Quality Of Resulting Data Research Articles

A districting problem with data reliability constraints for equity analysis

While data plays an important role in transportation research, sampled data is not always reliable. Data reliability issue is significant especially for minority groups. In this study, a districting approach is proposed which improves data reliability through aggregation of basic spatial units (BSU), adapted from a max-p-regions problem. The model generates as many aggregated zones as possible that minimize intrazonal heterogeneity while minimizing data margin of error (MOE) of all aggregated zones using a controlling MOE threshold. The problem is first formulated as an integer programming which selects optimal set of zones from a pre-generated set of candidate zones. The difficulty of solving the formulation lies in the generation of the candidate set, so a heuristic solution algorithm is proposed. Two case studies are provided to illustrate the method and validate its performance by evaluating the resulting data quality in an example subsequent planning model. First is an area in Downtown Manhattan with 62 census tracts, comparing the aggregated zones with Neighborhood Tabulation Areas (NTAs) and Taxi Zones. Second is the generation of the New York City Equitable Zoning (NYCEZ), which generated 574 Equitable Zones that reduce the average MOE% of demographic data by 48% for seniors, 75% for low-income population, and 46% for long commuters, all with a district number that is higher than NTAs (221) and Taxi Zones (263). NYCEZ and census tracts are then compared in a subsequent model, synthetic population generation, showing an improvement of 6.2% in standard deviation across simulated populations under the proposed zone design. NYCEZ showed smaller variation in the generated population data. The algorithm can help the decision making of public agencies and the service design of mobility providers by producing reliable and equitable data. The algorithm can also be applied to data-sharing between mobility providers and agencies to alleviate privacy concerns.

Enhanced quality of documentation for biologic therapy of chronic rhinosinusitis through structured digital reporting and indication?

With targeted inhibition of type2 inflammation, biologics represent the standard add-on therapy for inadequately controlled severe forms of chronic rhinosinusitis with nasal polyps (CRSwNP). Despite standardization with paper-based checklists, the documentation of medical history and current findings pertinent to indication criteria are asignificant challenge for physicians. Through development of an application based on structured reporting, the current study aimed to improve documentation quality and simplify the decision-making process. Previously available paper checklists served as a comparison. For this study, adigital incremental tool was programmed to record current findings and check for fulfilment of indication criteria. The tool was compared with other checklists in terms of completeness, time required, and readability. Atotal of 20findings were collected for each of the three documentation options and included in the analysis. Documentation with the two paper-based checklists had comparable information content: 17.5 ± 5.1/21.7 ± 7.6points out of amaximum of 43points; p > 0.05. Documentation using the digital application led to asignificant increase in information content compared to all paper-based documentation. The average score was 38.25 ± 3.7 (88.9% of maximum; p < 0.001). On average, user satisfaction was high (9.6/10). Use of the digital application was initially more time consuming, but as more cases were documented, the time taken improved significantly. In the future, structured reporting using apps could replace paper-based reporting for the indication of biologic therapy in CRSwNP patients and offer additional benefits in terms of data quality and traceability of results. The increasing volume of documentation in the future, the progress of digitalization, and the possibility of networking between individual centers make introduction of the app in the near future both likely and economical.

Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study.

In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Blended acquisition with temporally signatured/modulated and spatially dispersed source array: The first pilot survey onshore Abu Dhabi

AbstractRecently, we established a blended‐acquisition method: temporally signatured and/or modulated and spatially dispersed source array that jointly uses various signaturing and/or modulation in the time dimension and dispersed source array in the space dimension. We acquired and processed the first pilot programme with our method onshore Abu Dhabi. In this paper, we review the resulting acquisition‐productivity enhancement in the time dimension and discuss it in the space dimension as well. We then review the deblended‐data‐reconstruction processing, followed by imaging processing, and discuss their performance and resulting data quality. We last establish a relationship between the acquisition productivity and the processing performance. We found that this method significantly enhances the acquisition productivity compared to conventional blending methods. For the processing performance, the deblended data can successfully be reconstructed from the blended data; afterwards, the subsurface sections can naturally be imaged from the deblended data. Furthermore, this method owns a relationship: The deblended‐data‐reconstruction performance increases with the acquisition time (i.e. the acquisition effort that is inversely proportional to the acquisition productivity) up to a plateau; the imaging processing improves the data quality as the flow makes progress and eventually reaches a high data quality after poststack processing, regardless of the acquisition effort.

Protocol for a web survey experiment studying the feasibility of asking respondents to capture and submit photos of the books they have at home and the resulting data quality.

This document presents the protocol of a study conducted as a part of the WEB DATA OPP project, which is funded by the H2020 program. The study aimed to investigate different aspects of the collection of images through web surveys. To do this, we implemented a mobile web survey in an opt-in online panel in Spain. The survey had various questions, some of which were about the books that the participants have at their main residence. The questions related to books were asked in three different ways: regular survey questions showing visual examples of how different numbers of books fit in a 74 centimetre wide shelf depending on their thickness, regular survey questions without the visual examples, and questions where participants were asked to send photos of the books at their home. This report explains how the study was designed and conducted. It covers important aspects such as the experimental design, the questionnaire used, the characteristics of the participants, ethical considerations, and plans for disseminating the results.

IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion.

Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications' varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of applications increases, and it also has financial implications, as it would require a separate data pipeline for each application. To address this challenge, this paper proposes a novel approach integrating fusion methods into end-to-end data quality assessment to cater to different applications within a single data pipeline. By using real-time and historical analytics, the study investigates the effects of each fusion method on the resulting data quality score and how this can be used to support different applications. The study results, based on two real-world datasets, indicate that Kalman fusion had a higher overall mean quality score than Adaptive weighted fusion and Naïve fusion. However, Kalman fusion also had a higher computational burden on the system. The proposed solution offers a flexible and efficient approach to addressing IoT applications' diverse data quality needs within a single data pipeline.

Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging.

A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology. We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports. The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89). We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

Development of Service-Oriented Architecture-Based Microservices Management as a Data Integration Service (Case Study: Udayana University)

Udayana University has developed numerous information systems to accommodate all types of academic and non-academic activities. Because the process of retrieving data from other systems still employs the traditional method with a joint scheme between databases, the resulting data quality becomes increasingly inaccurate over time. Some systems have also established simple web services that do not adhere to the Restful API authoring rules. Based on these issues, modifications were made to the data integration process using Microservices Management, Service Oriented Architecture, and OAuth 2.0 as the security protocol. The results indicate that the microservices management system is highly effective and efficient for improving process performance, has a low risk level, a good control test validation of 93,33 %, and a usability grade of B, indicating that the system is deemed good and usable.

A point selection method in map generalization using graph convolutional network model

ABSTRACT For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resulting data quality. The rules summarized by traditional generalization methods usually require manual setting of conditions or thresholds and sometimes encounter special cases that make it difficult to directly match certain rules or integrate different rules together. An alternative method is using a data-driven strategy under AI technology background to simulate cartographer behaviors through typical sample training, such as deep learning. The integration of cartography domain knowledge and deep learning is a better choice to settle generalization decisions. This study uses a combination of domain knowledge and a data-driven approach to introduce graph neural networks into point cluster generalization. First, we construct a virtual graph structure of point clusters using Delaunay triangulation, secondly, we extract spatial features, contextual features, and attributes of each point separately, and then propose a generalization model based on the TAGCN network. Finally, this model is trained with the manually generalized sample to realize the automatic point cluster generalization. The results demonstrate that the proposed model is valid and efficient for point cluster generalization and that this algorithm can better maintain various characteristics of the point cluster in both the local area and the overall map compared to other methods.

