evaluation of methodological rigor and overall study quality of eye‐tracking research. We propose a set of field‐specific reporting guidelines in the form of a checklist to improve the quality of data collected and contribute to the larger goal of advancing the replication and reproducibility of eye‐tracking research in SLA and bilingualism.

Measuring Expenditure with a Mobile App: Do Probability-Based and Nonprobability Panels Differ?

Abstract In this case study, we examine a novel aspect of data collected in a typical probability and a typical nonprobability panel: mobile app data. The data were collected in Great Britain in 2018, using the Innovation Panel of the UK Household Longitudinal Study and the Lightspeed online access panel. Respondents in each panel were invited to participate in a month-long study, reporting all their daily expenditures in the app. In line with most of the research on nonprobability and probability-based panel data, our results indicate differences in the data gathered from these data sources. For example, more female, middle-aged, and highly educated people with higher digital skills and a greater interest in their finances participated in the nonprobability app study. Our findings also show that resulting differences in the app spending data are difficult to eliminate by weighting. The only data quality aspect for which we do not find evidence of differences between the nonprobability and probability-based panel is behavior in using the spending app. This finding is contrary to the argument that nonprobability online panel participants try to maximize their monetary incentive at the expense of data quality. However, this finding is in line with some of the scarce existing literature on response behavior in surveys, which is inconclusive regarding the question of whether nonprobability online panel participants answer questions less conscientiously than probability-based panel respondents. Since the two panels in our case study differ in more aspects than the sample selection procedure, more research in different contexts is necessary to establish generalizability and causality.

Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT.

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

Data Quality of Different Modes of Supervision in Classroom Surveys

Conducting quantitative research involving adolescents demands a thoughtful approach to the question of supervision, given that each option comes with its distinct set of implications. This study reviews these implications and empirically tests whether differences in data quality can be found among three modes of standardized survey research with medium-sized groups of adolescents (12–17 years). The data basis is a quasi-experimental survey study testing different forms of digital, hybrid, or in-person supervision that took place in 2021 in secondary schools in Germany (N = 923). The aim of this study is to test how aspects of data quality—item nonresponse, interview duration, drop-out rate, and response patterns—differ between these forms of supervision. Results could help researchers surveying young people to decide (1) whether they allow confidants or other adults to be present during interviews, (2) if they can rely on teachers alone when surveying classrooms, and (3) if it is cost-efficient to send out external supervisors for classroom sessions. While drop-out rates do not differ, item non-response, interview duration, and response patterns differ significantly; students supervised at home by external interviewers answered more questions, took more time to answer, and were less likely to give potentially meaningless answers in grid questions. The implications drawn from the findings question the common approach of solely relying on teachers for survey administration without the support of external supervisors or adequate training. Recruiting respondents via schools and surveying them online in their homes during school hours has been shown to be a robust method with regard to the analyzed indicators.

Validation of global precipitation time series products against tree ring records and remotely sensed vegetation greenness.

Global interpolated climate products are widely used in ecological research to investigate biosphere-climate interactions and to track ecological response to climate variability and climate change. In turn, biological data could also be used for an independent validation of one aspect of climate data quality. All else being equal, more variance explained in biological data identifies the better climate data product. Here, we compare seven global precipitation time series products, including gauge-based datasets (CRU-TS, UDEL-TS, GPCC), re-analysis products (ERA5, CHELSA), a satellite-based dataset (PERSIANN) and a multi-source product that draws on gauge, re-analysis, and satellite data (MSWEP). We focus on precipitation variables, because they are more difficult to interpolate than temperature, and show larger divergence among gridded data products. Our validation is based on 20 years of remotely sensed vegetation greenness (MODIS-EVI) and 120 years of tree ring records from the International Tree Ring Data Bank (ITRDB). The results for the 20-year EVI based validation shows that all gauge and re-analysis data products performed similarly, but were outperformed by the multi-source MSWEP product, especially in regions with low weather station coverage, such as Africa. For analyzing long 120-year time-series, UDEL-TS showed superior performance prior to the 1940s, with especially large margins for northern Asia and the Himalayas region. For other regions, CRU-TS and GPCC could be recommended. We provide maps that can guide the best regional choice of climate product for research involving time series of biological response to historic climate variability and climate change.

