Data Quality Standards Research Articles

The importance and challenges of data collection in risk assessment

Reliable data collection is paramount for assessing and mitigating risks posed by natural hazards and climate change to urban sustainability and resilience. This study underscores the significance of diverse data collection methods, including field surveys, artificial intelligence (AI) analysis, and satellite imagery, each essential for developing robust risk assessment frameworks. Field surveys offer detailed insights into structural and non-structural building components, critical for localized risk assessments. AI enhances data processing efficiency, crucial for handling extensive datasets and enabling rapid response capabilities. Satellite imagery provides comprehensive area coverage, invaluable for strategic planning and emergency management in large or inaccessible areas. However, these methods also present unique challenges. Field surveys are resource-intensive and susceptible to environmental conditions, which can affect data accuracy. AI, while efficient, requires high-quality data and may struggle with complex scenarios that deviate from its training data. Satellite imagery, although broad-reaching, may lack the necessary resolution for detailed assessments and is dependent on weather conditions. Addressing these challenges is crucial to ensuring the integrity and reliability of risk assessments. By continuously refining these methods and maintaining high standards for data quality and ethical considerations, we can better prepare for and mitigate the impacts of natural hazards and climate change. This commitment to advancing data collection techniques is essential for protecting our built environments and ensuring community resilience.

A Research Paper on: AI/ML-Based Data Duplication Alert System

Data duplication is a pervasive issue across organizations dealing with extensive data, leading to wasted storage, increased processing costs, and compromised data integrity. Traditional methods for identifying and managing data duplication are often time-consuming and inefficient, especially as data volumes continue to scale. To address these challenges, we propose an AI/ML-Based Data Duplication Alert System, leveraging machine learning algorithms to intelligently detect and alert users to potential data duplication. The system employs advanced techniques such as natural language processing (NLP), pattern recognition, and clustering to analyze data structures and content across databases, documents, and storage locations. By utilizing both supervised and unsupervised learning models, it can detect duplicate data entries even when they include typos or structural variations. Models are evaluated using statistical metrics such as Receiver Operating Characteristic (ROC) curves, precision, recall, and accuracy rates exceeding 95%, ensuring high reliability in detecting duplicates. In addition to real-time alerts, the system integrates seamlessly with data management workflows, preventing duplicate entries at the point of data entry, thus upholding data quality standards. This AI/ML-based solution automates the detection process, enabling faster response times, reducing storage requirements, and improving data accuracy. By ensuring data consistency, the system promotes more efficient data utilization across organizational systems while maintaining a high standard of accuracy and precision.

Building a Foundation for High-Quality Health Data: Multihospital Case Study in Belgium.

Data quality is fundamental to maintaining the trust and reliability of health data for both primary and secondary purposes. However, before the secondary use of health data, it is essential to assess the quality at the source and to develop systematic methods for the assessment of important data quality dimensions. This case study aims to offer a dual aim-to assess the data quality of height and weight measurements across 7 Belgian hospitals, focusing on the dimensions of completeness and consistency, and to outline the obstacles these hospitals face in sharing and improving data quality standards. Focusing on data quality dimensions completeness and consistency, this study examined height and weight data collected from 2021 to 2022 within 3 distinct departments-surgical, geriatrics, and pediatrics-in each of the 7 hospitals. Variability was observed in the completeness scores for height across hospitals and departments, especially within surgical and geriatric wards. In contrast, weight data uniformly achieved high completeness scores. Notably, the consistency of height and weight data recording was uniformly high across all departments. A collective collaboration among Belgian hospitals, transcending network affiliations, was formed to conduct this data quality assessment. This study demonstrates the potential for improving data quality across health care organizations by sharing knowledge and good practices, establishing a foundation for future, similar research.

