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Haplotype estimation, or phasing, has gained significant traction in large-scale projects due to its valuable contributions to population genetics, variant analysis, and the creation of reference panels for imputation and phasing of new samples. To scale with the growing number of samples, haplotype estimation methods designed for population scale rely on highly optimized statistical models to phase genotype data, and usually ignore read-level information. Statistical methods excel in resolving common variants, however, they still struggle at rare variants due to the lack of statistical information. In this study we introduce SAPPHIRE, a new method that leverages whole-genome sequencing data to enhance the precision of haplotype calls produced by statistical phasing. SAPPHIRE achieves this by refining haplotype estimates through the realignment of sequencing reads, particularly targeting low-confidence phase calls. Our findings demonstrate that SAPPHIRE significantly enhances the accuracy of haplotypes obtained from state of the art methods and also provides the subset of phase calls that are validated by sequencing reads. Finally, we show that our method scales to large data sets by its successful application to the extensive 3.6 Petabytes of sequencing data of the last UK Biobank 200,031 sample release. Author summary: Haplotype estimation, also known as phasing, is now applied to population scale projects, typically of hundreds of thousands of samples to millions of samples. Generally phasing relies on statistical methods as they provide very accurate results for common variations. However, for rare and very rare variants the lack of statistical power often results in poor phasing. The large amount of rare variations discovered with whole-genome sequencing as well as the number of samples makes it expensive to process. We have developed the SAPPHIRE method that leverages whole-genome sequencing data to verify and correct the phase at poorly phased variant loci. It does so by finding sequencing reads that contain both the poorly phased variant and an accurately phased common variant. SAPPHIRE scales with large data sets by specifically targeting variation where statistical phasing performed poorly, therefore it reduces the quantity of sequencing data to be processed and combines the advantages of both read-based and statistical approaches. We show the efficiency of SAPPHIRE by improving the estimated haplotypes for 200,031 samples in the UK Biobank. SAPPHIRE is free and available as open-source software. [ABSTRACT FROM AUTHOR]

Background: Usage of electronic gaming machines (EGMs) and on‐line gambling is strongly associated with gambling‐related harm. Player‐tracking systems can monitor a gambler's activity across multiple sessions and/or operators, providing a clearer picture of the person's risk of harm with respect to these gambling formats and enabling harm reduction efforts. The Finnish and Norwegian state monopolies have player‐tracking systems in place, while the United Kingdom is implementing an operator‐led system called 'single customer view' for on‐line gambling, and Australian states are proposing similar 'player cards' for land‐based EGMs. Argument: Player tracking can advance harm reduction efforts in three ways. First, player tracking improves our understanding of gambling‐related harm by providing data on how the population gambles, which can potentially be linked with operator, government and/or prevalence data sets. Secondly, player tracking can be used to implement harm reduction measures such as expenditure limits, self‐exclusion and age verification. Thirdly, player tracking can provide a platform to evaluate harm reduction measures via gold‐standard field trials. These potential benefits need to be weighed against various practical and ethical issues. Conclusions: The potential benefits of player‐tracking systems would be maximized via systems administered independently of the gambling industry and implemented universally across all gambling in a given jurisdiction. [ABSTRACT FROM AUTHOR]

The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and job creation. Data science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services. Businesses can also harness the power of artificial intelligence (AI) and big data, and the smart use of digital technologies to enhance productivity and performance, paving the way for innovation. However, integrating data science decisions into an SME requires both skills and IT investments. In most cases, such expenses are beyond the means of SMEs due to their limited resources and restricted access to financing. This paper presents trends and challenges towards effective data-driven decision making for organisations based on a 3-year long study which covered more than 85 UK SMEs, mostly from the West Midlands region of England. In particular, this study attempts to find answers to several key research questions around data science and AI adoption among UK SMEs, and the advantages of digitalisation and data-driven decision making, as well as the challenges hindering their effective utilisation of these technologies. We also present two case studies that demonstrate the potential of digitisation and data science, and use these as examples to unveil challenges and showcase the wealth of currently available opportunities for SMEs. [ABSTRACT FROM AUTHOR]

Background: The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain. Methods: Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot). Results: Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0–66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4–70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI. Conclusions: There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them. [ABSTRACT FROM AUTHOR]

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene–Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene–environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB. [ABSTRACT FROM AUTHOR]

In this paper we make a case for 'Little Data', which is real-time, self-collected, idiosyncratic datasets maintained by individuals about themselves on myriad topics. We develop and offer a methodology for combining these messy, highly personal insights, to make deductive observations about collective practices. In testing this approach, we use the case study of the 2020–21 stay-at-home orders imposed in the U.S.A., U.K., and Western Europe during the Coronavirus pandemic to operationalise and demonstrate the applicability of this method. Our main finding is to show that whilst stay-at-home orders did have a significant impact on habits during the COVID-19 pandemic, these changes were often counterintuitive, of an insightful nature on topics that would otherwise not be investigated, and always short-lived. Our main contribution is to present Little Data, despite and because of its fragmented and disparate nature, as a viable and useful tool to understand personal habits at finite junctures. [ABSTRACT FROM AUTHOR]

This paper first examines the UK Department for Transport's top-level planning for transport big data and UK rail data access, including the Department's proposed directions for big data applications, transport data sets and data fusion. It then summarizes such information as data statistics, Data Portal Services and data application developed and provided to the public by UK's rail regulator Office of Rail and Road and UK's rail operator Network Rail. It finally puts forward suggestions on the application and development of the big data in China's rail transport industry from the perspective of building the rail transport data set, constructing the data portal for rail transport, and promoting the opening sharing of relevant data. [ABSTRACT FROM AUTHOR]