QI$$^2$$: an interactive tool for data quality assurance

AbstractThe importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.

Mapping secondary data gaps for social simulation modelling: A case study of the journeys of Syrian asylum seekers to Europe.

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers' journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011-21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.

An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis

Big data has emerged as a fundamental component in various domains, enabling organizations to extract valuable insights and make informed decisions. However, ensuring data quality is crucial for effectively using big data. Thus, big data quality has been gaining more attention in recent years by researchers and practitioners due to its significant impact on decision-making processes. However, existing studies addressing data quality anomalies often have a limited scope, concentrating on specific aspects such as outliers or inconsistencies. Moreover, many approaches are context-specific, lacking a generic solution applicable across different domains. To the best of our knowledge, no existing framework currently automatically addresses quality anomalies comprehensively and generically, considering all aspects of data quality. To fill the gaps in the field, we propose a sophisticated framework that automatically corrects big data quality anomalies using an intelligent predictive model. The proposed framework comprehensively addresses the main aspects of data quality by considering six key quality dimensions: Accuracy, Completeness, Conformity, Uniqueness, Consistency, and Readability. Moreover, the framework is not correlated to a specific field and is designed to be applicable across various areas, offering a generic approach to address data quality anomalies. The proposed framework was implemented on two datasets and has achieved an accuracy of 98.22%. Moreover, the results have shown that the framework has allowed the data quality to be boosted to a great score, reaching 99%, with an improvement rate of up to 14.76% of the quality score.

On some aspects of medical data quality

This paper examines the specific problem of the quality of medical data of patients when they are hospitalized for treatment through the lens of the general problem of data quality. The need to apply international standards in data quality is discussed, taking into account the specifics of the medical field and the national standards and regulations. This necessity is considered with respect to the problem of medical institutions switching to IT systems with electronic medical records and electronic health records. The specifics of the procedures for filling in the files of hospitalized patients are highlighted.

VISproPT: A high-precision instrument for 3D shape analysis of parabolic trough panels

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum migration to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011–21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.

GRU-D-Weibull: A novel real-time individualized endpoint prediction

BackgroundIn the era of healthcare digital transformation, using electronic health record (EHR) data to generate various endpoint estimates for active monitoring is highly desirable in chronic disease management. However, traditional predictive modeling strategies leveraging well-curated data sets can have limited real-world implementation potential due to various data quality issues in EHR data. MethodsWe propose a novel predictive modeling approach, GRU-D-Weibull, which models Weibull distribution leveraging gated recurrent units with decay (GRU-D), for real-time individualized endpoint prediction and population level risk management using EHR data. ExperimentsWe systematically evaluated the performance and showcased the real-world implementability of the proposed approach through individual level endpoint prediction using a cohort of patients with chronic kidney disease stage 4 (CKD4). A total of 536 features including ICD/CPT codes, medications, lab tests, vital measurements, and demographics were retrieved for 6879 CKD4 patients. The performance metrics including C-index, L1-loss, Parkes' error, and predicted survival probability at time of event were compared between GRU-D-Weibull and other alternative approaches including accelerated failure time model (AFT), XGBoost based AFT (XGB(AFT)), random survival forest (RSF), and Nnet-survival. Both in-process and post-process calibrations were experimented on GRU-D-Weibull generated survival probabilities. ResultsGRU-D-Weibull demonstrated C-index of ~0.7 at index date, which increased to ~0.77 at 4.3 years of follow-up, comparable to that of RSF. GRU-D-Weibull achieved absolute L1-loss of ~1.1 years (sd≈0.95) at CKD4 index date, and a minimum of ~0.45 year (sd≈0.3) at 4 years of follow-up, comparing to second-ranked RSF of ~1.4 years (sd≈1.1) at index date and ~0.64 years (sd≈0.26) at 4 years. Both significantly outperform competing approaches. GRU-D-Weibull constrained predicted survival probability at time of event to smaller and more fixed range than competing models throughout follow-up. Significant correlations were observed between prediction error and missing proportions of all major categories of input features at index date (Corr ~0.1 to ~0.3), which faded away within 1 year after index date as more data became available. Through post training recalibration, we achieved a close alignment between the predicted and observed survival probabilities across multiple prediction horizons at different time points during follow-up. ConclusionGRU-D-Weibull shows advantages over competing methods in handling missingness commonly encountered in EHR data and providing both probability and point estimates for diverse prediction horizons during follow-up. The experiment highlights the potential of GRU-D-Weibull as a suitable candidate for individualized endpoint risk management, utilizing real-time clinical data to generate various endpoint estimates for monitoring. Additional research is warranted to evaluate the influence of different data quality aspects on prediction performance. Furthermore, collaboration with clinicians is essential to explore the integration of this approach into clinical workflows and evaluate its effects on decision-making processes and patient outcomes.