Assessing Resources Devoted to Electronic Reporting and Automation Among Central Cancer Registries Meeting National Program of Cancer Registries (NPCR) Quality Standards

The Centers for Disease Control and Prevention (CDC) has emphasized a need for data modernization initiatives (DMIs) to improve the quality and timeliness of cancer surveillance data. To guide such DMI efforts, we need data on the resources required to generate high-quality data. Twenty-one central cancer registries (CCRs) collected data from July 2020 to June 2021. We explored the potential relationship between resources for (1) electronic reporting and automation and (2) the quality of CCR data. We then compared activity-based costs of registries that always (n = 8), sometimes (n = 6), or seldom/never (n = 7) met data quality standards for completeness, timeliness, duplicate rate, and missing values. We found the registries that consistently met data quality standards used more resources on setting up processes to acquire data, training staff, and processing data. Compared to high-quality registries, registries that seldom/never met quality standards spent the most on case finding/data abstraction of non-hospital records. This study provides key findings for resource use, which can guide advancements when implementing electronic reporting and automation to improve CCR operations.

Multi-Platform Collaboration in Integrated Surveying: Ensuring Completeness and Reliability of Geospatial Data—A Case Study

Multi-platform geospatial data synergy is critical for complete and reliable surveys. This study investigates various methods for combining data from terrestrial laser scanning (TLS), orthophotos, databases of topographic objects, utility databases, tacheometry, and GNSS to assess and improve positioning accuracy and consistency of data in spatial databases. The study highlights the challenges and solutions regarding integrating various datasets to yield a complete and reliable geospatial database for building surveys and the construction and keeping of spatial databases. Input from diversified surveying technologies, such as TLS, GNSS, and orthophotos, offers detailed and precise data necessary to create and update accurate base maps and databases of topographic objects. A complete survey of a structure and its surroundings demonstrates how the synergistic application of diverse data sources helps improve the positioning accuracy and consistency of spatial databases. The results indicate the importance of multi-platform collaboration for high data quality standards in surveying, which is critical for effective planning and implementation of surveying projects.

Enterprise Data Governance : A Comprehensive Framework for Ensuring Data Integrity, Security, and Compliance in Modern Organizations

Enterprise data governance has emerged as a critical function for organizations seeking to maximize the value of their data assets while mitigating risks associated with data management. This article presents a comprehensive framework for implementing effective data governance strategies in modern enterprises. It examines the key components of robust governance structures, including clear policies, well-defined roles and responsibilities, and the establishment of data quality standards. The article explores methods for ensuring regulatory compliance and enhancing data security, emphasizing the importance of risk management and data protection measures. By analyzing case studies across various industries, the article identifies best practices and common challenges in data governance implementation. Additionally, it investigates the impact of emerging technologies such as artificial intelligence and blockchain on data governance practices. The findings underscore the necessity of a holistic approach to data governance that aligns with organizational objectives, fosters a data-driven culture, and adapts to evolving regulatory landscapes. This article contributes to the growing body of knowledge on enterprise data management and provides practical insights for organizations striving to improve their data governance capabilities in an increasingly complex digital environment.

Importance of the quality management of aerobiological monitoring networks: The case study of Madrid Region in Spain

Interest in biological air quality monitoring is rising, and updated public information is increasingly demanded by stakeholders in the case of airborne pollen, which requires maintaining high standards of data quality. The number of aerobiological stations worldwide is continuously growing, and quality management is becoming more complex with the increase in the scale of aerobiological networks. Quality control exercises are crucial for maintaining the quality of the data used in the pollen monitoring routine over time. In this study we show the results of an intercomparison test among technicians in the Madrid Region Palynological Network in central Spain in order to identify potential sources of error during the pollen analysis. The findings of this intercomparison exercise indicated very high-quality pollen data based on two different proficiency tests: i) the technicians' analysis of common samples with the light microscopes used in the routine pollen analysis; and ii) the analysis of common samples with all technicians using the same light microscope. A few specific remarkable errors were detected (i.e., those whose Absolute Error > |10| and Relative Error > |20 %|), such as confusion by four participants between Quercus vs. Acer and Quercus vs. Platanus pollen. Since the pollen analysis routine requires very experienced professionals, we believe it is essential to provide adequate training programmes for pollen analysis and the specific identification of these problematic pollen types. High-quality environmental data is crucial for sharing with regional, national and continental networks to ensure applicability, reproducibility and integration in large-scale studies.

A standardized image processing and data quality platform for rodent fMRI

Functional magnetic resonance imaging in rodents holds great potential for advancing our understanding of brain networks. Unlike the human community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences, is a pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, this software platform will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.