This study exploits service modularity in front‐end logistics services in e‐fulfillment, from a customer‐centric approach, particularly in order management, delivery, and return. Through an online survey of UK customers, the service priorities of 494 respondents via AHP (Analytic Hierarchical Process) were analyzed. Extracting customers' service priorities, ordering behavior, and demographic information as input data, the clustering algorithm KAMILA (KAy‐means for MIxed LArge data sets) was further applied. The three identified customer clusters (multichannel shoppers, infrequent shoppers, and online fans) provide preliminary evidence on how commonality and variability aspects of service modularity in front‐end logistics services can optimize the number of service options and their performance levels. Therefore, our study, building on value co‐creation and modularity, proposes a systematic way of exploiting service modularity for the customer segmentation process that addresses heterogeneous customer preferences cost‐efficiently and uncomplicatedly. Furthermore, we provide a framework for the governance of front‐end logistics services, guiding outsourcing decisions. Accordingly, it reveals the implications of customer priorities and service decomposition logic choices on value creation. Finally, the propositions formulated aim to develop theoretical foundations for explaining how the heterogeneity in customer priorities for logistics services can be managed with modularity, creating value both for customers and retailers. [ABSTRACT FROM AUTHOR]

Physical activity has been found to potentially modulate glaucoma risk, but the evidence remains inconclusive. The increasing use of wearable physical activity trackers may provide longitudinal and granular data suitable to address this issue, but little is known regarding the characteristics and availability of these data sources. We performed a scoping review and query of data sources on the availability of wearable physical activity data for glaucoma patients. Literature databases (PubMed and MEDLINE) were reviewed with search terms consisting of those related to physical activity trackers and those related to glaucoma, and we evaluated results at the intersection of these two groups. Biomedical databases were also reviewed, for which we completed database queries. We identified eight data sources containing physical activity tracking data for glaucoma, with two being large national databases (UK BioBank and All of Us) and six from individual journal articles providing participant-level information. The number of glaucoma patients with physical activity tracking data available, types of glaucoma-related data, fitness devices utilized, and diversity of participants varied across all sources. Overall, there were limited analyses of these data, suggesting the need for additional research to further investigate how physical activity may alter glaucoma risk. [ABSTRACT FROM AUTHOR]

Background: To determine the extent and nature of changes associated with COVID-19 infection in terms of healthcare utilisation, this study observed healthcare contact 1 to 4 and 5 to 24 weeks following a COVID-19 diagnosis compared to propensity-matched controls. Methods: Two hundred forty nine thousand three hundred ninety Welsh individuals with a positive reverse transcription–polymerase chain reaction (RT-PCR) test were identified from data from national PCR test results. After elimination criteria, 98,600 positive individuals were matched to test negative and never tested controls using propensity matching. Cohorts were split on test location. Tests could be taken in either the hospital or community. Controls were those who had tested negative in their respective environments. Survival analysis was utilised for first clinical outcomes which are grouped into primary and secondary. Primary outcomes include post-viral-illness and fatigue as an indication of long-COVID. Secondary outcomes include clinical terminology concepts for embolism, respiratory conditions, mental health conditions, fit notes, or hospital attendance. Increased instantaneous risk for positive individuals was quantified using hazard ratios (HR) from Cox regression, while absolute risk (AR) and relative risk were quantified using life table analysis. Results: Analysis was conducted using all individuals and stratified by test location. Cases are compared to controls from the same test location. Fatigue (HR: 1.77, 95% CI: 1.34–2.25, p = < 0.001) and embolism (HR: 1.50, 95% CI: 1.15–1.97, p = 0.003) were more likely to occur in all positive individuals in the first 4 weeks; however, anxiety and depression (HR: 0.83, 95% CI: 0.73–0.95, p = 0.007) were less likely. Positive individuals continued to be more at risk of fatigue (HR: 1.47, 95% CI: 1.24–1.75, p = < 0.001) and embolism (HR: 1.51, 95% CI: 1.13–2.02, p = 0.005) after 4 weeks. All positive individuals are also at greater risk of post-viral illness (HR: 4.57, 95% CI: 1.77–11.80, p = 0.002). Despite statistical association between testing positive and several conditions, life table analysis shows that only a small minority of the study population were affected. Conclusions: Community COVID-19 disease is associated with increased risks of post-viral-illness, fatigue, embolism, and respiratory conditions. Despite elevated risks, the absolute healthcare burden is low. Subsequently, either very small proportions of people experience adverse outcomes following COVID-19 or they are not presenting to healthcare. [ABSTRACT FROM AUTHOR]

This article contributes to the international comparative analysis of university venture capital (UVC), providing a quasi-experimental design for follow-up research and practice. The US venture capital industry, with its unicorn focused high-growth format opened up a venture capital gap. UVC transmutes academic innovation into high-tech firms, industries and regional renewal, filling interstitial funding gaps among angel, public and private venture capital offers. It is a knowledge-based industrial policy by Another Name, with direct/explicit and indirect/implicit versions, on a continuum with variation depending upon shifting ideological and competitive concerns. Beyond as of right "womb" provision, UVC capstones an academic innovation ecosystem of technology transfer, incubation and acceleration, translational research, proof of concept funds and entrepreneurship education. Venture capital, exemplified by Sand Hill Road, de-emphasizes classic regional development objectives, neglecting appropriability conditions such as academic and regional circumstances that UK and China prioritize. More modest firm formation outcomes are dismissed as failures, with entrepreneurs encouraged to return to the entrepreneurial churn. We examine the origin and development of UVC from macro, meso and micro, historical and comparative perspectives. Multi-method/multi-sample, comparative case study, and big data analytics show the constraint, variety, and early affinity of UVC to academic icons with significant untapped potential to inspire widespread economic and social advance. [ABSTRACT FROM AUTHOR]

