Data Collection Challenges Research Articles

Emphasizing Sexual Orientation and Gender Identity Data Capture for Improved Cardiovascular Care of the LGBTQ+ Population

ImportanceThe rising self-identifying lesbian, gay, bisexual, transgender, and queer (LGBTQ+) population makes understanding the unique health care needs of sexual and gender minoritized patients an urgent one. The interaction between minority stress and cardiovascular disease has been well described among underrepresented minoritized populations. The underrepresentation of minoritized populations in clinical research is partly responsible for worse cardiovascular outcomes in these populations. The absence of sexual orientation and gender identity and expression (SOGIE) data makes it difficult to understand the cardiovascular health of LGBTQ+ adults, thereby widening health care disparities in this population. Advancing cardiovascular health equity for LGBTQ+ patients must begin with careful and accurate SOGIE data collection.ObservationsCurrent SOGIE data capture remains inadequate despite federal mandates. Challenges in data collection include political and regulatory discrimination, patient/practitioner hesitancy, lack of supportive guidance on SOGIE data collection, improper terminology, regulatory inertia, and inadequate and often incorrect integration of SOGIE data into electronic health records (EHRs). Additional challenges include grouping participants as “others” for statistical significance. The inclusion of SOGIE data has demonstrated an impact in other fields like cancer survivorship and surgery. The same needs to be done for cardiology.Conclusions and RelevancePotential solutions for improving much-needed SOGIE data collection include (1) implementing LGBTQ+ inclusive policies, (2) integrating SOGIE data into the EHR, (3) educating health care professionals on the relevance of SOGIE to patient-centered care, and (4) creating a diverse cardiovascular workforce. These steps can substantially enhance the ability to collect SOGIE data to address LGBTQ+ cardiovascular health care disparities.

Experiential Learning for Circular Operations Management in Higher Education

This research-to-practice article delves into novel learning experiences for operations management education, involving the circular economy and experiential learning. Higher Education academics are required to develop effective learning that actively and impactfully helps nurture in students the essential competency to face sustainable development demands. In operations management education, one possibility is to integrate real-world circular economy challenges into learning activities that address issues concerning solid waste generation in business processes and operations. This type of innovative learning experience involves both conceptual understanding and practical implementation. Accordingly, experiential learning is considered a suitable pedagogy for this purpose in this work because of its hands-on applications, critical thinking, and active engagement. To illustrate this proposition, this paper presents a case study concerning an operations management undergraduate course at a Mexican university. The case study indicates how to translate a situation of solid waste generation in a business into relevant disciplinary experiential learning. The results show that students regarded the learning experience as motivating, interesting, and relevant while widely accomplishing their learning objectives. However, limitations did exist regarding experiential learning, the methodological approach, data collection, and implementation challenges. Future work points to the need for further learning experiences and to improve research reliability, transferability, and validity.

Proceedings from the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference

We summarize here the proceedings of the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference, where experts and advocates gathered to advance research into applications of Artificial Intelligence (AI), Machine Learning (ML) and other computational tools in the diagnosis and management of Inborn Errors of Immunity (IEI). The conference focused on key themes such as expediting IEI diagnoses, challenges in data collection, roles of natural language processing (NLP) and large language models (LLMs) in interpreting electronic health records (EHRs), and ethical considerations in implementation. Innovative AI-based tools trained on EHRs and claims databases have discovered new patterns of warning signs for IEIs, facilitating faster diagnoses, and enhancing patient outcomes. Challenges persist in training AIs due to data limitations especially in rare diseases, overlapping phenotypes, and biases inherent in current datasets. Furthermore, experts highlighted the significance of ethical considerations, data protection, and the necessity for open science principles. The conference delved into regulatory frameworks, equity in access, and the imperative for collaborative efforts to overcome these obstacles and harness AI's transformative potential. Concerted efforts are still needed to successfully integrate AI into daily clinical immunology practice.

Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.

A Review of the Processes and Procedures of Road Traffic Accident Mortality Data Collection in Zambia

Road traffic crashes (RTCs) pose significant public health challenges globally, leading to millions of deaths, injuries and disabilities each year. Low- and middle-income countries, including Zambia, bear a disproportionate burden of RTC fatalities and injuries. However, the collection and accuracy of RTC data in Zambia are hampered by various challenges, including underreporting and inadequate data collection procedures. Strengthening RTC data collection systems is crucial to accurately understand the magnitude of the problem and implement effective measures to reduce RTC fatalities and injuries. This article presents a review of RTC mortality data collection processes and procedures in Zambia, focusing on the key stakeholders involved, namely the Zambia Police Service, Health Care Facilities, and the Department of National Registration, Passports and Citizenship. The study identifies key challenges such as paper-based records, inadequate training, resource limitations, varying case definitions, and low death registration rates. The research emphasizes the need to strengthen data systems and improve coordination among stakeholders. Recommendations include establishing a multidisciplinary road crash data analysis team, utilizing technology for data collection and analysis, providing training and capacity building, conducting public awareness campaigns, and enhancing collaboration between the police, hospitals and Civil Registration and Vital Statistics (CRVS). Implementing these recommendations is crucial to improve the accuracy and completeness of RTC mortality data, leading to evidence-based road safety interventions and policies. This research contributes to the understanding of RTC data collection practices in Zambia and provides valuable insights for policymakers, researchers, and practitioners in addressing the challenges in RTC mortality data collection.

Patterns of vehicle lights: Addressing complexities of camera-based vehicle light datasets and metrics

This paper explores the representation of vehicle lights in computer vision and its implications for various pattern recognition tasks in autonomous driving. Different representations for vehicle lights, including bounding boxes, center points, corner points, and segmentation masks, are discussed in terms of their strengths and weaknesses toward a variety of domain tasks, as well as associated data collection and annotation challenges. This leads to the introduction of the LISA Vehicle Lights Dataset, providing light annotations related to position, state, color, and signal, specifically designed for downstream applications in vehicle detection, intent and trajectory prediction, and safe path planning. A comparison of existing vehicle light datasets is provided, highlighting the unique features and limitations of each dataset. Because occlusions from vehicle pose and passing objects can limit camera observation, we introduce a group of Light Visibility neural networks, which take as input a detected vehicle image and return as output whether a corresponding vehicle light is present in the image. This is especially important for the evaluation of light localizations, states, and signals, as systems decisions should account for differences of lights being in unknown states due to occlusion versus model uncertainty. We show that our trained Light Visibility Models achieve over 90% accuracy on each of the four light classes. Our dataset and model are made available at https://cvrr.ucsd.edu/vehicle-lights-dataset.

Modelling the effect of macroeconomic factors on residential real estate investment returns in Abuja and Lagos, Nigeria

PurposeThis study evaluated the effect of macroeconomic factors on residential real estate (RE) investment returns in the cities of Abuja and Lagos, Nigeria, with a view to guiding RE investors and researchers.Design/methodology/approachA survey research design was employed using a questionnaire to collect RE transaction data from 2008 to 2022 from estate surveying and valuation firms in the study areas. Rental and capital value data collected were used to construct rental and capital value indices and total returns on investment. The macroeconomic data used were retrieved from the archives of the Central Bank of Nigeria (CBN). Granger causality (GC) and multiple regression models were adopted to evaluate the effect of selected macroeconomic variables on residential RE investment returns in the study areas.FindingsThe study found a progressive upward movement in rental and capital values of residential RE investment in the study areas within the study period. Total and risk-adjusted returns on investment were equally positive within the study period. Only the inflation rate, unemployment rate and real gross domestic product (GDP) per capita were found to be the major determinants of residential RE investment returns in the study areas within the study period.Research limitations/implicationsThe secrecy associated with property transaction information/data by RE practitioners in the study areas posed a challenge. Property transaction data were not adequately kept in a way for easier access and retrieval in many of the estate firms and agent offices. Consequently, there was a lack of data that spanned the study period in some of the sampled estate firms or agent offices. This data collection challenge was, however, overcome by the excess time spent retrieving the required data for this study to ensure that the findings appropriately answer the research questions. Practical implicationsInflation and GDP per capita have been found to be significant factors that influence residential RE investment performance in the study areas. Therefore, investors should pay attention to these identified macroeconomic factors for residential RE investment in the study areas whilst making investment decisions in order to mitigate a possible loss of income or return. The government should formulate and implement economic policies that would address the current high unemployment and inflation rates in Nigeria at large.Originality/valueThis study has extended and further enriched the existing body of knowledge in the field of RE investment analysis in Nigeria. To the best of the authors' knowledge, this study is the first to adopt the Cornish Fisher value-at-risk and modified Sharpe ratio models to analyse risk and risk-adjusted returns on residential RE investment, respectively, in Nigeria. It has therefore redirected the focus of RE researchers and practitioners to a more objective approach to RE investment performance analysis in Nigeria.

Current issues in doctoral supervision practice in the UK

ABSTRACT Doctoral study remains the most popular form of research degree globally. The student and supervisor relationship is an integral part of the postgraduate researcher (PGR) experience. Supervisory relationships can be complex and multi-faceted, with differing expectations from both supervisor and PGR. However, comparing different perspectives of the PGR supervisory journey is relatively unexplored. This study aimed to explore how both PGRs and supervisors perceive the evolution of supervisory relationships throughout the doctoral journey. Employing a mixed methods design, the findings indicate that there is a transition in the dynamics of the relationship between the supervisor and the student which changes during this process. The role of the supervisor moves from being a more knowledgeable other operating in an advisory capacity, described by one participant as a ‘balance of expertise’ that ‘swings’ as the relationship evolves. Drawing upon Jindal-Snape’s multiple and multi-dimensional transitional theory, the results suggest that managing expectations, supporting the transition to doctoral study, the role of peer support for PGRs and supervisors are fundamental to a positive supervisory experience. The global pandemic affected the supervisory relationship and research journey posing some challenges for data collection; however, it facilitated opportunities to adapt and evolve supervisory practice.

"Trigger Warning: This Study Contains Extremist Content." Research Strategies for Investigations of Online Extremism and Terrorism

Scholars studying online extremism and terrorism face major challenges, including finding safe access to hostile environments where members evade law enforcement. Protective measures, such as research ethics, often overlook the safety of investigators. Investigators, including Open-Source Intelligence (OSINT) analysts, encounter emotional harm, abuse from ideologues, consent issues, and legal challenges in data collection. Despite rising awareness of these challenges, scholars lack guidance on starting and navigating research in these areas. This paper identifies challenges and offers strategies for safely, ethically, and legally researching in this environment.

REDCap as an accreditation tool for academic programs in clinical research: A case study

Abstract The Master of Science in Clinical Research Management program at Rutgers Biomedical Health Sciences underwent significant restructuring aligned with the Clinical and Translational Science Award funding parameters. This evolution necessitated formal evaluation through accreditation by the Commission on Accreditation of Allied Health Education Programs. The years-long accreditation process posed challenges, particularly regarding the collection of course learning outcomes data aligned with accreditation competency standards. The objective of this special communication is to report the rationale behind pursuing accreditation for clinical research degrees, the data collection challenges during the accreditation process, and a potential solution. In order to address existing university metric data gaps, Research Electronic Data Capture (REDCap) software was used to develop a data collection tool that streamlined the accreditation process and reduced the administrative burden. REDCap was effective in allowing faculty to self-report 3 years of course outcomes data for accreditation. There was an elevated level of user satisfaction compared to alternative data collection methods. A SWOT analysis identified the strengths and weaknesses of using REDCap, emphasizing strengths in functionality that include customizability, data validation, and compliance with regulatory standards. Overall, the advantages of leveraging REDCap for accreditation data collection, including customization, data security, and user-friendliness outweigh the key disadvantage of REDCap, which is its limited reporting capabilities.

Enhancing Property Valuation in Ruwa, Harare, Zimbabwe, using Kobo Collect and GIS

The escalating urbanization of Ruwa, a burgeoning suburb on the outskirts of Harare, Zimbabwe, has catalyzed an exigent demand for precise property valuation. Traditional valuation methods have struggled amidst the challenges of manual data collection, data inaccuracies, and protracted analyses. To address these constraints, a pioneering solution emerged at the crossroads of modern data collection and geospatial analysis. The fusion of Kobo Collect and Geographic Information Systems (GIS) presents a transformative avenue to surmount these challenges. The conventional practices faced hurdles marked by manual data collection's limitations. Inherent subjectivity and potential errors from pen-and-paper surveys posed challenges to data integrity. Moreover, as Ruwa underwent rapid changes, the inability to update data in real-time diminished valuation accuracy. Slow analysis processes also constrained timely access to property valuation insights. The integration of Kobo Collect and GIS provides a solution aligned with Ruwa's urban evolution. Kobo Collect's agile data collection forms, accessible on mobile devices, amplify data accuracy and practicality. The assimilation of GIS adds a spatial layer, and geotagging properties, and facilitates spatial analyses, unraveling intricate interplays of property values and geographic context. The outcomes encompass heightened data accuracy, as digital data entry minimizes errors. Efficiency gains result from real-time data entry and expedited analyses. Spatial insights from GIS analysis divulge patterns influencing property values. Stakeholders, empowered with informed decision-making capabilities, envision strategic urban planning and property development. The endeavor was not without challenges. Technical training ensured optimal tool utilization, while data privacy measures secured sensitive information.

How can Educational Psychologists evaluate group supervision to ensure positive impact for supervisees’ development and skills, and for the children and families for whom they work?

AimThis review investigated how group supervision within education or social care settings has been evaluated, to identify tools for EP practice that ensure good outcomes for supervisees and the children and families they support.MethodA systematic literature search was conducted using databases PsycINFO, Web of Science and ERIC. Twelve studies identified using the inclusion/exclusion criteria involved a range of research designs.FindingsThe selected studies suggest group supervision can benefit supervisees in terms of wellbeing, on direct practice with families, and on team relationships. The most innovative and revealing studies were small-scale and conducted in the context of social work. These highlighted the importance of having a ‘clinician’ involved in facilitation for practice change, and group supervision characterised as ‘practice-focused’ was found to improve parental engagement and goal agreement.LimitationsThere is a paucity and lack of breadth in methodological approaches in evaluating the impact of supervision, and challenges in data collection. Further research should incorporate triangulation of information (i.e. supervisee, supervisor, and client).ConclusionsEvaluation tools relevant for EP use include making use of data sources such as supervision records, using a coding framework for self-evaluation and using questionnaires to assess how supervision has achieved the intended outcomes for supervisees. A subsequent follow-up of supervisee’s intended actions to identify practice change, should include gathering feedback directly from children and families.

Collecting Multi-country Retrospective Antimicrobial Consumption and Use Data: Challenges and Experience.

Excessive or inappropriate antimicrobial use contributes to antimicrobial resistance, emphasizing the need to monitor and document the types and quantities of antibiotics used. Thus, data on antimicrobial consumption (AMC) and antimicrobial usage (AMU) are key in informing and promoting judicious use. Our study, conducted during 2019-2023, as part of the CAPTURA project, aimed to understand the state of data availability and quality for AMC and AMU monitoring in Asia. In this article, we describe the challenges and opportunities faced and provide examples of AMU and AMC analysis. World Health Organization (WHO) and country-tailored methodologies and tools were applied to collect retrospective data from 2016 to 2019 in Bangladesh, Bhutan, Laos, Nepal, Pakistan, Papua New Guinea, Sri Lanka, and Timor-Leste. The primary indicator for national AMC was total level of consumption, expressed as total defined daily doses (DDD) per 1000 inhabitants per day for the year or period of data collected. For facility AMC and AMU, the primary indicator was total DDD per admissions per day for the year or period of data collected. Although many countries faced infrastructural challenges in data collection and storage, we managed to collect and analyze AMC data from 6 countries and AMU data from 5. The primary indicators, and additional findings, were visualized to facilitate dissemination and promote the development of action plans. Looking ahead, it is crucial that future initiatives empower each country to establish surveillance infrastructures tailored to their unique contexts, ensuring sustainable progress in the fight against antimicrobial resistance.

