This paper presents a comprehensive exploration of data quality terminology, revealing a significant lack of standardisation in the field. The goal of this work was to conduct a comparative analysis of data quality terminology across different domains and structure it into a hierarchical data model. We propose a novel approach for aggregating disparate data quality terms used to describe the multiple facets of data quality under common umbrella terms with a focus on the ISO 25012 standard. We introduce four additional data quality dimensions: governance, usefulness, quantity, and semantics. These dimensions enhance specificity, complementing the framework established by the ISO 25012 standard, as well as contribute to a broad understanding of data quality aspects. The ISO 25012 standard, a general standard for managing the data quality in information systems, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of digital systems across a multitude of domains. In contrast, frameworks such as ALCOA+, which were originally developed for specific regulated industries, can be applied more broadly but may not always be generalisable. Ultimately, the model we propose aggregates and classifies data quality terminology, facilitating seamless communication of the data quality between different domains when collaboration is required to tackle cross-domain projects or challenges. By establishing this hierarchical model, we aim to improve understanding and implementation of data quality practices, thereby addressing critical issues in various domains.

This study reviews various data quality frameworks that have some form of regulatory backing. The aim is to identify how these frameworks define, measure, and apply data quality dimensions. This review identified generalisable frameworks, such as TDQM, ISO 8000, and ISO 25012, and specialised frameworks, such as IMF’s DQAF, BCBS 239, WHO’s DQA, and ALCOA+. A standardised data quality model was employed to map the dimensions of the data from each framework to a common vocabulary. This mapping enabled a gap analysis that highlights the presence or absence of specific data quality dimensions across the examined frameworks. The analysis revealed that core data quality dimensions such as “accuracy”, “completeness”, “consistency”, and “timeliness” are equally and well represented across all frameworks. In contrast, dimensions such as “semantics” and “quantity” were found to be overlooked by most frameworks, despite their growing impact for data practitioners as tools such as knowledge graphs become more common. Frameworks tailored to specific domains were also found to include fewer overall data quality dimensions but contained dimensions that were absent from more general frameworks, highlighting the need for a standardised approach that incorporates both established and emerging data quality dimensions. This work condenses information on commonly used and regulation-backed data quality frameworks, allowing practitioners to develop tools and applications to apply these frameworks that are compliant with standards and regulations. The bibliometric analysis from this review emphasises the importance of adopting a comprehensive quality framework to enhance governance, ensure regulatory compliance, and improve decision-making processes in data-rich environments.

The machine learning algorithm based on multiple-input multiple-output linear regression models has been developed to describe PM2.5 and PM10 concentrations over time. The algorithm is fact-acting and allows for speedy forecasts without requiring demanding computational power. It is also simple enough that it can self-update by introducing a recursive step that utilizes newly measured values and forecasts to continue to improve itself. Starting from raw data, pre-processing methods have been used to verify the stationary data by employing the Dickey–Fuller test. For comparison, weekly and monthly decompositions have been achieved by using Savitzky–Golay polynomial filters. The presented algorithm is shown to have accuracies of $30\%$ for PM2.5 and $26\%$ for PM10 for a forecasting horizon of 24 h with a quarter-hourly data acquisition resolution, matching other results obtained using more computationally demanding approaches, such as neural networks. We show the feasibility of using multivariate linear regression (together with the small real-time computational costs for the training and testing procedures) to forecast particulate matter air pollutants and avoid environmental threats in real conditions.

This paper presents a comprehensive exploration of Data Quality terminology, revealing a significant lack of standardisation in the field. We propose a novel approach to aggregating disparate Data Quality terms used to describe the multiple facets of Data Quality, under common umbrella terms, with a focus on the ISO 25012 standard. Our aim is to design a Data Quality Data Model that serves as a universally applicable framework for Data Quality assessment. We introduce four additional Data Quality dimensions: Governance, Usefulness, Quantity, and Semantics, enhancing specificity, complementing the framework established by the ISO 25012 standard, and understanding of Data Quality aspects. The ISO 25012 standard, while tailored for software development, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of software development across a multitude of domains. In contrast, frameworks like ALCOA+ that are specific to certain domains lack the ability to be generalised. The model we propose can be seen as a "Rosetta Stone" for Data Quality terminology, facilitating a seamless communication of Data Quality between different domains when collaboration is required to tackle cross-domain projects or challenges.

Introduction: Clinical trial representativeness is essential for ensuring that study findings generalise to target treatment populations. Current assessment approaches rely on subjective demographic comparisons that lack standardisation and fail to account for data quality. Existing data quality frameworks assess completeness at the dataset level as the availability of required data values but do not address cohort-specific suitability for representativeness analysis. This study proposes that for clinical trial datasets, the completeness dimension should be interpreted through two complementary aspects: demographic coverage (the degree to which trial demographics represent the target population) and dataset completeness (the availability of required demographic data for analysis). Missing demographic data can compromise similarity assessments, yet no standardised approach exists to integrate both aspects of completeness into representativeness evaluation. Methods: This work introduces a quantitative framework that operationalises this dual interpretation of completeness. Coverage is measured using Jensen–Shannon Distance across demographic distributions, while dataset completeness quantifies the percentage of valid data available. These metrics are aggregated through clinically informed weights into a single suitability score. The framework was validated using simulated trial cohorts sampled from the MIMIC-III database and compared against a target sepsis population of 1,315 patients extracted from the same dataset. The validation employed simple random sampling with varying cohort sizes to simulate Phase II/III single-centre trial scenarios. Results: Results showed that demographic alignment drives overall suitability. Correlation analysis confirmed that coverage and completeness vary independently, validating the composite metric approach. Suitability scores ranged from 86.5% to 91.6% (mean 89.1%), demonstrating the framework’s ability to discriminate between cohorts. Cohort size showed no correlation with suitability ( r = 0.08 , p = 0.42 ), indicating that high representativeness can be achieved in smaller trials. Patient weight and language showed the lowest completeness, reflecting dataset limitations that can be mitigated through feature weighting. Discussion: Interpretability and reproducibility suggest potential for integration into trial design workflows, supporting evidence-based enrolment strategies and transparent regulatory evaluation. By providing systematic, quantifiable metrics, this work advances clinical trial quality assessment beyond qualitative comparisons and establishes a foundation for systematic evaluation of trial representativeness.