This study explores the nexus between digital and green transformations—the so-called "twin" transition—in European regions in an effort to identify the impact of digital and environmental technologies on the greenhouse gas (GHG) emissions originating from industrial production. We conduct an empirical analysis based on an original dataset that combines information on environmental and digital patent applications with information on GHG emissions from highly polluting plants for the period 2007–2016 at the metropolitan region level in the European Union and the UK. Results show that the local development of environmental technologies reduces GHG emissions, while the local development of digital technologies increases them, albeit in the latter case different technologies seem to have different impacts on the environment, with big data and computing infrastructures being the most detrimental. We also find differential impacts across regions depending on local endowment levels of the respective technologies: the beneficial effect of environmental technologies is stronger in regions with large digital technology endowments and, conversely, the detrimental effect of digital technologies is weaker in regions with large green technology endowments. Policy actions promoting the "twin" transition should take this evidence into account, in light of the potential downside of the digital transformation when not combined with the green transformation. [ABSTRACT FROM AUTHOR]

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships ("footprints") for use in applications such as greenhouse gas (GHG) flux inversions. Because a single model simulation is required for each data point, LPDMs do not scale well to applications with large data sets such as flux inversions using satellite observations. Here, we develop a proof-of-concept machine learning emulator for LPDM footprints over a ∼ 350 km × 230 km region around an observation point, and test it for a range of in situ measurement sites from around the world. As opposed to previous approaches to footprint approximation, it does not require the interpolation or smoothing of footprints produced by the LPDM. Instead, the footprint is emulated entirely from meteorological inputs. This is achieved by independently emulating the footprint magnitude at each grid cell in the domain using gradient-boosted regression trees with a selection of meteorological variables as inputs. The emulator is trained based on footprints from the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) for 2014 and 2015, and the emulated footprints are evaluated against hourly NAME output from 2016 and 2020. When compared to CH 4 concentration time series generated by NAME, we show that our emulator achieves a mean R -squared score of 0.69 across all sites investigated between 2016 and 2020. The emulator can predict a footprint in around 10 ms, compared to around 10 min for the 3D simulator. This simple and interpretable proof-of-concept emulator demonstrates the potential of machine learning for LPDM emulation. [ABSTRACT FROM AUTHOR]

Genomic regions under positive selection harbor variation linked for example to adaptation. Most tools for detecting positively selected variants have computational resource requirements rendering them impractical on population genomic datasets with hundreds of thousands of individuals or more. We have developed and implemented an efficient haplotype-based approach able to scan large datasets and accurately detect positive selection. We achieve this by combining a pattern matching approach based on the positional Burrows–Wheeler transform with model-based inference which only requires the evaluation of closed-form expressions. We evaluate our approach with simulations, and find it to be both sensitive and specific. The computational resource requirements quantified using UK Biobank data indicate that our implementation is scalable to population genomic datasets with millions of individuals. Our approach may serve as an algorithmic blueprint for the era of "big data" genomics: a combinatorial core coupled with statistical inference in closed form. [ABSTRACT FROM AUTHOR]

This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting. [ABSTRACT FROM AUTHOR]

This study aims to explore the role of small and medium-sized enterprises (SMEs) in developing data-driven solutions to address the direct and indirect challenges posed by COVID-19. A sample of six case studies of SMEs from the UK and Portugal were selected to explore in-depth the experience of these companies in proposing innovative solutions in the pandemic context. The findings reveal that the pandemic caused amplifying effects on the digitalization of organizations and the emergence of data-driven solutions. However, the development of a data-driven approach involves not only technologies but also the digitalization of processes and highly skilled human resources. The pandemic was also a catalyst for the emergence of collaborative initiatives that have enabled the development of solutions involving diverse players from science, business, and civilian society. This study offers innovative contributions by focusing exclusively on companies developing data-driven solutions supported by technologies such as the internet of things (IoT), big data, and artificial intelligence. [ABSTRACT FROM AUTHOR]

In democratic societies, the political process ultimately is about making value judgements. Comparative cultural policy research indicates different value orientations may be behind cultural policies of different nations. It has also indicated developments that are common, such as democratisation of culture, neoliberalisation of politics and globalisation. As of yet, such value changes have not been studied at the level of cultural policy documents analysed as text. This article proposes a methodology to study value changes behind cultural policies over time and across nations. Based in the "pragmatic sociology" of Boltanski, Thévenot, and Chiapello, the article presents a big data methodology to uncover value changes of policy documents over time, between different policy agents, and across nations using material from the UK and the Netherlands. The paper explains the methodology, discusses the implications of the preliminary outcomes and provides suggestions for further comparative policy analyses. [ABSTRACT FROM AUTHOR]

Financial transactions are increasingly used by consumer apps and financial service providers to estimate consumption-based carbon emissions. This approach promises a low-resource, ultra-fast, and highly scalable approach to measuring emissions at different levels of potential policy intervention--spanning the national, subnational, local, and individual level. Despite this potential, there is a lack of research exploring the validity of this approach to carbon profiling. Here we address this oversight in three ways. First, we provide a step-by-step description of our approach toward estimating carbon footprints from micro-level transaction data generated by more than 100,000 customers of a large retail bank in theUnitedKingdom. Second, we quantitatively compare emission estimates obtained from transaction data with those calculated from a more standard data source used in carbon profiling, the largest household expenditure survey in the United Kingdom. Third, we offer a detailed qualitative comparison of the advantages and disadvantages of transactions versus alternative data sources (such as survey data), across key dimensions including data availability, data quality, and data detail. We find that financial transactions offer a credible alternative to survey-based sources and, if made more widely accessible, could provide important advantages for profiling emissions. These include objective, micro-level data on consumption behaviors, larger sample sizes, and longitudinal, frequent data capture. [ABSTRACT FROM AUTHOR]

The author comments on an article published within the issue which explores the gap between how marketing research is taught in business schools in Great Britain and how the practitioner world of market research is evolving. Topics covered include the challenge of re-aligning the academic and practitioner communities in marketing research, and the need to change in the curriculum as well as a new name for the sector that reflects the trend from surveys to big data.