Gas well production optimization: Classifying liquid loading severity in shale gas wells using semi-supervised learning

The liquid loading of shale gas wells presents significant challenges, including a reduction in production capacity, an increase in production costs, and environmental degradation. It is of paramount importance to provide accurate and timely warnings of liquid loading in order to ensure the efficient production operations. Existing physical models often rely on assumptions regarding material properties and fluid behaviors. However, the establishment of precise physical models in complex real-world scenarios represents a significant challenge. Most existing data-driven methods rely on either supervised or unsupervised learning strategies. The former causes a considerable investment of time and human resources to generate labels, while the latter demands exceptionally high standards for data quality. These are challenging to achieve in actual production. Most studies on liquid loading adopt a dichotomous approach, whereby the presence or absence of liquid loading is determined. A paucity of studies has attempted to classify the severity of liquid loading. In this paper, a semi-supervised learning-based liquid loading severity classification model is proposed. The model integrates the benefits of supervised and unsupervised learning, enabling the utilization of the entire dataset with a minimal number of labels. The paper also presents a Dynamic Time Relationship Self-Attention (DTRSA) module, which enhances the model’s anti-interference ability against other anomalies and improves the accuracy of liquid loading severity classification. In order to assess the effectiveness of the proposed model, experiments are conducted on a dataset of 219 shale gas wells in the southwestern region of Sichuan Province, China, within an oil and gas field. The model exhibits an overall classification accuracy of 98.46% in detecting liquid loading, with an earlier warning time than existing physical models.

Cancer survival statistics in China 2019–2021: a multicenter, population-based study

BackgroundA milestone goal of the Healthy China Program (2019–2030) is to achieve 5-year cancer survival at 43.3% for all cancers combined by 2022. To assess the progress towards this target, we analyzed the updated survival for all cancers combined and 25 specific cancer types in China from 2019 to 2021. MethodsWe conducted standardized data collection and quality control for cancer registries across 32 provincial-level regions in China, and included 6,410,940 newly diagnosed cancer patients from 281 cancer registries during 2008–2019, with follow-up data on vital status available until December 2021. We estimated the age-standardized 5-year relative survival overall and by site, age group, and period of diagnosis using the International Cancer Survival Standard Weights, and quantified the survival changes to assess the progress in cancer control. ResultsIn 2019–2021, the age-standardized 5-year relative survival for all cancers combined was 43.7% (95% confidence interval [CI], 43.6–43.7). The 5-year relative survival varied by cancer type, ranging from 8.5% (95% CI, 8.2–8.7) for pancreatic cancer to 92.9% (95% CI, 92.4–93.3) for thyroid cancer. Eight cancers had 5-year survival of over 60%, including cancers of the thyroid, breast, testis, bladder, prostate, kidney, uterus, and cervix. The 5-year relative survival was generally lower in males than in females. From 2008 to 2021, we observed significant survival improvements for cancers of the lung, prostate, bone, uterus, breast, cervix, nasopharynx, larynx, and bladder. The most significant improvement was in lung cancer. ConclusionsProgress in cancer control was evident in China. This highlights the importance of a comprehensive approach to control and prevent cancer.

LinkedDataOps:quality oriented end-to-end geospatial linked data production governance

This work describes the application of semantic web standards to data quality governance of data production pipelines in the architectural, engineering, and construction (AEC) domain for Ordnance Survey Ireland (OSi). It illustrates a new approach to data quality governance based on establishing a unified knowledge graph for data quality measurements across a complex, heterogeneous, quality-centric data production pipeline. It provides the first comprehensive formal mappings between semantic models of data quality dimensions defined by the four International Organization for Standardization (ISO) and World Wide Web Consortium (W3C) data quality standards applied by different tools and stakeholders. It provides an approach to uplift rule-based data quality reports into quality metrics suitable for aggregation and end-to-end analysis. Current industrial practice tends towards stove-piped, vendor-specific and domain-dependent tools to process data quality observations however there is a lack of open techniques and methodologies for combining quality measurements derived from different data quality standards to provide end-to-end data quality reporting, root cause analysis or visualisation. This work demonstrated that it is effective to use a knowledge graph and semantic web standards to unify distributed data quality monitoring in an organisation and present the results in an end-to-end data dashboard in a data quality standards-agnostic fashion for the Ordnance Survey Ireland data publishing pipeline.

Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education

Despite the promises of ML in education, its adoption in the classroom has surfaced numerous issues regarding fairness, accountability, and transparency, as well as concerns about data privacy and student consent. A root cause of these issues is the lack of understanding of the complex dynamics of education, including teacher-student interactions, collaborative learning, and classroom environments. To overcome these challenges and fully utilize the potential of ML in education, software practitioners need to work closely with educators and students to fully understand the context of the data (the backbone of ML applications) and collaboratively define the ML data specifications. To gain a deeper understanding of such a collaborative process, we conduct ten co-design sessions with ML software practitioners, educators, and students. In the sessions, teachers and students work with ML engineers, UX designers, and legal practitioners to define dataset characteristics for a given ML application. We find that stakeholders contextualize data based on their domain and procedural knowledge, proactively design data requirements to mitigate downstream harms and data reliability concerns, and exhibit role-based collaborative strategies and contribution patterns. Further, we find that beyond a seat at the table, meaningful stakeholder participation in ML requires structured supports: defined processes for continuous iteration and co-evaluation, shared contextual data quality standards, and information scaffolds for both technical and non-technical stakeholders to traverse expertise boundaries.

Real-World Data Quality Framework for Oncology Time to Treatment Discontinuation Use Case: Implementation and Evaluation Study.

The importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD). This study aims to implement real-world time to treatment discontinuation (rwTTD) for a systemic anticancer therapy (SACT) as a new use case for the Use Case Specific Relevance and Quality Assessment, a framework linking data quality and relevance in fit-for-purpose RWD assessment. To define the rwTTD use case, we mapped the operational definition of rwTTD to RWD elements commonly available from oncology electronic health record-derived data sets. We identified 20 tasks to check the completeness and plausibility of data elements concerning SACT use, line of therapy (LOT), death date, and length of follow-up. Using descriptive statistics, we illustrated how to implement the Use Case Specific Relevance and Quality Assessment on 2 oncology databases (Data sets A and B) to estimate the rwTTD of an SACT drug (target SACT) for patients with advanced head and neck cancer diagnosed on or after January 1, 2015. A total of 1200 (24.96%) of 4808 patients in Data set A and 237 (5.92%) of 4003 patients in Data set B received the target SACT, suggesting better relevance of the former in estimating the rwTTD of the target SACT. The 2 data sets differed with regard to the terminology used for SACT drugs, LOT format, and target SACT LOT distribution over time. Data set B appeared to have less complete SACT records, longer lags in incorporating the latest data, and incomplete mortality data, suggesting a lack of fitness for estimating rwTTD. The fit-for-purpose data quality assessment demonstrated substantial variability in the quality of the 2 real-world data sets. The data quality specifications applied for rwTTD estimation can be expanded to support a broad spectrum of oncology use cases.

A roadmap for improving data quality through standards for collaborative intelligence in human-robot applications.

Collaborative intelligence (CI) involves human-machine interactions and is deemed safety-critical because their reliable interactions are crucial in preventing severe injuries and environmental damage. As these applications become increasingly data-driven, the reliability of CI applications depends on the quality of data, shaping the system's ability to interpret and respond in diverse and often unpredictable environments. In this regard, it is important to adhere to data quality standards and guidelines, thus facilitating the advancement of these collaborative systems in industry. This study presents the challenges of data quality in CI applications within industrial environments, with two use cases that focus on the collection of data in Human-Robot Interaction (HRI). The first use case involves a framework for quantifying human and robot performance within the context of naturalistic robot learning, wherein humans teach robots using intuitive programming methods within the domain of HRI. The second use case presents real-time user state monitoring for adaptive multi-modal teleoperation, that allows for a dynamic adaptation of the system's interface, interaction modality and automation level based on user needs. The article proposes a hybrid standardization derived from established data quality-related ISO standards and addresses the unique challenges associated with multi-modal HRI data acquisition. The use cases presented in this study were carried out as part of an EU-funded project, Collaborative Intelligence for Safety-Critical Systems (CISC).