COMPARISON OF LOW-COST HANDHELD LIDAR-BASED SLAM SYSTEMS FOR MAPPING UNDERGROUND TUNNELS

Abstract. The use of mobile mapping technologies (MMT) has become increasingly popular across various applications such as forestry, cultural heritage, mining, and civil engineering. While Simultaneous Localization and Mapping (SLAM) algorithms have greatly improved in recent years with regards to accuracy, robustness, and cooperativity, it is important to understand the limitations and strengths of each metrological measurement method to ensure the provision of 3D data of appropriate quality for the selected application. In this study, we perform a comparative analysis of three LiDAR-based handheld mobile mapping systems with survey-grade reference point clouds in a challenging test area of a partially collapsed underground tunnel. We investigate various aspects of 3D data quality, including accuracy and completeness, and present an improved method for 3D data completeness assessment aimed at evaluating SLAM-derived point clouds. The results demonstrate unique and diverse strengths and shortcomings of the tested mapping systems, which provide valuable guidelines for selecting an appropriate system for subterranean applications.

Extending a Data Management Maturity Model for Process Mining in Healthcare.

Many components must work together to continuously improve processes in healthcare organizations. Process mining has recently developed into a discipline that can make a significant contribution here. We want to extend an existing management tool to assess and improve the capability of organizations in this area. We add a dimension to the adoption readiness assessment and maturity model for sharable clinical pathways to assess and improve event data quality. We present different approaches for formal and checkpoint assessments and an embedding of the improvement strategy with examples. The additional dimension from the process mining domain integrates with the existing model. At all levels, links can be established between the various aspects of event data quality with existing dimensions. The model has yet to be tested in a real-world use case.

Impact of Data Quality on Question Answering System Performances

In contrast with the research of new models, little attention has been paid to the impact of low or high-quality data feeding a dialogue system. The present paper makes the first attempt to fill this gap by extending our previous work on question-answering (QA) systems by investigating the effect of misspelling on QA agents and how context changes can enhance the responses. Instead of using large language models trained on huge datasets, we propose a method that enhances the model's score by modifying only the quality and structure of the data feed to the model. It is important to identify the features that modify the agent performance because a high rate of wrong answers can make the students lose their interest in using the QA agent as an additional tool for distant learning. The results demonstrate the accuracy of the proposed context simplification exceeds 85%. These findings shed light on the importance of question data quality and context complexity construct as key dimensions of the QA system. In conclusion, the experimental results on questions and contexts showed that controlling and improving the various aspects of data quality around the QA system can significantly enhance his robustness and performance.