The authors offer a response to commentaries made on their article on how player-tracking technologies could enhance stakeholders' ability to understand and reduce gambling-related harm. Topics include the benefits that can be yielded by linking self-report and naturalistic data together, how all gambling provision models raise their own issues, and important points concerning user privacy and points to features of operator data that make for strong makers of harm.

Big data starts booming in 2013 and has multiple applications in all walks of life. In such an environment, big data for information technology (BDI) and decision making (BDD) have formed some hot topics in common. This paper reviews the body of BDI and BDD research studies from 1994 to 2020, using bibliometrics analysis. The aim of this paper is to explore the current status, the correlation between BDI and BDD, the future trends and challenges. From time and space dimensions, CiteSpace and VOS viewer are used to obtain the annual trends of documents, the distribution of countries and sources, the citations and the h-index of BDI and BDD. The top three productive countries are the USA, China and the UK. From the perspective of h-index, the USA and the UK are at the forefront of the world. The value of big data is realized through information acquisition, storage, analysis, expression transmission and service sharing technologies, and the decision-making techniques exist throughout the process of big data analysis. "Business" and "Information science library science" are the latest hotspots of BDI. The appliances in the organization, supply chain management, education, and the environment are recent themes of BDD. Big data technology processing capabilities and network security issues are the main challenges in the future. This study contributes to the body of knowledge on BDI and BDD, and hops to help in understanding the evolution of them in relevant fields. [ABSTRACT FROM AUTHOR]

This study draws on organizational learning and strategic decision-making theory to develop a conceptual framework to explore how the selection measures of BDA systems and external support partners are linked to BDA system quality, and how these influence firms' competitive position. Through a cross-sectional survey of 523 IT professionals from the US, UK, and India, using path analysis and structural equation modeling, we found that the information systems selection process, enhanced by knowledge management capabilities, is positively related to the BDA system quality and firms' performance. However, inconsistent with prior studies on transactional systems, we found no support for the hypothesis that software vendor criteria influence BDA quality. Also, in selecting systems and external facilitators, organizations appear to be pivoting towards parameters that are considered "emerging", such as cloud computing, DevOps, and agile experiences, as they increase the likelihood of unlocking business value from BDA. [ABSTRACT FROM AUTHOR]

The aim of UK-REACH ("The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers") is to understand if, how, and why healthcare workers (HCWs) in the United Kingdom (UK) from ethnic minority groups are at increased risk of poor outcomes from COVID-19. In this article, we present findings from the ethical and legal stream of the study, which undertook qualitative research seeking to understand and address legal, ethical, and social acceptability issues around data protection, privacy, and information governance associated with the linkage of HCWs' registration data and healthcare data. We interviewed 22 key opinion leaders in healthcare and health research from across the UK in two-to-one semi-structured interviews. Transcripts were coded using qualitative thematic analysis. Participants told us that a significant aspect of Big Data research in public health is varying drivers of mistrust—of the research itself, research staff and funders, and broader concerns of mistrust within participant communities, particularly in the context of COVID-19 and those situated in more marginalised community settings. However, despite the challenges, participants also identified ways in which legally compliant and ethically informed approaches to research can be crafted to mitigate or overcome mistrust and establish greater confidence in Big Data public health research. Overall, our research indicates that a "Big Data Ethics by Design" approach to research in this area can help assure (1) that meaningful community and participant engagement is taking place and that extant challenges are addressed, and (2) that any new challenges or hitherto unknown unknowns can be rapidly and properly considered to ensure potential (but material) harms are identified and minimised where necessary. Our findings indicate such an approach, in turn, will help drive better scientific breakthroughs that translate into medical innovations and effective public health interventions, which benefit the publics studied, including those who are often marginalised in research. [ABSTRACT FROM AUTHOR]

Objective: Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-acquisition order is not prescribed. Using deep-learning-based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second-trimester anomaly scans.Methods: We collected prospectively full-length video recordings of routine second-trimester anomaly scans. Important scan events in the videos were identified by detecting automatically image freeze and image/clip save. The video immediately preceding and following the important event was extracted and labeled as one of 11 commonly acquired anatomical structures. We developed and used a purposely trained and tested deep-learning annotation model to label automatically the large number of scan events. Thus, anomaly scans were partitioned as a sequence of anatomical planes or fetal structures obtained over time.Results: A total of 496 anomaly scans performed by 14 sonographers were available for analysis. UK guidelines specify that an image or videoclip of five different anatomical regions must be stored and these were detected in the majority of scans: head/brain was detected in 97.2% of scans, coronal face view (nose/lips) in 86.1%, abdomen in 93.1%, spine in 95.0% and femur in 92.3%. Analyzing the clinical workflow, we observed that sonographers were most likely to begin their scan by capturing the head/brain (in 24.4% of scans), spine (in 23.2%) or thorax/heart (in 22.8%). The most commonly identified two-structure transitions were: placenta/amniotic fluid to maternal anatomy, occurring in 44.5% of scans; head/brain to coronal face (nose/lips) in 42.7%; abdomen to thorax/heart in 26.1%; and three-dimensional/four-dimensional face to sagittal face (profile) in 23.7%. Transitions between three or more consecutive structures in sequence were uncommon (up to 13% of scans). None of the captured anomaly scans shared an entirely identical sequence.Conclusions: We present a novel evaluation of the anomaly scan acquisition process using a deep-learning-based analysis of ultrasound video. We note wide variation in the number and sequence of structures obtained during routine second-trimester anomaly scans. Overall, each anomaly scan was found to be unique in its scanning sequence, suggesting that sonographers take advantage of the fetal position and acquire the standard planes according to their visibility rather than following a strict acquisition order. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual's brain‐wide similarity to the expected SMI patterns derived from meta‐analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual's similarity to genome‐wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI‐MDD (Cohen's d = 0.20, p = 1 × 10−23) and PRS‐MDD (d = 0.17, p = 1 × 10−15) than nonpsychiatric controls. UKBB participants with BD and SSD showed significant elevation in the respective RVIs (d = 0.65 and 0.60; p = 3 × 10−5 and.009, respectively) and PRS (d = 0.57 and 1.34; p =.002 and.002, respectively). Elevated RVI‐SSD were replicated in an independent sample (d = 0.53, p = 5 × 10−5). RVI‐MDD and RVI‐SSD but not RVI‐BD were associated with childhood adversity (p <.01). In nonpsychiatric controls, elevation in RVI and PRS were associated with lower cognitive performance (p < 10−5) in six out of seven domains and showed specificity with disorder‐associated deficits. In summary, the RVI is a novel brain index for SMI and shows similar or better specificity for SMI than PRS, and together they may complement each other in the efforts to characterize the genomic to brain level risks for SMI. [ABSTRACT FROM AUTHOR]

Background and objectives: Myasthenia gravis (MG) is a T cell-driven, autoantibody-mediated disorder affecting transmission in neuromuscular junctions. The associations between the peripheral T cells and MG have been extensively studied. However, they are mainly of observational nature, thus limiting our understanding of the effect of inflammatory biomarkers on MG risk. With large data sets now available, we used Mendelian randomization (MR) analysis to investigate whether the biomarkers on T cells are causally associated with MG and further validate the relationships. Methods: We performed a two-sample MR analysis using genetic data from one genome-wide association study (GWAS) for 210 extensive T-cell traits in 3,757 general population individuals and the largest GWAS for MG currently available (1,873 patients versus 36,370 age/gender-matched controls) from US and Italy. Then the biomarkers of interest were validated separately in two GWASs for MG in FIN biobank (232 patients versus 217,056 controls) and UK biobank (152 patients versus 386,631 controls). Results: In the first analysis, three T-cell traits were identified to be causally protective for MG risk: 1) CD8 on terminally differentiated CD8+ T cells (OR [95% CI] = 0.71 [0.59, 0.86], P = 5.62e-04, adjusted P =2.81e-02); 2) CD4+ regulatory T proportion in T cells (OR [95% CI] = 0.44 [0.26, 0.72], P = 1.30e-03, adjusted P =2.81e-02); 3) HVEM expression on total T cells (OR [95% CI] = 0.67 [0.52, 0.86], P = 1.61e-03, adjusted P =2.81e-02) and other eight T-cell subtypes (e.g., naïve CD4+ T cells). In particular, HVEM is a novel immune checkpoint on T cells that has never been linked to MG before. The SNPs on the TNFRSF14 per se further support a more direct link between the HVEM and MG. The validation analysis replicated these results in both FIN and UK biobanks. Both datasets showed a concordant protective trend supporting the findings, albeit not significant. Conclusion: This study highlighted the role of HVEM on T cells as a novel molecular-modified factor for MG risk and validated the causality between T cells and MG. These findings may advance our understanding of MG’s immunopathology and facilitate the future development of predictive disease-relevant biomarkers. [ABSTRACT FROM AUTHOR]

The recent advances in Internet of Things (IoT), computational analytics, processing power, and assimilation of Big Data (BD) are playing an important role in revolutionizing maintenance and operations regimes within the wider facilities management (FM) sector. The BD offers the potential for the FM to obtain valuable insights from a large amount of heterogeneous data collected through various sources and IoT allows for the integration of sensors. The aim of this article is to extend the exploratory studies conducted on Big Data analytics (BDA) implementation and empirically test and categorize the associated drivers and challenges. Using exploratory factor analysis (EFA), the researchers aim to bridge the current knowledge gap and highlight the principal factors affecting the BDA implementation. Questionnaires detailing 26 variables are sent to the FM organization in the U.K. who are in the process or have already implemented BDA initiatives within their FM operations. Fifty-two valid responses are analyzed by conducting EFA. The findings suggest that driven by market competition and ambitious sustainability goals, the industry is moving to holistically integrate analytics into its decision making. However, data quality, technological barriers, inadequate preparedness, data management, and governance issues and skill gaps are posing to be significant barriers to the fulfillment of expected opportunities. The findings of this study have important implications for FM businesses that are evaluating the potential of the BDA and IoT applications for their operations. Most importantly, it addresses the role of the BD maturity in FM organizations and its implications for perception of drivers. [ABSTRACT FROM AUTHOR]

This paper explores how liquidity in the UK rental markets reacts to variations in demand across time and space. We employ a survival analysis approach with a non-parametric hazard rate to investigate whether the probability of a property to exit the market changes across calendar months. Our unique dataset comes from Zoopla.com and contains 300,198 rental listings in 13 major UK university cities over the 2015–2017 period. Our results suggest that the probability of exit is lower during the winter season compared to summer. This could be explained by students' higher housing demand at the start of the academic term. The results become more pronounced (i.e. the seasonal difference is higher) when the distance between marketed property and university campuses is taken into account. [ABSTRACT FROM AUTHOR]

The term political extremism is commonly used to refer to political attitudes considered to be outside the ideological mainstream. This study leverages computational content analysis of big data to longitudinally examine (1970–2019) the prevalence of terms denoting far-right and far-left political extremism in more than 30 million written news and opinion articles from 54 news media outlets popular in the United States and the United Kingdom. We find that the usage of terms denoting right and left political extremism has been increasing across news media outlets in both countries. This trend is particularly stark for far-right-denoting terms, which have been growing in prevalence since at least 2008. Most U.S. and U.K. news media outlets tend to use far-right-denoting terms substantially more often than they use far-left-denoting terms. The rising prevalence in news media of terms denoting political extremism is strongly correlated with similar growing usage of terms denoting prejudice and social justice discourse. [ABSTRACT FROM AUTHOR]