Impact of linear infrastructure on South Africa’s primate fauna: the need for mitigation

Abstract South Africa’s extensive linear infrastructure network (which includes roads and power lines) is severely impacting the country’s historically recognised five primate species: greater or thick-tailed bushbaby (Otolemur crassicaudatus), southern lesser bushbaby (Galago moholi), chacma baboon (Papio ursinus), vervet monkey (Chlorocebus pygerythrus) and samango monkey (Cercopithecus albogularis). We present South African mortality data from two different linear infrastructure types on a country wide scale, over a long-term sampling period. Using primate road mortality and power line electrocution data acquired from different data sources, we compare and discuss different mortality data collection methodologies, the resulting data quality and identify current limitations in understanding the direct impacts of linear infrastructure which have important implications for primate conservation planning. Between 1996-2021 a total of 483 primate mortalities were recorded on roads and power lines, the majority on the former. Vervet monkeys were most severely impacted by both linear infrastructure types whereas lesser bushbabies experienced the least number of mortalities. Both data sets showed numerous incidents where more than one individual was killed (roadkill: 4%, up to four killed in one incident; electrocutions: 13%, up to six killed in one incident). GPS coordinates were available for 61% of roadkill records and for 65% of electrocution records. Age or sex of carcasses were not available for electrocution records and only available for 11% of roadkill records. Although South Africa leads the African continent regarding roadkill and electrocution data collection, there are still areas in the collection protocol that can be improved and projects implementing mitigation measures (e.g. canopy bridges) to reduce primate roadkill are lacking. We argue that the mortality data presented here should form the basis for future mitigation implementation and recommend that linear infrastructure be more prominently recognised as a direct threat when developing national and international Red Lists.

Improving data quality in large-scale repositories through conflict resolution

Digital repositories rely on technical metadata to manage their objects. The output of characterization tools is aggregated and analyzed through content profiling. The accuracy and correctness of characterization tools vary; they frequently produce contradicting outputs, resulting in metadata conflicts. The resulting metadata conflicts limit scalable preservation risk assessment and repository management. This article presents and evaluates a rule-based approach to improving data quality in this scenario through expert-conducted conflict resolution. We characterize the data quality challenges and present a method for developing conflict resolution rules to improve data quality. We evaluate the method and the resulting data quality improvements in an experiment on a publicly available document collection. The results demonstrate that our approach enables the effective resolution of conflicts by producing rules that reduce the number of conflicts in the data set from 17 to 3%. This replicable method for presents a significant improvement in content profiling technology for digital repositories, since the enhanced data quality can improve risk assessment and preservation management in digital repository systems.

Impact of discretization discrepancy in mapping quality depending on mesh displacement and rotation

In an effort to reduce prototypes in the industrial manufacturing of sheet metal components and their assembly, numerical simulation is well established. Where each single process transfers into its virtual counterpart to ensure quality assurance, disregarding the influence of their preceding processes. In order to further the accuracy of a virtual process, links between sequenced methods are established to create a process chain considering all influences on a manufactured part in its early design stages. If a finite element solver change occurs within this linkage, it is necessary to transfer and/or transform source mesh bound data to fit the target mesh, if said data may influence any simulation further down the chain. This data transfer, called “mapping”, depends on the utilized allocation and interpolation algorithm. This contribution aims to link the difference in mesh discretization and position to the resulting data quality depending on both meshes as well as various approximation and interpolation procedures. The considered geometry represents a S-shaped profile common in sheet metal forming and subsequent joining whereas the mapped data consist of scalar and tensorial values such as thickness and stresses. It is expected that a large difference in the meshes coupled with certain interpolation methods lead to significant errors during mapping between source- and target-data.

