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ICT has enabled democratic process to be more transparent and enabled citizens' participation in the election process. However, public trust is a mandatory requirement for a good democratic transition. Participation in monitoring election process could be implemented as a crowdsourcing effort to improve public trust in the election result which in this article based on a case study of KawalPilpres to monitor 2019 Indonesian presidential election. Trust factor is a key success for monitoring effort. Therefore, data quality becomes necessity. Data quality is assessed using Loshin's maturity assessment and analysed using Loshin's improvement strategies. Based on our assessments, there are three top categories for improvements namely governance, expectations, and policies of data quality management.

Data collection using Internet-based samples has become increasingly popular in many social science disciplines, including advertising. This research examines whether one popular Internet data source, Amazon's Mechanical Turk (MTurk), is an appropriate substitute for other popular samples utilized in advertising research. Specifically, a five-sample between-subjects experiment was conducted to help researchers who utilize MTurk in advertising experiments understand the strengths and weaknesses of MTurk relative to student samples and professional panels. In comparisons across five samples, results show that the MTurk data outperformed panel data procured from two separate professional marketing research companies across various measures of data quality. The MTurk data were also compared to two different student samples, and results show the data were at least comparable in quality. While researchers may consider MTurk samples as a viable alternative to student samples when testing theory-driven outcomes, precautions should be taken to ensure the quality of data regardless of the source. Best practices for ensuring data quality are offered for advertising researchers who utilize MTurk for data collection.

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

The identification of microplastics becomes increasingly challenging with decreasing particle size and increasing sample heterogeneity. The analysis of microplastic samples by Fourier transform infrared (FTIR) spectroscopy is a versatile, bias-free tool to succeed at this task. In this study, we provide an adaptable reference database, which can be applied to single-particle identification as well as methods like chemical imaging based on FTIR microscopy. The large datasets generated by chemical imaging can be further investigated by automated analysis, which does, however, require a carefully designed database. The novel database design is based on the hierarchical cluster analysis of reference spectra in the spectral range from 3600 to 1250 cm−1. The hereby generated database entries were optimized for the automated analysis software with defined reference datasets. The design was further tested for its customizability with additional entries. The final reference database was extensively tested on reference datasets and environmental samples. Data quality by means of correct particle identification and depiction significantly increased compared to that of previous databases, proving the applicability of the concept and highlighting the importance of this work. Our novel database provides a reference point for data comparison with future and previous microplastic studies that are based on different databases.

PurposeQuality 4.0 is an emerging research topic concerned with rethinking how quality management needs to be adopted in the digital era. The purpose of this research is to conduct a systematic review on the state of the research in the field of Industry 4.0 impact on improving quality management aspects and how technology can be leveraged to enhance its practices.Design/methodology/approachA systematic review of the literature published in the last 5 years is conducted. 52 papers were selected, mapped based on the technology they focused on and categorized based on the addressed quality aspects.FindingsThe review revealed various areas where quality management can benefit from Industry 4.0 technologies, identified several research gaps and suggested new directions for future research. Firstly, the literature provided some insights about industry 4.0 potential contributions but lacks further detail on the exact applications and solutions through use cases and case studies. Secondly, there has been a focus on the potential benefits provided for quality control while there is a clear scarcity in terms of the other quality management tools and methodologies. Thirdly, there is a lack of studies on economic analysis or detailed impacts on quality costs that justifies the substantial investments needed. Finally, there is a need for including more studies about the mapping and integration of ISO 9001 requirements and Industry 4.0 features.Originality/valueThis is the first attempt to conduct a comprehensive review on the ways industry 4.0 technologies can be leveraged for the field of quality management. Based on this review, several directions for further research in this area are identified.

This paper reviews the literature in historical record linkage in the United States and examines the performance of widely used record-linking algorithms and common variations in their assumptions. We use two high-quality, hand-linked data sets and one synthetic ground truth to examine the direct effects of linking algorithms on data quality. We find that (i) no algorithm (including hand linking) consistently produces representative samples; (ii) 15 to 37 percent of links chosen by widely used algorithms are classified as errors by trained human reviewers; and (iii) false links are systematically related to baseline sample characteristics, showing that some algorithms may introduce systematic measurement error into analyses. A case study shows that the combined effects of (i)–(iii) attenuate estimates of the intergenerational income elasticity by up to 29 percent, and common variations in algorithm assumptions result in greater attenuation. As current practice moves to automate linking and increase link rates, these results highlight the important potential consequences of linking errors on inferences with linked data. We conclude with constructive suggestions for reducing linking errors and directions for future research.

Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from US statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.

Globally, countries report forest information to the Food and Agriculture Organization (FAO) of the United Nations Global Forest Resources Assessments (FRA) at regular intervals. While the status and trends of national forest monitoring capacities have been previously assessed for the tropics, this has not been systematically done worldwide. In this paper, we assess the use and quality of forest monitoring data sources for national reporting to the FRA in 236 countries and territories. More specifically, we (a) analyze the use of remote sensing (RS) for forest area monitoring and the use of national forest inventory (NFI) for monitoring forest area, growing stock, biomass, carbon stock, and other attributes in FRA 2005–2020, (b) assess data quality in FRA 2020 using FAO tier-based indicators, and (c) zoom in to investigate changes in tropical forest monitoring capacities in FRA 2010–2020. Globally, the number of countries monitoring forest area using RS at good to very good capacities increased from 55 in FRA 2005 to 99 in FRA 2020. Likewise, the number of countries with good to very good NFI capacities increased from 48 in FRA 2005 to 102 in FRA 2020. This corresponds to ∼85% of the global forest area monitored with one or more nationally-produced up-to-date RS products or NFI in FRA 2020. For large proportions of global forests, the highest quality data was used in FRA 2020 for reporting on forest area (93%), growing stock (85%), biomass (76%), and carbon pools (61%). Overall, capacity improvements are more widespread in the tropics, which can be linked to continued international investments for forest monitoring especially in the context of reducing emissions from deforestation and forest degradation in tropical countries (REDD+). More than 50% of the tropical countries with targeted international support improved both RS and NFI capacities in the period 2010–2020 on top of those that already had persistent good to very good capabilities. There is also a link between improvements in national capacities and improved governance measured against worldwide governance indicators (WGI). Our findings—the first global study—suggest an ever-improving data basis for national reporting on forest resources in the context of climate and development commitments, e.g. the Paris Agreement and Sustainable Development Goals.

The purpose of this study is to empirically address questions pertaining to the effects of data screening practices in survey research. This study addresses questions about the impact of screening techniques on data and statistical analyses. It also serves an initial attempt to estimate descriptive statistics and graphically display the distributions of popular screening techniques. Data were obtained from an online sample who completed demographic items and measures of character strengths (N = 307). Screening indices demonstrate minimal overlap and differ in the number of participants flagged. Existing cutoff scores for most screening techniques seem appropriate, but cutoff values for consistency-based indices may be too liberal. Screens differ in the extent to which they impact survey results. The use of screening techniques can impact inter-item correlations, inter-scale correlations, reliability estimates, and statistical results. While data screening can improve the quality and trustworthiness of data, screening techniques are not interchangeable. Researchers and practitioners should be aware of the differences between data screening techniques and apply appropriate screens for their survey characteristics and study design. Low-impact direct and unobtrusive screens such as self-report indicators, bogus items, instructed items, longstring, individual response variability, and response time are relatively simple to administer and analyze. The fact that data screening can influence the statistical results of a study demonstrates that low-quality data can distort hypothesis testing in organizational research and practice. We recommend analyzing results both before and after screens have been applied.