A Holistic Approach Towards Digitized Audit Procedures in Manufacturing for Data Quality Assessment

Digitized assessment of data quality in smart manufacturing facilitates the transformation towards efficient decision making in production environments. Inference can be drawn from the need of manufacturers, implementing robust and effective data analytics tools which emphasizes the demand for holistic data quality standards. Therefore, an interactive chatbot containing a comprehensive audit procedure incorporating a guideline aligned to ISO standards was developed. Using this tool, an applicable and structured audit process is offered to ensure data quality. This concludes in specific recommendations for manufacturers improving smart applications.

Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning

The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care. To discern what constitutes high-quality and useful data sets for health and biomedical ML research purposes according to subject matter experts. This qualitative study interviewed data set experts, particularly those who are creators and ML researchers. Semistructured interviews were conducted in English and remotely through a secure video conferencing platform between August 23, 2022, and January 5, 2023. A total of 93 experts were invited to participate. Twenty experts were enrolled and interviewed. Using purposive sampling, experts were affiliated with a diverse representation of 16 health data sets/databases across organizational sectors. Content analysis was used to evaluate survey information and thematic analysis was used to analyze interview data. Data set experts' perceptions on what makes data sets AI ready. Participants included 20 data set experts (11 [55%] men; mean [SD] age, 42 [11] years), of whom all were health data set creators, and 18 of the 20 were also ML researchers. Themes (3 main and 11 subthemes) were identified and integrated into an AI-readiness framework to show their association within the health data ecosystem. Participants partially determined the AI readiness of data sets using priority appraisal elements of accuracy, completeness, consistency, and fitness. Ethical acquisition and societal impact emerged as appraisal considerations in that participant samples have not been described to date in prior data quality frameworks. Factors that drive creation of high-quality health data sets and mitigate risks associated with data reuse in ML research were also relevant to AI readiness. The state of data availability, data quality standards, documentation, team science, and incentivization were associated with elements of AI readiness and the overall perception of data set usefulness. In this qualitative study of data set experts, participants contributed to the development of a grounded framework for AI data set quality. Data set AI readiness required the concerted appraisal of many elements and the balancing of transparency and ethical reflection against pragmatic constraints. The movement toward more reliable, relevant, and ethical AI and ML applications for patient care will inevitably require strategic updates to data set creation practices.

What ADS-B Data Can Tell Us About HL7525(KAL631) Accident at the CEBU Airport

This research aims to extract useful information from the HL7525 accident ADS-B data archive. The approach used is Exploratory Data Analysis (EDA), which ensures that the ADS-B data quality standards must be met and visualizes the landing position or touchdown, altitude, and speed data, which is then compared with the flight rules for the landing phase. These criteria are based on three important characteristics that ensure the aircraft does not overrun: touchdown point, touchdown speed, and deceleration after touchdown. Data analysis revealed that the stored ADS-B HL7525 data had good data quality, falling within the ICAO standard (tier-1) for 82% of the total data, meaning that this data was 82% representative of the actual environment. In addition, it was found that the aircraft attempted three landings, with the first and second attempts showing the go-around procedure, and the third attempt (the final attempt) landing successfully but ending in an accident. Based on the analysis results, two of the landing criteria were met even though the slope angle exceeded 30, and the third criterion was not met because the deceleration process after touchdown was not in accordance with the procedure.

National routine data for low birthweight and preterm births: Systematic data quality assessment for United Nations member states (2000-2020).