Open data as an anticorruption tool? Using distributed cognition to understand breakdowns in the creation of transparency data

Abstract One of the drivers for pushing for open data as a form of corruption control stems from the belief that in making government operations more transparent, it would be possible to hold public officials accountable for how public resources are spent. These large datasets would then be open to the public for scrutiny and analysis, resulting in lower levels of corruption. Though data quality has been largely studied and many advancements have been made, it has not been extensively applied to open data, with some aspects of data quality receiving more attention than others. One key aspect however—accuracy—seems to have been overlooked. This gap resulted in our inquiry: how is accurate open data produced and how might breakdowns in this process introduce opportunities for corruption? We study a government agency situated within the Brazilian Federal Government in order to understand in what ways is accuracy compromised. Adopting a distributed cognition (DCog) theoretical framework, we found that the production of open data is not a neutral activity, instead it is a distributed process performed by individuals and artifacts. This distributed cognitive process creates opportunities for data to be concealed and misrepresented. Two models mapping data production were generated, the combination of which provided an insight into how cognitive processes are distributed, how data flow, are transformed, stored, and processed, and what instances provide opportunities for data inaccuracies and misrepresentations to occur. The results obtained have the potential to aid policymakers in improving data accuracy.

An Advanced Big Data Quality Framework Based on Weighted Metrics

While big data benefits are numerous, the use of big data requires, however, addressing new challenges related to data processing, data security, and especially degradation of data quality. Despite the increased importance of data quality for big data, data quality measurement is actually limited to few metrics. Indeed, while more than 50 data quality dimensions have been defined in the literature, the number of measured dimensions is limited to 11 dimensions. Therefore, this paper aims to extend the measured dimensions by defining four new data quality metrics: Integrity, Accessibility, Ease of manipulation, and Security. Thus, we propose a comprehensive Big Data Quality Assessment Framework based on 12 metrics: Completeness, Timeliness, Volatility, Uniqueness, Conformity, Consistency, Ease of manipulation, Relevancy, Readability, Security, Accessibility, and Integrity. In addition, to ensure accurate data quality assessment, we apply data weights at three data unit levels: data fields, quality metrics, and quality aspects. Furthermore, we define and measure five quality aspects to provide a macro-view of data quality. Finally, an experiment is performed to implement the defined measures. The results show that the suggested methodology allows a more exhaustive and accurate big data quality assessment, with a more extensive methodology defining a weighted quality score based on 12 metrics and achieving a best quality model score of 9/10.

The Visible Cash Effect with Prepaid Incentives: Evidence for Data Quality, Response Rates, Generalizability, and Cost

Abstract A prior study found that mailing prepaid incentives with $5 cash visible from outside the envelope increased the response rate to a mail survey by 4 percentage points compared to cash that was not externally visible. This “visible cash effect” suggests opportunities to improve survey response at little or no cost, but many unknowns remain. Among them: Does the visible cash effect generalize to different survey modes, respondent burdens, and cash amounts? Does it differ between fresh samples and reinterview samples? Does it affect data quality or survey costs? This article examines these questions using two linked studies where incentive visibility was randomized in a large probability sample for the American National Election Studies. The first study used $10 incentives with invitations to a long web questionnaire (median 71 minutes, n = 17,849). Visible cash increased response rates in a fresh sample for both screener and extended interview response (by 6.7 and 4.8 percentage points, respectively). Visible cash did not increase the response rate in a reinterview sample where the baseline reinterview response rate was very high (72 percent). The second study used $5 incentives with invitations to a mail-back paper questionnaire (n = 8,000). Visible cash increased the response rate in a sample of prior nonrespondents by 4.0 percentage points (from 31.5 to 35.5), but it did not increase the response rate in a reinterview sample where the baseline reinterview rate was very high (84 percent). In the two studies, several aspects of data quality were investigated, including speeding, non-differentiation, item nonresponse, nonserious responses, noncredible responses, sample composition, and predictive validity; no adverse effects of visible cash were detected, and sample composition improved marginally. Effects on survey costs were either negligible or resulted in net savings. Accumulated evidence now shows that visible cash can increase incentives’ effectiveness in several circumstances.