Unraveling the Potential of Immersive Virtual Environments for Behavior Mapping in the Built Environment: A Mapping Review

Introduction/Purpose. Behavior mapping is a crucial practice to capture precise data on human activities. Over the years, technological advancements have improved reliable data collection over the conventional “paper and pen”-based methods. Likewise, researchers increasingly utilize IVE (immersive virtual environment) technology in the built environment for its experiential capabilities. The aim of this research is to comprehensively investigate the most recent research and technological trends, data collection methods, and challenges and opportunities associated with IVE adoption for behavior mapping. Design/Methodology/Approach. This study investigates the current status of behavior mapping in the built environment through a mixed qualitative–quantitative study. A systematic background review was conducted on 353 articles, and a bibliometric analysis was performed on 123 journal articles retrieved from Scopus. The analysis generated science maps, unveiling technological advancements, data types, collection methods, research trends, and potential directions to address the research questions at hand. Findings. A thorough qualitative review presented deeper insight into major transitions and technological contributions, whereas the quantitative study revealed current research trends and future directions. Furthermore, it discussed the contribution, opportunities, and challenges of IVE technology for data collection. Research Limitations. The review and bibliometric study findings are circumscribed by literature data retrieved from Scopus data only, and potential research directions, opportunities, and obstacles are proposed based on analysis of review results, which might cause an incomprehensive nature. Practical Implications. This study highlights the importance of behavioral mapping research and the potential for technology to assist it. The paper also identifies current shortcomings and constraints in the literature, emphasizing the need for interventions in IVE technology.

HTA342 Real World Evidence in German HTA: The Challenges of Comparative Routine Practice Data Collection (ABD) for Early Benefit Assessment (AMNOG)

HTA342 Real World Evidence in German HTA: The Challenges of Comparative Routine Practice Data Collection (ABD) for Early Benefit Assessment (AMNOG)

Aquaculture in Algeria: Current Status, Analysis, and Considerations for Commercial Development

Aquaculture in Algeria has developed significantly since the 1920s, primarily focusing on oyster and mussel farming. Establishing the Ministry of Fisheries and Marine Resources (MPRH) in 1999 marked a crucial milestone for the industry. Despite challenges in data collection, Algerian aquaculture production reached only 4,779.29 tonnes in 2021, showing a mere 0.2% of African production and 0.0044% of world aquaculture production. More than half of production comes from marine fish cultured in private-sector sea cages. However, shrimp production is almost null. The taxonomic composition of Algerian aquaculture is diverse, with marine and freshwater fish species playing prominent roles. Despite these challenges, the aquaculture field in Algeria has demonstrated an impressive annual growth rate of 12.49% and receives continuous government support. Nevertheless, limited infrastructure, a shortage of expertise, and the need for further research pose significant obstacles. Efforts are underway to integrate aquaculture with agriculture in Saharan zones, further facilitated by governmental support. Overcoming challenges and promoting sustainability through strengthening research capacities, fostering collaboration, and providing financial support is crucial for the advancement of industry. By addressing challenges and embracing sustainability, the aquaculture sector in Algeria can unlock its full potential, ensuring food security, economic growth, and environmental sustainability.

Comprehensive Simulation Framework for Space-Air-Ground Integrated Network Propagation Channel Research.

The space-air-ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.

Real-Time Monitoring of Animals and Environment in Broiler Precision Farming—How Robust Is the Data Quality?

Increasing digitalization in animal farming, commonly addressed as Precision Livestock Farming (PLF), offers benefits in terms of productivity, sustainability, reduced labor and improved monitoring of animal welfare. However, the large amounts of collected data must be stored, processed and evaluated in a proper way. In practice, challenges of continuous and exact data collection can arise, e.g., from air pollutants like dust occluding cameras and sensors, degrading material, the ever-present commotion caused by animals, workers and machines, regularly required maintenance or weak signal transmission. In this study, we analyzed the quality of multi-source spatio-temporal data from a broiler house with 8100 birds over a period of 31 months collected by the Farmer Assistant System (FAS). This is a ceiling-suspended robot equipped with several sensors and cameras that continuously collect data while moving through the barn. The data analysis revealed numerous irregularities: missing values, outliers, repetitive measurements, systematic errors, and temporal and spatial inconsistencies. About 40–50% of all records collected with the early version of the FAS had to be sorted out. The newer version of FAS provided cleaner data, although still about 10–20% of the data had to be removed. Our study has shown that where sophisticated technological systems meet a challenging environment, a thorough and critical review of data completeness and quality is crucial to avoid misinterpretation. The pipeline developed here is designed to help developers and farmers detect failures in signal processing and localize the problem in the hardware components. Scientists, industrial developers and farmers should work more closely together to develop new PLF technologies to more easily advance digitization in agriculture.

"not one size fits all” The challenges of measuring paediatric health-related quality of life and the potential role of digital ecological momentary assessment: a qualitative study

PurposeTo explore the views of clinicians and researchers about the challenges of measuring health-related quality of life (HRQoL) in children (5–11 years) and to explore whether digital ecological momentary assessment (EMA) could enhance HRQoL measurement.MethodsSemi-structured qualitative interviews with 18 professionals (10 academics/researchers, four clinicians, four with both professional backgrounds) experienced in child HRQoL measurement. We analysed data thematically.ResultsTheme One describes the uncertainty around conceptualising HRQoL for children and which domains to include; the greater immediacy and sensitivity of children’s reflections on their HRQoL, leading to high variability of the construct; and the wide individual differences across childhood, incongruent with fixed HRQoL measures. Theme Two describes the challenges of proxy reporting, questioning whether proxies can meaningfully report a child’s HRQoL and reflecting on discrepancies between child and proxy reporting. Theme Three covers the challenge of interpreting change in HRQoL over time; does a change in HRQoL reflect a change in health, or does this reflect developmental changes in how children report HRQoL. Theme Four discusses digital EMA for HRQoL data capture. In-the-moment, repeated measurement could provide rich data and address challenges of recall, ecological validity and variability; passive data could provide objective markers to supplement subjective responses; and technology could enable personalisation and child-centred design. However, participants also raised methodological, practical and ethical challenges of digital approaches.ConclusionDigital EMA may address some of the challenges of HRQoL data collection with children. We conclude by discussing potential future research to explore and develop this approach.

Insights into Electrocatalyst Transformations Studied in Real Time with Electrochemical Liquid-Phase Transmission Electron Microscopy.

ConspectusThe value of operando and in situ characterization methodologies for understanding electrochemical systems under operation can be inferred from the upsurge of studies that have reported mechanistic insights into electrocatalytic processes based on such measurements. Despite the widespread availability of performing dynamic experiments nowadays, these techniques are in their infancy because the complexity of the experimental design and the collection and analysis of data remain challenging, effectively necessitating future developments. It is also due to their extensive use that a dedicated modus operandi for acquiring dynamic electrocatalytic information is imperative. In this Account, we focus on the work of our laboratory on electrochemical liquid-phase transmission electron microscopy (ec-LPTEM) to understand the transformation/activation of state-of-the-art nanocatalysts for the oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and CO2 electroreduction (CO2ER). We begin by describing the development of electrochemical microcells for TEM studies, highlighting the importance of tailoring the system to each electrochemical process to obtain reliable results. Starting with the anodic OER for alkaline electrolyzers, we demonstrate the capability of real-time monitoring of the electrowetting behavior of Co-based oxide catalysts and detail the fascinating insights gained into solid-liquid interfaces for the reversible surface reconstruction of the catalystic surfaces and their degradation processes. Importantly, in the case of the OER, we report the exceptional capacity of ec-LPTEM to probe gaseous products and therefore resolve solid-liquid-gas phenomena. Moving toward the cathodic ORR for fuel cells, we summarize studies that pertain to the evaluation of the degradation mechanisms of Pt nanoparticles and discuss the issues with performing real-time measurements on realistic catalyst layers that are composed of the carbon support, ionomer network, and Pt nanocatalysts. For the most cathodic CO2ER, we first discuss the challenges of spatiotemporal data collection in microcells under these negative potentials. We then show that control over the electrochemical stimuli is critical for determining the mechanism of restructuring/dissolution of Cu nanospheres, either for focusing on the first stages of the reaction or for start/stop operation studies. Finally, we close this Account with the possible evolution in the way we visualize electrochemical processes with ec-LPTEM and emphasize the need for studies that bridge the scales with the ultimate goal of fully evaluating the impact of the insights obtained from the in situ-monitored processes on the operability of electrocatalytic devices.