Data Quality Assessment in Smart Manufacturing: A Review

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed upon two primary needs for the wide use of quality metrics: (i) an evolving list of comprehensive quality metrics and (ii) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in Proteomics, Proteomics Clinical Applications, Journal of Proteome Research, and Molecular and Cellular Proteomics, as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

ObjectivesPrimary objective: to assess nine data quality metrics for 14 maternal and newborn health data elements, following implementation of an integrated, district-focused data quality intervention. Secondary objective: to consider whether...

BackgroundSingle-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking.ResultsHere we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package.Conclusionsscruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.

The Practitioner's Guide to Data Quality Improvement

The rapid evolution of manufacturing technologies under the Industry 4.0 paradigm has driven the need for efficient data management and real-time decision-making. The client-server architecture is Central to this transformation, enabling seamless communication, data sharing, and process automation across smart manufacturing environments. This literature review explores the pivotal role of client-server architecture and key communication protocols in enabling Smart Manufacturing and Industry 4.0 initiatives. The study examines the fundamental components of client-server architecture, including clients, servers, and communication protocols, and their interactions in industrial settings. It focuses on three primary protocols, MQTT, HTTP, and OPC UA, analyzing their mechanisms, benefits, and applications in smart manufacturing environments. The research highlights how MQTT’s lightweight, publish-subscribe model facilitates efficient real-time data exchange in IoT-heavy industrial networks. HTTP’s widespread adoption and versatility in handling diverse data types are discussed, emphasizing its role in web-based dashboards and cloud integration. The review also delves into OPC UA’s platform-independent nature and advanced features, which promote interoperability and future-proofing of manufacturing systems. This review synthesizes current knowledge of these technologies and underscores their collective importance in addressing critical requirements of smart manufacturing, including scalability, security, real-time capabilities, and cross-system compatibility. The findings suggest that integrating these communication protocols and architectures is crucial for realizing the full potential of Industry 4.0, paving the way for more agile, efficient, and data-driven production ecosystems.

Metadata is an important document of image about outcome, which reflects history and status of the results. One of the factors affecting the quality of image data is quality of metadata, and quality of metadata is a decisive factor whether the user adopts dataset. Therefore, quality of metadata must be controlled; and metadata will introduce a random uncertainty in the process of entrying, editing, modifing, which must be carried out uncertainty analysis in order to improve the accuracy of check of metadata. First, analyzing the qualitative requirements of image metadata, and exploring how to control the quality of image metadata from acquired methods for manual entry and automatically generated by software. Second, analyzing the results of metadata and checking metadata, by checking image metadata to find and correct data items and attribute values to control the quality of metadata. There are three methods of check, one is template check for there being standard data template; the other is classification check for there no being standard template; the last one is the check of coordinate metadata to check the coordinated cutting scope. At last, I use the certain theory to make uncertainty analysis for metadata attributes.

The recent incorporation of standardized data quality metrics into the GBIF, iDigBio, and ALA portal infrastructures enables data providers with useful information they can use to clean or augment Darwin Core data at the source based on these recommendations. Numerous taxonomic and geographic based metrics provide useful information on the quality of various Darwin Core fields in this realm, while also providing input on Darwin Core compliance for others. As a provider/data manager for the Biodiversity Institute, University of Kansas, and having spent some time evaluating their efficacy and reliability, this presentation will highlight some of the positive and negative aspects of my experience with specific examples while highlighting concerns regarding the user experience and standardization of these metrics across the aggregator landscape. These metrics have indicated both data and publishing issues that have increased the utility and cleanliness of our data while also highlighting batch processing challenges and issues with the process of inferring "bad" data. The integration of these metrics into source database infrastructure will also be postulated, with Specify Software as an example.

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making.

The effects of protocol factors and participant characteristics on functional near-infrared spectroscopy data quality after stroke

The purpose of this study is to define the dimensions, assessment, and analyze the data quality on higher education. This study followed the rules of data quality in higher education that have been determined by the Ministry of Research, Technology and Higher Education of the Republic of Indonesia. This study conducted a case study on Institute of Statistics, one of the institutions of higher education in Indonesia, by performing direct queries against a database of an integrated information system that used to record data on the implementation of higher education. The findings of this study indicated that the dimensions of data quality that can be used to measure the quality of data in this study 3 of 4 dimensions established by the government that is completeness, validity, and accuracy. From the results of data quality assessment in this study found that the data quality of higher education in the Institute of Statistics has not complied with data quality requirements that required by the government. The implications emanating from this study is that data quality assessment is an essential thing that the institution of higher education should do. The result of this assessment can be used to be aware of the current condition of data quality from the organizations. Furthermore, the researchers used the results of this study to conduct further research to develop strategies to improve data quality.