Outliers in neuroimaging represent spurious data or the data of unusual phenotypes that deserve special attention such as clinical follow‐up. Outliers have usually been detected in a supervised or semi‐supervised manner for labeled neuroimaging cohorts. There has been much less work using unsupervised outlier detection on large unlabeled cohorts like the UK Biobank brain imaging dataset. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes. Here, we developed a two‐level outlier detection and screening methodology to characterize individual outliers from the multimodal MRI dataset of more than 15,000 UK Biobank subjects. In primary screening, using brain ventricles, white matter, cortical thickness, and functional connectivity‐based imaging phenotypes, every subject was parameterized with an outlier score per imaging phenotype. Outlier scores of these imaging phenotypes had good‐to‐excellent test–retest reliability, with the exception of resting‐state functional connectivity (RSFC). Due to the low reliability of RSFC outlier scores, RSFC outliers were excluded from further individual‐level outlier screening. In secondary screening, the extreme outliers (1,026 subjects) were examined individually, and those arising from data collection/processing errors were eliminated. A representative subgroup of 120 subjects from the remaining non‐artifactual outliers were radiologically reviewed, and radiological findings were identified in 97.5% of them. This study establishes an unsupervised framework for investigating rare individual imaging phenotypes within a large neuroimaging cohort. [ABSTRACT FROM AUTHOR]

PURPOSE: The ability to accurately predict an individual's risk for cancer is critical to the implementation of precision prevention measures. Current cancer risk predictions are frequently made with simple models that use a few proven risk factors, such as the Gail model for breast cancer, which are easy to interpret, but may theoretically be less accurate than advanced machine learning (ML) models. METHODS: With the UK Biobank, a large prospective study, we developed models that predicted 13 cancer diagnoses within a 10-year time span. ML and linear models fit with all features, linear models fit with 10 features, and externally developed QCancer models, which are available to more than 4,000 general practices, were assessed. RESULTS: The average area under the receiver operator curve (AUC) of the linear models (0.722, SE = 0.015) was greater than the average AUC of the ML models (0.720, SE = 0.016) when all 931 features were used. Linear models with only 10 features generated an average AUC of 0.706 (SE 0.015), which was comparable to the complex models using all features and greater than the average AUC of the QCancer models (0.684, SE 0.021). The high performance of the 10-feature linear model may be caused by the consideration of often omitted feature types, including census records and genetic information. CONCLUSION: The high performance of the 10-feature linear models indicate that unbiased selection of diverse features, not ML models, may lead to impressively accurate predictions, possibly enabling personalized screening schedules that increase cancer survival. Study of 11 cancers found that simple models made unbiasedly from big data work similarly to ML models. [ABSTRACT FROM AUTHOR]

This paper investigates the predictive ability of a brand new dataset that is based on big unstructured data published by the UK Office for National Statistics as "Faster Indicators of UK Economic Activity." We consider some indicative ways for use in macroeconomic nowcasting. Even though the ONS confirms that the newly introduced big data‐based indicators are not constructed with forecasting purposes in mind, and applied researchers should be cautious when using them in this way, a simple out‐of‐sample nowcasting exercise reveals partial evidence that this dataset has some predictive power over GDP growth. Our results, which show a positive and encouraging first step towards the use of this type of data, suggest that national statistics agencies should allocate more resources in constructing big data‐based databases. [ABSTRACT FROM AUTHOR]

The Snowden revelations concerning the use of bulk surveillance have uncovered shortcomings in the existing intelligence oversight architectures in several leading democracies and confronted them with a variety of new challenges generated by rapid technological advances. The impact of the disclosures has also been reflected in scholarship, namely in the way intelligence oversight is being reconceptualized as a broader form of governance beyond legal compliance. This article examines the case of the UK and investigates instances when the two main oversight institutions, namely IPCO and the ISC, have been shaping the public debate through their published reports and their engagement with civil society actors. The paper argues that oversight institutions are better equipped for shaping the democratic debate on bulk surveillance than any other societal actors due to their configuration of institutional features and statutory power. Empowering existing or creating new independent oversight entities with access to classified information and reliant on technical expertise is the way forward for democratic governance of intelligence services. [ABSTRACT FROM AUTHOR]

This article presents the first-ever big data study of the student evaluation of teaching (SET) using artificial intelligence (AI). We train natural language processing (NLP) models on 1.6 million student evaluations from the US and the UK. We address two research questions: (1) are these models able to predict student ratings from the student textual feedback, and (2) can AI-powered SET eliminate the problems of the traditional SET based on Likert scale surveys. We test these NLP models on a new dataset of 12,386 university reviews from the UK and on 155 SET reviews from a university that agreed to run a pilot AI project. The trained NLP models exhibited high prediction accuracy, and they learnt two biases from humans, those of extreme responding and assigning higher ratings to less demanding courses. In the future, universities will likely adopt many AI-based tools that have proved successful in client management in other sectors. However, our results make a strong case against using AI as a black box for performativity purposes. It should remain a useful tool for university administrators who are aware of the AI weaknesses documented here. [ABSTRACT FROM AUTHOR]

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely difficult to investigate using conventional techniques. In this article, the authors present three U.K. Twitter gambling advertising studies using both big data analytics and manual content analysis to explore the volume and content of gambling ads, the age and engagement of followers, and compliance with U.K. advertising regulations. They analyze 890,000 organic ads from 417 accounts along with data on 620,000 followers and 457,000 engagements (replies and retweets). They find that approximately 41,000 U.K. children follow Twitter gambling accounts, and that two-thirds of gambling advertising tweets fail to fully comply with regulations. Ads for e-sports gambling are markedly different from those for traditional gambling (such as on soccer, casinos, and lotteries) and appear to have strong appeal for children, with 28% of engagements with e-sports gambling ads coming from users under 16 years old. The authors make six policy recommendations: spotlight e-sports gambling advertising, create new social media–specific regulations, revise regulation on content appealing to children, use technology to block users under 18 years from seeing gambling ads, require ad labeling of organic gambling tweets, and deploy better enforcement. [ABSTRACT FROM AUTHOR]

Happiness economics theorizes that economic growth is only tenuously connected to happiness. This article tests this theory on historical evidence by quantifying the trend in emotional well-being (EWB) of British men during the period of rapid industrialization between 1800 and 1900, using a digitized corpus of 19,682 pamphlets published in Britain during this period and held by JSTOR. EWB is measured between these years using a sentiment analysis method of quantifying the frequency over time of a set of positive-valence ("happy") word groups relative to negative-valence ("sad") word groups in the corpus, finding that it remains trendless throughout. This method thereby also provides insight into the quality of life of men during the Industrial Revolution. [ABSTRACT FROM AUTHOR]

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Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs. When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, apart from the accessory nature of the relationships we found, we did not find any significant signal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance. Author summary: Almost all drugs to treat HIV target the Reverse Transcriptase (RT) and Drug resistance mutations (DRMs) appear in HIV under treatment pressure. Resistant strains can be transmitted and limit treatment options at the population level. Classically, multiple statistical testing is used to find DRMs, by comparing virus sequences of treated and naive populations. However, with this method, each mutation is considered individually and we cannot hope to reveal any interaction (epistasis) between them. Here, we used machine learning to discover new DRMs and study potential epistasis effects. We applied this approach to a very large UK dataset comprising ≈ 55, 000 RT sequences. Results robustness was checked on different UK and African datasets. Six new mutations associated to resistance were found. All six have a low genetic barrier and show high correlations with known DRMs. Moreover, all these mutations are close to either the active site or the regulatory binding pocket of RT. Thus, they are good candidates for further wet experiments to establish their role in drug resistance. Importantly, our results indicate that epistasis seems to be limited to the classical scheme where primary DRMs confer resistance and associated mutations modulate the strength of the resistance and/or compensate for the fitness cost induced by DRMs. [ABSTRACT FROM AUTHOR]

BACKGROUND Short-term forecasts of international migration are often based on data that are incomplete, biased, and reported with delays. There is also a scarcity of migration forecasts based on combined traditional and new forms of data. OBJECTIVE This research assessed an inclusive approach of supplementing official migration statistics, typically reported with a delay, with the so-called big data from Google searches to produce short-term forecasts ("now-casts") of immigration flows from Romania to the United Kingdom. METHODS Google Trends data were used to create composite variables depicting the general interest of Romanians in migrating into the United Kingdom. These variables were then assessed as predictors and compared with benchmark results by using univariate time series models. RESULTS The proposed Google Trends indices related to employment and education, which exhaust all possible keywords and eliminate language bias, match trends observed in the migration statistics. They are also capable of moderate reductions in prediction errors. CONCLUSIONS Google Trends data have some potential to indicate up-to-date current trends of interest in mobility, which may serve as useful predictors of sudden changes in migration. However, these data do not always improve the accuracy of forecasts. The usability of Google Trends is also limited to short-term migration forecasting and requires understanding of contexts surrounding origin and destination countries. [ABSTRACT FROM AUTHOR]

Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population‐based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large‐scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract‐based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction. [ABSTRACT FROM AUTHOR]

The prevalence of obesity, both worldwide and in the UK, continues to rise and has been classified by the NHS as a major public health issue with current public health survey data indicating that nearly two thirds of adults are living with overweight and obesity. We know from existing appetite research that it is easy to overconsume calories, but difficult to reduce calorie intake below requirements and we seek to expand this evidence base to seek future research themes to provide the evidence on how to manage obesity. This Medical Research Council (MRC)‐funded hot topic workshop explored issues that are evolving from current literature to create discussion about potential future collaborative research around 4 themes: (i) Food reformulation/innovation – how appetite research can influence sustainable and healthy diets, (ii) One diet does not fit all – how should we conduct appetite research to embrace inter‐individual differences (e.g. responder/non‐responders), (iii) Environmental drivers of obesity/food choices – bridging the gap between appetite research and obesity services – future research themes, (iv) Big data approaches to develop understanding of drivers of appetite and food choice. Appetite forms a bridge to understanding the interaction between the internal and external environments and therefore has both biological and behavioural relevance for behaviour change associated with eating. This report summarises how future research can embrace this multidisciplinary challenge. [ABSTRACT FROM AUTHOR]

The multiple ways of accessing the virtual environment are changing, those who access the Internet are changing and the role that the Internet plays in our lives. In 1995, only 1% of the world's population had access to the Internet. There are now over 4 billion Internet users worldwide and this number is growing. Over time, cyberspace has generated a series of controversies, starting from the difficulty of being given a unanimously accepted definition. At both state and institutional levels, an attempt was made to define this new concept, the results being different and adapted to the specifics of each organization. Thus, in the Cyber Security Strategy of Canada, cyberspace is presented as "the electronic world generated by the interconnection of computer networks", and in the Cyber Security Strategy of the United Kingdom of Great Britain and Northern Ireland, it is defined as "An interactive domain of digital networks that store, modify and transport data". [ABSTRACT FROM AUTHOR]