Pollinator monitoring more than pays for itself

Abstract Resilient pollination services depend on sufficient abundance of pollinating insects over time. Currently, however, most knowledge about the status and trends of pollinators is based on changes in pollinator species richness and distribution only. Systematic, long‐term monitoring of pollinators is urgently needed to provide baseline information on their status, to identify the drivers of declines and to inform suitable response measures. Power analysis was used to determine the number of sites required to detect a 30% change in pollinator populations over 10 years. We then evaluated the full economic costs of implementing four national monitoring schemes in the UK: (a) professional pollinator monitoring, (b) professional pollination service monitoring, (c) volunteer collected pan traps and (d) volunteer focal floral observations. These costs were compared to (a) the costs of implementing separate, expert‐designed research and monitoring networks and (b) the economic benefits of pollination services threatened by pollinator loss. Estimated scheme costs ranged from £6,159/year for a 75‐site volunteer focal flower observation scheme to £2.7 M/year for an 800‐site professional pollination service monitoring network. The estimated research costs saved using the site network as research infrastructure range from £1.46–4.17 M/year. The economic value of UK crop yield lost following a 30% decline in pollinators was estimated at ~£188 M/year. Synthesis and applications. We evaluated the full costs of running pollinator monitoring schemes against the economic benefits to research and society they provide. The annual costs of monitoring are &lt;0.02% of the economic value of pollination services that would be lost after a 30% decline in pollination services. Furthermore, by providing high‐quality scientific data, monitoring schemes would save at least £1.5 on data collection per £1 spent. Our findings demonstrate that long‐term systematic monitoring can be a cost‐effective tool for both answering key research questions and setting action points for policymakers. Careful consideration must be given to scheme design, the logistics of national‐scale implementation and resulting data quality when selecting the most appropriate combination of surveyors, methods and site networks to deliver a successful scheme.

Factors Influencing Fog Computing Adoption Based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework

The rapid increase of data generated has brought challenges on data quality level. Fog computing in general has been supporting the requirements of end user devices that could not be met by cloud computing solution and it is acknowledged to have a major impact on how an organisation decides to adopt for preprocessing a huge amount of data being generated by the devices. Since IoT devices generating very heterogeneous and dynamic data, there are challenges for the level of data quality. The limitation has hindered the development of fog systems framework that capable operating the dynamic execution of edge devices that handling generation and collection large amounts of data on-premise and off-premise. Thus,sufficient operations of identifying Quality of Result enable user to detect any problems when conducting the decision making. The aim of this paper is to address the factors that perceived likely to influence the adoption of fog computing in evaluating the data analysis on data transmitted from the ever increases devices.A conceptual framework has been constructed considering attributes such as heterogeneous data analysis (on-premise and off-premise) and Quality of Results (quality indicators, quality control, validity outcome and reliability outcome).Potential benefits from the implementation of this framework to organisation is it enable to provide greater value and benefits to the business process. The framework of this study could also be influencing and inhibiting the adoption of fog computing.Quality of result has higher chances to satisfy the defined industrial’s requirement. In addition, fog-computing adoption is important for serving an environment for industry to execute, monitor, and analyze a large form of data in a fog landscape.

Corporate Editors in the Evolving Landscape of OpenStreetMap

OpenStreetMap (OSM), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the OSM community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the OSM database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot OSM-QA-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the OSM community—and researchers—might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses.

Quality-driven early stopping for explorative cluster analysis for big data

Data analysis has become a critical success factor for companies in all areas. Hence, it is necessary to quickly gain knowledge from available datasets, which is becoming especially challenging in times of big data. Typical data mining tasks like cluster analysis are very time consuming even if they run in highly parallel environments like Spark clusters. To support data scientists in explorative data analysis processes, we need techniques to make data mining tasks even more efficient. To this end, we introduce a novel approach to stop clustering algorithms as early as possible while still achieving an adequate quality of the detected clusters. Our approach exploits the iterative nature of many cluster algorithms and uses a metric to decide after which iteration the mining task should stop. We present experimental results based on a Spark cluster using multiple huge datasets. The experiments unveil that our approach is able to accelerate the clustering up to a factor of more than 800 by obliterating many iterations which provide only little gain in quality. This way, we are able to find a good balance between the time required for data analysis and quality of the analysis results.

Quality in Non-GxP Research Environment.

There has been increasing evidence in recent years that research in life sciences is lacking in reproducibility and data quality. This raises the need for effective systems to improve data integrity in the evolving non-GxP research environment. This chapter describes the critical elements that need to be considered to ensure a successful implementation of research quality standards in both industry and academia. The quality standard proposed is founded on data integrity principles and good research practices and contains basic quality system elements, which are common to most laboratories. Here, we propose a pragmatic and risk-based quality system and associated assessment process to ensure reproducibility and data quality of experimental results while making best use of the resources.

An Empirical Evaluation of various Discrimination Measures for Discrimination Prevention

Discrimination prevention in Data mining has been studied by researchers. Several methods have been devised to take care of both direct and indirect discrimination prevention. In order to prevent discrimination, each of these methods tries to minimize the impact of discriminating attributes by modifying certain discriminating rules. The discriminating rules are identified using certain threshold and discrimination measure such as elift for direct discrimination and elb for indirect discrimination. Performance of these methods are measured and compared in terms discrimination removal using DDPD, DDPP for direct discrimination and IDPD, IDPP for indirect discrimination as well as resultant data quality using MC and GC for both kinds of discrimination.This paper deals with study of use of discrimination measures other than elift such as slift, clift and olift. The empirical evaluation presented here shows that slift provides best overall performance.

Research on the Technology of Data Cleaning in Big Data

Some dirty data exists inevitably under big data environment, and it seriously affects the data quality, while the technology of data cleaning is one of the most important methods to improve data quality, and the researches on the data cleaning framework are helpful for big data decision. A general framework of data cleaning in big data is proposed, the core data cleaning module includes three submodules, which are incomplete records cleaning, inconsistent data repairing and approximate duplicate records cleaning, and the processes of data cleaning are discussed specifically. The character of big data is volume, variety, value, velocity and complexity, and there are some incomplete, incorrect and duplicate dirty data in original information, which cause the big data uncontrollable and unavailable[1-2]. It is hoped that valuable information can be extracted from the mass data to provide reference for decision makers. Because of error in data merging or migration of dada sources, it is unavoidable to exist some redundant, incomplete, indeterminable and inconsistent data, which is called dirty data and affects seriously the efficiency of data utilization and the quality of decision making. The technology of data cleaning is particularly important to make the data more accurate and consistent, and it can filter or modify the unnecessary data and output the required data. At present, there are some researches on the data cleaning for big data[3-6]. The technology of big data is developed from the traditional technology, and inherits the traditional concepts and analysis methods[7-8], such as data cleaning and data warehouse. The traditional data cleaning can provide high quality data and enhance efficiency and correctness of data analysis. In big data environment, data cleaning is the basis and original process of big data analysis, which decides the data quality of results. The technology of data cleaning in big data is discussed in this paper, and a general framework of data cleaning is proposed.

Who Has Time for Biological Collections Data Quality Feedback? Maybe a Community Can Help

At an institution without a permanent collections manager or curators, who has time to publish data or research issues on that data? Collections with little or no institutional support often benefit from passionate volunteers who continually seek ways to keep them relevant. The University of Texas at El Paso Biodiversity Collections (UTEP-BC) has been cared for in this manner by a small group of dedicated faculty and emeritus curators who have managed with no budget to care for the specimens, perform and publish research about them, and publish a good portion of the collections data. An IMLS grant allowed these dedicated volunteers to hire a Collections Manager who would migrate the already published data from the collections and add unpublished specimen records from the in-house developed FileMaker Pro database to a new collection management system (Arctos) that would allow for better records management and ease of publication. Arctos is a publicly searchable web-based system, but most collections also see the benefit of participation with biodiversity data aggregators such as the Global Biodiversity Information Facility (GBIF), iDigBio, and a multitude of discipline-specific aggregators. Publication of biodiversity data to aggregators is loaded with hidden pathways, acronyms, and tech-speak with which a curator, registrar, or collections manager may not be familiar. After navigating the process to publish the data the reward is feedback! Now data can be improved, and everyone wins, right? In the case of UTEP-BC data, the feedback sits idle as the requirements of the grant under which the Collection Manager was hired take precedence. It will likely remain buried until long after the grant has run its course. Fortunately, the selection of Arctos as a collection management system allowed the UTEP-BC Collection Manager to confer with others publishing biodiversity data to the data aggregators. Members of the Arctos Community have carried on multiple conversations about publishing to aggregators and how to handle the resulting data quality flags. These conversations provide a synthesis of the challenges experienced by collections in over 20 institutions when publishing biodiversity data to aggregators and responding (or not) to their data quality flags. This presentation will cover the experiences and concerns of one Collection Manager as well as those of the Arctos Community related to publishing data to aggregators, deciphering their data quality flags, and development of appropriate responses to those flags.