Towards a content agnostic computable knowledge repository for data quality assessment

Adverse ground behavior events, such as convergence and ground falls, pose critical risks to underground mine safety and productivity. Today, monitoring of such failures is primarily conducted using legacy techniques with low spatial and temporal resolution while exposing workers to hazardous environments. This study assesses the potential of novel simultaneous localization and mapping (SLAM)-based light detection and ranging (Lidar) data quality for rapid, digital, and eventually autonomous mine-wide underground geotechnical monitoring. We derive a comprehensive suite of quality metrics based on tests in two underground mines for two state-of-the-art mobile laser scanning (MLS) systems. Our results provide evidence that SLAM-based MLS provides data of the quality required to detect geotechnically relevant changes while being significantly more efficient for large mine layouts when compared to traditional static systems. Additionally, we show that SLAM-specific processing can achieve an order of magnitude better relative accuracy relevant for change detection than quality metrics derived from traditionally deployed tests would suggest while reducing SLAM drift error by up to 90%. In collaboration with an operating block cave mine, we confirm these capabilities in field tests on a mine-wide scale and, for the first time, demonstrate methods of rockfall detection using MLS data. While more work is required to investigate optimal collection, processing, and utilization of MLS data, we demonstrate its potential to become an effective and widely applicable data source for rapid, accurate, and comprehensive geotechnical inspections.

In industrial settings, measuring the quality of data used to represent an intended domain of use and its operating conditions is crucial and challenging. Thus, this paper aims to present a set of metrics addressing this data quality issue in the form of a library, named DQM (Data Quality Metrics), for Machine Learning (ML) use. Additional metrics specific to industrial application are developed in the proposed library. This work aims also to assess various data and datasets types. Those metrics are used to characterize the training and evaluating datasets involved in the process of building ML models for industrial use cases. Two categories of metrics are implemented in DQM: inherent data metrics, are the ones evaluating the quality of a given dataset independently from the ML model such as statistical proprieties and attributes, and model dependent metrics which are those implemented to measure the quality of the dataset by considering the ML model outputs such the gap between two datasets in regards to a given ML model. DQM is used in the scope of the Confiance.ai program to evaluate datasets used for industrial purposes such as autonomous driving.

In the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before. The purpose of this article is to provide an insight into the data quality definitions, dimensions, and assessment methodologies. In this article, a scoping literature review approach was used to describe and summarize the main concepts related to data quality and data quality assessment methodologies. Search terms were selected to find the relevant articles published between January 1, 2012 and September 31, 2022. The retrieved articles were then reviewed and the results were reported narratively. In total, 23 papers were included in the study. According to the results, data quality dimensions were various and different methodologies were used to assess them. Most studies used quantitative methods to measure data quality dimensions either in paper-based or computer-based medical records. Only two studies investigated respondents' opinions about data quality. In health care, high-quality data not only are important for patient care, but also are vital for improving quality of health care services and better decision making. Therefore, using technical and nontechnical solutions as well as constant assessment and supervision is suggested to improve data quality.

Background: There has been limited work comparing survey characteristics and assessing the quality of child anthropometric data from population-based surveys. Objective: To investigate survey characteristics and indicators of quality of anthropometric data in children aged 0–59 months from 23 countries in the West Central Africa region. Methods: Using established methodologies and criteria to examine child age, sex, height, and weight, we conducted a comprehensive assessment and scoring of the quality of anthropometric data collected in 100 national surveys. Results: The Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) collected data from a greater number of younger children than older children while the opposite was found for the National Nutrition Surveys (NNS). Missing or implausible height/weight data proportions were 12% and 8% in MICS and DHS compared to 3% in NNS. Average data quality scores were 14 in NNS, 33 in DHS, and 41 in MICS. Conclusions: Although our metric of data quality suggests that data from the NNS appear more consistent and robust, it is equally important to consider its disadvantages related to access and lack of broader socioeconomic information. In comparison, the DHS and MICS are publicly-accessable for research and provide socioeconomic context essential for assessing and addressing the burden of undernutrition within and between countries. The strengths and weaknesses of data from these three sources should be carefully considered when seeking to determine the burden of child undernutrition and its variation within countries.