Low birthweight (<2500 g) and preterm birth (<37 weeks) are markers of newborn vulnerability. To facilitate informed decisions about investments in prevention and care, it is imperative to enhance data quality and use. Hence, the objective of this study is to systematically assess the quality of data concerning low birthweight and preterm births within routine administrative data sources. Systematic data quality assessment by adopting the WHO Data Quality Framework. National routine data system from UN member states. Livebirths. National routine administrative data on low birthweight and preterm births for 195 countries from 2000 to 2020 were systematically collated, totalling >700 million live births. The WHO data quality framework was adapted to undertake standardised data quality assessments. Availability, reporting quality, internal and external consistency of low birthweight and preterm data. Most United States Member States (64%: 124/195) had national data on low birthweight and (40%: 82/195) had data on preterm birth. Routine data system reporting was highest in North America, Australasia and Europe, where more than 95% live births had data on low birthweight and over 75% had data preterm births. In contrast,data reporting was lowest in sub-Saharan Africa (13% for low birthweight, 8% for preterm births) and Southern Asia (16% for low birthweight, 5% for preterm births). Most countries collect individual-level data; but, aggregate data reporting from hospital-based systems remain common in sub-Saharan Africa and Southern Asia. While data quality was generally high in North America, Australasia and Europe, gaps remain in the availability of gestational age metadata. Consistency between low birthweight and preterm rates were poor in Southern Asia and sub-Saharan Africa regions across time. There was high external consistency between low birthweight rates obtained from routine administrative data compared with low birthweight rates obtained from survey data for countries with highdata quality. Sub-Saharan Africa and South Asia countries have data gaps but also opportunities for rapid progress. Most births occure in facilities, electronic health information systems already include low birthweight, and adding accurate gestational age including with ultrasound assessment is becoming increasingly attainable. Moving toward the collection of individual level data would enable monitoring of quality of care and longer-term outcomes. This is crucial for every child and family and essential for measuring progress towards relevant sustainable development goals. The assessment will inform countries' actions for data quality improvement at national level and use of data for impact.

Digital transformation of the agricultural sector: prospects, challenges and solutions

The digital transformation of the agricultural sector has received considerable attention due to its potential to increase efficiency and productivity. This paper reviews recent research and publications to identify key themes and gaps in the digitalisation of agriculture. The analysis shows that while some studies highlight the potential benefits of digital agriculture, others emphasise the limitations and challenges associated with its implementation. The case of Ukraine fits into the ‘light digitalisation’ scenario, where agriculture digitalisation is at a basic level, data control is limited, and digital literacy and technology adoption varies among different agricultural stakeholders.To promote the digitalisation process, data exchange between stakeholders such as farmers, government, traders, producers, consumers and AgriTech should be improved. Digital platforms, e-governance, agricultural extension, access to financial services and information exchange are very important for digital transformation. The author explores digital interactions between farmers and agri-tech companies, food traders/producers and consumers, highlighting the elements and benefits of such interactions.The role of the government in creating a reliable digitalisation system and facilitating cooperation between stakeholders is discussed. It proposes the implementation of solutions such as farm management systems, data collection tools, decision support systems and data-driven farming technologies. These solutions facilitate data analysis, informed decision-making and optimisation of farm operations.Challenges and limitations associated with the digital transformation of the agricultural sector are limited financial resources, regulatory constraints, resistance to change, and privacy concerns. Further research is needed to explore data quality standards, data ownership and privacy rules, skills development, modelling systems and digital platforms.&#x0D; JEL classification: H83, O38, Q10, Q18

Characterising a mobile reference station (MoRS) to quantify personal exposure to air quality

There is increasing clinical, epidemiological, and toxicological evidence linking exposure to air pollution with multiple health outcomes that lead to increased mortality and morbidity. Traditionally, fixed air quality monitors have been used to provide ambient air pollution measurements, but they have spatial and temporal limitations. Rapid advances in instrument miniaturisation have made novel sensing technologies more accessible but these are prone to high sensitivity and inaccuracies. To bridge the gap between fixed monitors and small sensors we have developed a Mobile Reference Station (MoRS) – a portable platform delivering high quality measurements of air pollutants using smaller, low power reference grade instruments at high time resolutions. MoRS enables the simultaneous measurement of a broad aerosol size distribution (10 nm–35 μm), gaseous pollutant concentrations (nitrogen dioxide (NO2) and ozone (O3)), environmental parameters (noise, relatively humidity (RH) and temperature) as well as collecting filter samples for laboratory analysis. The MoRS instrumentation is described and the major challenges in ensuring that high data quality standards are maintained are discussed. Laboratory and field tests were used to derive scaling factors for all the MoRSinstrumentation. Field testing of MoRS showed excellent intercomparability against reference instrumentation (R2 > 0.98) and good agreement with reference instruments in the ultrafine aerosol range, although there was an overestimation of fine particle aerosols. Measurements taken during example mainline train and London Underground (LU) journeys are displayed showing the value of the high-quality data derived from MoRS and how this can help to disentangle multiple confounding environmental pollutants and enrich epidemiological studies.