There is a drastic geographic imbalance in available global streamflow gauge and catchment property data, with additional large variations in data characteristics. As a result, models calibrated in one region cannot normally be migrated to another without significant modifications. Currently in these regions, non‐transferable machine learning models are habitually trained over small local data sets. Here we show that transfer learning (TL), in the senses of weight initialization and weight freezing, allows long short‐term memory (LSTM) streamflow models that were pretrained over the conterminous United States (CONUS, the source data set) to be transferred to catchments on other continents (the target regions), without the need for extensive catchment attributes available at the target location. We demonstrate this possibility for regions where data are dense (664 basins in Great Britain), moderately dense (49 basins in central Chile), and scarce with only remotely sensed attributes available (5 basins in China). In both China and Chile, the TL models showed significantly elevated performance compared to locally trained models using all basins. The benefits of TL increased with the amount of available data in the source data set, and seemed to be more pronounced with greater physiographic diversity. The benefits from TL were greater than from pretraining LSTM using the outputs from an uncalibrated hydrologic model. These results suggest hydrologic data around the world have commonalities which could be leveraged by deep learning, and synergies can be had with a simple modification of the current workflows, greatly expanding the reach of existing big data. Finally, this work diversified existing global streamflow benchmarks. Plain Language Summary: We introduced a method to utilize available big data to better start and warm up a machine learning streamflow model that is later fine‐tuned for prediction in basins on other continents (Asia, South America and Europe). This procedure noticeably improved streamflow volume prediction for different scenarios with varying amounts of data in the target basins (in terms of time period, length of collected data, and number of basins having data). This allows thousands of basins across the world with only a few years' worth of streamflow observations to benefit from improved modeling and accuracy resulting from the use of deep learning. Key Points: Basins around the world can be better modeled by applying transfer learning (TL) to a deep network trained in the US, and tuning it locallyThe benefits of TL increased with the amount and diversity of the source data, and were larger than from pretraining with a hydrologic modelThis work greatly expands the reach of deep learning, adds to the value of existing big data, and calls for synergy of global data sets [ABSTRACT FROM AUTHOR]

Reviews of official statistics for UK housing have noted that developments have not kept pace with real‐world change, particularly the rapid growth of private renting. This paper examines the potential value of big data in this context. We report on the construction of a dataset from the on‐line adverts of one national lettings agency, describing the content of the dataset and efforts to validate it against external sources. The paper specifically examines what these data might add to our understanding of changing volumes and rents in the private rented sector. Fluctuations in market share across advertising platforms make assessment of volume problematic, while rental prices appear more robust through comparison with other reference information. Focussing on one urban area, we illustrate how the dataset can shed new light on local changes. Lastly, we discuss the issues involved in making more routine use of this kind of data. [ABSTRACT FROM AUTHOR]

In the era of Big Data, the Internet has become one of the main data sources: Data can be collected for relatively low costs and can be used for a wide range of purposes. To be able to timely support solid decisions in any field, it is essential to increase data production efficiency, data accuracy, and reliability. In this framework, our paper aims at identifying an optimized and flexible method to collect and, at the same time, geolocate social media information over a whole country. In particular, the target of this paper is to compare three alternative methods to collect data from the social media Twitter. This is achieved considering four main comparison criteria: Collection time, dataset size, pre-processing phase load, and geographic distribution. Our findings regarding Great Britain identify one of these methods as the best option, since it is able to collect both the highest number of tweets per hour and the highest percentage of unique tweets per hour. Furthermore, this method reduces the computational effort needed to pre-process the collected tweets (e.g., showing the lowest collection times and the lowest number of duplicates within the geographical areas) and enhances the territorial coverage (if compared to the population distribution). At the same time, the effort required to set up this method is feasible and less prone to the arbitrary decisions of the researcher. [ABSTRACT FROM AUTHOR]

The UK Biobank (UKB) is a health research resource of major international importance, incorporating comprehensive characterization of >500 000 men and women recruited between 2006 and 2010 from across the UK. There is prospective tracking of health outcomes for all participants through linkages with national cohorts (death registers, cancer registers, electronic hospital records, and primary care records). The dataset has been enhanced with the UKB imaging study, which aims to scan a subset of 100 000 participants. The imaging protocol includes magnetic resonance imaging of the brain, heart, and abdomen, carotid ultrasound, and whole-body dual X-ray absorptiometry. Since its launch in 2015, over 48 000 participants have completed the imaging study with scheduled completion in 2023. Repeat imaging of 10 000 participants has been approved and commenced in 2019. The cardiovascular magnetic resonance (CMR) scan provides detailed assessment of cardiac structure and function comprising bright blood anatomic assessment (sagittal, coronal, and axial), left and right ventricular cine images (long and short axes), myocardial tagging, native T1 mapping, aortic flow, and imaging of the thoracic aorta. The UKB is an open access resource available to health researchers across all scientific disciplines from both academia and industry with no preferential access or exclusivity. In this paper, we consider how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date. [ABSTRACT FROM AUTHOR]

The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic. [ABSTRACT FROM AUTHOR]

To understand the links between Big Data and economic development is to focus on the knowledge that may be created from Big Data and the knowledge workers who can produce and use this knowledge to maximum economic impact. Using location quotients, this paper finds that the current geographical concentration of employees with skills to generate value from Big Data means that economic opportunities will be centred in the London and South East regions. In contrast, Big Data opportunities will be less important for the already lagging regions in Great Britain. [ABSTRACT FROM AUTHOR]

Public bodies in the United Kingdom are increasingly using algorithms and big data to make decisions. While there is growing awareness of the risks of algorithmic discrimination, it can be very difficult to establish that a specific algorithm is in fact discriminatory. This raises the question of how courts should allocate the burden of testing and proving whether a government algorithm is discriminatory, as between the government and the person affected. In R (Bridges) v South Wales Police [2020] EWCA Civ 1058, the England and Wales Court of Appeal found that public bodies must take positive steps to identify and address risks of algorithmic discrimination. This note explores the decision in Bridges and its implications for algorithmic decision-making in government. It suggests that Bridges, alongside recent decisions in Canada and the Netherlands, forms part of a broader trend: the courts are placing the burden of testing and reviewing potentially discriminatory algorithms on government, rather than the general public. [ABSTRACT FROM AUTHOR]