Data Completeness and Complex Semantics in Conceptual Modeling: The Need for a Disaggregation Construct

Conceptual modeling is important for developing databases that maintain the integrity and quality of stored information. However, classical conceptual models have often been assumed to work on well-maintained and high-quality data. With the advancement and expansion of data science, it is no longer the case. The need to model and store data has emerged for settings with lower data quality, which creates the need to update and augment conceptual models to represent lower-quality data. In this paper, we focus on the intersection between data completeness (an important aspect of data quality) and complex class semantics (where a complex class entity represents information that spans more than one simple class entity). We propose a new disaggregation construct to allow the modeling of incomplete information. We demonstrate the use of our disaggregation construct for diverse modeling problems and discuss the anomalies that could occur without this construct. We provide formal definitions and thorough comparisons between various types of complex constructs to guide future application and prove the unique interpretation of our newly proposed disaggregation construct.

Use of Data Enhancement for Emerging Study Methodologies That Compare Clinical Trial and Real-World Data

External comparators (ECs) are a useful emerging research methodology to complement clinical trials by providing real-world (RW) context on comparator treatments and are increasingly of interest to regulators and payers in rare diseases and their sub-populations. Indeed, in recent publications from FDA and NICE, there is an insistence on assessing the fitness-for-purpose of RW data, including both data quality (e.g., completeness, accuracy of key study variables) as well as relevance (e.g., availability of trial-like outcomes), to ensure robust and reliable results can be generated in RW evidence studies including ECs. In light of these requirements, we aimed to understand the suitability of existing haematology-oncology RW data sources (DSs) for comparison with clinical trial data, with the intention of bringing together qualified partners to establish a research collaboration network specialized in this emerging research methodology. Literature reviews, desk research, and recent/ongoing in-house clinical studies were reviewed to identify European DSs involved in RW haematology-oncology data generation. Of 332 European DSs identified, primary market research was conducted with 46 to ascertain fitness-for-purpose, including patient counts, variable availability, data quality, and operational aspects of data access. As detailed in Table 1 below, among the 46 sources, approximately 9,560 patients per year were recorded across 6 haematology-oncology malignancies - DLBCL, FL, CLL, MM, MCL, and MZL - spanning 11 European countries. We found that data on patient and disease characteristics, labs, clinical outcomes, and treatment sequence were generally readily available (collected in approximately 100%, 87%, 91%, and 91% of sources, respectively). Data quality and completeness was reasonable, but mixed for response measures and prognostic factors, particularly at later lines of therapy. On the other hand, genetic markers, QoL and HCRU measures, were often not readily available (collected in only approximately 46%, 20% and 11% of sources, respectively). Regarding operational aspects, hospitals were more willing than registries and claims databases to share patient-level data with external researchers. Most DSs reported data collection could be conducted via electronic extraction (e.g., electronic medical records, 65%) or a mix of electronic and manual methods (e.g., case report forms, 48%), which would enable the enhancement of structured EMR data with unstructured data (e.g., captured in clinical notes or pathology reports. Though data availability across the assessed European haematology-oncology DSs is sufficient to support ECs, artificial intelligence was identified as a useful tool to help enhance RW data readily captured in within EMRs - such as genetic markers, response measures and prognostic factors - and improve the comparability of clinical trial and RW patients. Specifically, emerging machine learning (ML) methods can improve propensity score matching, increase statistical power and reduce bias in treatment effect estimates or can offer alternatives to propensity score matching, such as G-computation and double debiased ML. Moreover, natural language processing (NLP) techniques can be used to extract unstructured data from clinical notes or pathology reports, instead of relying on more burdensome manual chart review methods. Although there is significant promise in emerging technologies for improving the data quality and operational efficiency of future haematology-oncology EC studies, the use of techniques such as NLP and ML to generate RW evidence may have limitations when used on small sample sizes in the context of rare diseases and, especially for the consumption of regulatory and payer audiences, will require data validation and compliance with information governance models to drive the fidelity and broad applicability of these techniques. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal