The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India

The importance of data quality to correctly determine prevalence estimates of child anthropometric failures has been a contentious issue among policymakers and researchers. Our research objective was to ascertain the impact of improved DHS data quality on the prevalence estimates of stunting, wasting, and underweight. The study also looks for the drivers of data quality. Using five data quality indicators based on age, sex, anthropometric measurements, and normality distribution, we arrive at two datasets of differential data quality and their estimates of anthropometric failures. For this purpose, we use the 2005–2006 and 2015–2016 NFHS data covering 311,182 observations from India. The prevalence estimates of stunting and underweight were virtually unchanged after the application of quality checks. The estimate of wasting had fallen 2 percentage points, indicating an overestimation of the true prevalence. However, this differential impact on the estimate of wasting was driven by the flagging procedure’s sensitivity and was in accordance with empirical evidence from existing literature. We found DHS data quality to be of sufficiently high quality for the prevalence estimates of stunting and underweight, to not change significantly after further improving the data quality. The differential estimate of wasting is attributable to the sensitivity of the flagging procedure.

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP). Results The data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

Facilitating Harmonized Data Quality Assessments. A Data Quality Framework for Observational Health Research Data Collections With Software Implementations in R

Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).Results The data quality framework comprises 34 data quality indicators. These target three aspects of data quality: compliance with pre-specified structural and technical requirements (Integrity), presence of data values (completeness), and error in the data values (correctness). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses.

Analysis of discrepancies in mirror data relating to the weight of goods in intra-EU trade statistics

In the research carried out to date by the authors of the article, the assessment of the quality of mirror data in the exchange of goods between European Union (EU) countries was based on the value of goods. A similar approach is applied by many researchers. The aim of the research discussed in the article is to assess the quality of data relating to intra-EU trade based on not only the value, but also on the quantity of goods. The analysis of discrepancies in data relating to trade between EU countries, with a particular emphasis on Poland, was based on selected research methods known from literature. Both the value-based and the quantitative approach constitute the authors' contribution to the development of research methodology. Data quality indicators were proposed and data pertaining to the weight of goods were used. Information on trade in goods between EU countries in 2017 obtained from Eurostat's Comext database was analysed. The research relating to the dynamics also covered the years 2005, 2008, 2011, and 2014. The results of the analysis demonstrated that the total share of export of goods from Poland to a given country within the EU is different for data expressed in value (value of goods) and in quantity (weight of goods). Therefore, both approaches should be applied in the study of the quality of mirror data.

Best Practices Framework for Improving Maintenance Data Quality to Enable Asset Performance Analytics

Field maintenance data is often captured manually and is prone to have incomplete and inaccurate information in the structured fields. However, unstructured fields captured through work order planning, scheduling, and execution contains a wealth of historical information about asset performance, failure patterns, and maintenance strategies. The prevalent data quality issues in maintenance data need to be understood and processed in order to extract actionable intelligence. This paper describes a best practices framework for measuring and improving data quality, developed through years of research and working with 120+ process and manufacturing organizations. The framework enables evaluating and executing analytics by identifying strengths in the data. It determines where and how asset performance measures such as benchmarking metrics, reliability measures, and bad actor identification can be evaluated with confidence. Missing or inconsistent information can be extracted from the unstructured fields using natural language processing (NLP) techniques to bridge gaps in the analysis. While the NLP algorithms make historical data usable for some analytics, the best practices identify improvements in the work process of capturing data, thereby improving future quality. A feedback on data quality indicators completes the loop to sustain improvements.

Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups.