Large-scale Data Collection Research Articles

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons

Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.

Automated, Real-Time Integration of Biometric Data From Wearable Devices With Electronic Medical Records: A Feasibility Study.

A major barrier to the incorporation of biometric data into clinical practice is the lack of device integration with electronic medical records (EMRs). We developed infrastructure to transmit biometric data from an Apple Watch into the EMR for physician review. The study objective was to test feasibility of using this infrastructure for patients undergoing radiotherapy. The study included patients with breast or prostate cancer receiving ≥3 weeks of radiotherapy who reported owning an Apple Watch. Daily resting heart rate (HR), HR variability, step count, and exercise minutes were automatically transferred to our EMR using a custom app installed on each patient's iPhone. Biometric data were presented to the treating radiation oncologist for review on a weekly basis during creation of the on-treatment note. Feasibility was defined a priori as physician review of biometric data for at least 90% of patients. Time trends in biometric data were tested using the Jonckheere-Terpstra test. Patient satisfaction was assessed using the System Usability Scale (SUS), with scores above 80 considered above-average user experience. Of the 20 patients enrolled, biometric data were successfully transmitted to the EMR and reviewed by the radiation oncologist for 95% (n = 19) of patients, thus meeting the a priori feasibility threshold. For patients with radiation courses ≥4 weeks, exercise minutes decreased over time (P = .01) and daily mean HR variability increased over time (P = .02). The median SUS was 82.5 (IQR, 70-87.5). Our study demonstrates the feasibility of real-time integration of biometric data collected from an Apple Watch into the EMR with subsequent physician review. The high rates of physician review and patient satisfaction provide support for further development of large-scale collection of wearable device data.

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide “eras” of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

The condition monitoring scheme for industrial IoT scenario: A distributed modeling for high-dimensional nonstationary data

Based on large-scale data collection and high-speed transmission, Industrial Internet of Things (IIoT) promotes the rapid development of intelligent manufacturing. IIoT systems are usually disturbed by complex external factors, which lead to high-dimensional nonstationary operating data. Besides, unexpected data transmission interruptions, sensor failures, and network delays lead to data loss. This paper proposes a distribution & communication strategy for monitoring high-dimensional nonstationary processes with missing values in IIoT scenarios. First, a deep learning-based imputation network is proposed to impute the missing values. Then a decomposition strategy based on degree of cointegration is proposed, which decomposes a high-dimensional nonstationary process into multiple blocks. And a communication strategy is proposed to mine the internal relationship between different blocks. Finally, faulty information is detected by a distributed framework. Two real cases from IIoT are applied to illustrate the monitoring performance of the proposed method. The results show that the proposed method outperforms existing benchmarks in data imputation and monitoring performance.

Evidence-Based Recommendations for Tablet Recordings From the Bridge2AI-Voice Acoustic Experiments

BackgroundAs part of a larger goal to create best practices for voice data collection to fuel voice AI research, the objective of this study was to investigate the ability of readily available iOS and Android tablets with and without low-cost headset microphones to produce recordings and subsequent acoustic measures of voice comparable to “research quality” instrumentation. MethodsRecordings of 24 sustained vowel samples representing a wide range of typical and disordered voices were played via a head-and-torso model and recorded using a research quality standard microphone/preamplifier/audio interface. Acoustic measurements from the standard were compared to two popular tablets using their built-in microphones and with low-cost headset microphones at different distances from the mouth. ResultsVoice measurements obtained via tablets + headset microphones close to the mouth (2.5 and 5cm) strongly correlated (r’s > 0.90) with the research standard and resulted in no significant differences for measures of vocal frequency and perturbation. In contrast, voice measurements obtained using the tablets’ built-in microphones at typical reading distances (30 and 45cm) tended to show substantial variability in measurement, greater mean differences in voice measurements, and relatively poorer correlations vs. the standard. ConclusionFindings from this study support preliminary recommendations from the Bridge2AI-Voice Consortium recommending the use of smartphones paired with low-cost headset microphones as adequate methods of recording for large-scale voice data collection from a variety of clinical and non-clinical settings. Compared to recording using a tablet directly, a headset microphone controls for recording distance and reduces the effects of background noise, resulting in decreased variability in recording quality. Data Availability StatementData supporting the results reported in this article may be obtained upon request from the contact author.

Optimizing Response Rates to Examine Health IT Maturity and Nurse Practitioner Care Environments in US Nursing Homes: Mixed Mode Survey Recruitment Protocol.

Survey-driven research is a reliable method for large-scale data collection. Investigators incorporating mixed-mode survey designs report benefits for survey research including greater engagement, improved survey access, and higher response rate. Mix-mode survey designs combine 2 or more modes for data collection including web, phone, face-to-face, and mail. Types of mixed-mode survey designs include simultaneous (ie, concurrent), sequential, delayed concurrent, and adaptive. This paper describes a research protocol using mixed-mode survey designs to explore health IT (HIT) maturity and care environments reported by administrators and nurse practitioners (NPs), respectively, in US nursing homes (NHs). The aim of this study is to describe a research protocol using mixed-mode survey designs in research using 2 survey tools to explore HIT maturity and NP care environments in US NHs. We are conducting a national survey of 1400 NH administrators and NPs. Two data sets (ie, Care Compare and IQVIA) were used to identify eligible facilities at random. The protocol incorporates 2 surveys to explore how HIT maturity (survey 1 collected by administrators) impacts care environments where NPs work (survey 2 collected by NPs). Higher HIT maturity collected by administrators indicates greater IT capabilities, use, and integration in resident care, clinical support, and administrative activities. The NP care environment survey measures relationships, independent practice, resource availability, and visibility. The research team conducted 3 iterative focus groups, including 14 clinicians (NP and NH experts) and recruiters from 2 national survey teams experienced with these populations to achieve consensus on which mixed-mode designs to use. During focus groups we identified the pros and cons of using mixed-mode designs in these settings. We determined that 2 mixed-mode designs with regular follow-up calls (Delayed Concurrent Mode and Sequential Mode) is effective for recruiting NH administrators while a concurrent mixed-mode design is best to recruit NPs. Participant recruitment for the project began in June 2023. As of April 22, 2024, a total of 98 HIT maturity surveys and 81 NP surveys have been returned. Recruitment of NH administrators and NPs is anticipated through July 2025. About 71% of the HIT maturity surveys have been submitted using the electronic link and 23% were submitted after a QR code was sent to the administrator. Approximately 95% of the NP surveys were returned with electronic survey links. Pros of mixed-mode designs for NH research identified by the team were that delayed concurrent, concurrent, and sequential mixed-mode methods of delivering surveys to potential participants save on recruitment time compared to single mode delivery methods. One disadvantage of single-mode strategies is decreased versatility and adaptability to different organizational capabilities (eg, access to email and firewalls), which could reduce response rates. DERR1-10.2196/56170.

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database

The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve patient outcomes. The PhysioNet/CinC Challenge 2016 database, a large and influential resource, encourages contributions to accurate heart sound state classification (normal versus abnormal), achieving promising benchmark performance (accuracy: 99.80%; sensitivity: 99.70%; specificity: 99.10%; and score: 99.40%). This study reviews recent advances in analytical techniques applied to this database, and 104 publications on PCG signal analysis are retrieved. These techniques encompass heart sound preprocessing, signal segmentation, feature extraction, and heart sound state classification. Specifically, this study summarizes methods such as signal filtering and denoising; heart sound segmentation using hidden Markov models and machine learning; feature extraction in the time, frequency, and time-frequency domains; and state-of-the-art heart sound state recognition techniques. Additionally, it discusses electrocardiogram (ECG) feature extraction and joint PCG and ECG heart sound state recognition. Despite significant technical progress, challenges remain in large-scale high-quality data collection, model interpretability, and generalizability. Future directions include multi-modal signal fusion, standardization and validation, automated interpretation for decision support, real-time monitoring, and longitudinal data analysis. Continued exploration and innovation in heart sound signal analysis are essential for advancing cardiac care, improving patient outcomes, and enhancing user trust and acceptance.

Children and young people as research collaborators in a large-scale migration research project

In this paper we detail and contextualise the collaborative participatory research process developed throughout the IMMERSE research project which maps the integration of migrant children in education in Europe. We begin with a brief overview of IMMERSE and thereafter discuss the research design with reference to literature on collaborative research approaches and on working with children and young people’s research advisory groups. IMMERSE involved a large-scale data collection with over 24,000 children and young people in six countries and such large-scale data collection presents a challenging context in which to include children as collaborators and influencers in the research process. We demonstrate how, drawing on existing knowledge as well as our own experiences of working with children as research partners, we developed innovative ways of ensuring children’s voices were integral to and embedded in the research at various points of the process. Throughout the duration of the project, we developed several strategies to ensure that children were involved as research partners and influencers. To ensure children and young people’s active involvement and influence on the research, a Children and Young People’s Advisory Group was established at the beginning of IMMERSE which was composed of 18 migrant and refugee children resident in Ireland. This group acted as an expert group, to reflect on the experiences of migrant children and represent their peers in this process. In addition, children across the six IMMERSE countries were involved in the design and validation of the dashboard of socio-educational integration indicators and the ensuing research instruments.

Comparison of user interfaces for measuring the matrix sentence test on a smartphone

Objective Smartphone-based self-testing could facilitate large-scale data collection and remote diagnostics. For this purpose, the matrix sentence test (MST) is an ideal candidate due to its repeatability and accuracy. In clinical practice, the MST requires professional audiological equipment and supervision, which is infeasible for smartphone-based self-testing. Therefore, it is crucial to investigate the feasibility of self-administering the MST on smartphones, including the development of an appropriate user interface for the small screen size. Design We compared the traditional closed matrix user interface (10 × 5 matrix) to three alternative, newly-developed interfaces (slide, type, wheel) regarding SRT consistency, user preference, and completion time. Study sample We included 15 younger normal hearing and 14 older hearing-impaired participants in our study. Results The slide interface is most suitable for mobile implementation, providing consistent and fast SRTs and enabling all participants to perform the tasks effectively. While the traditional matrix interface works well for most participants, some participants experienced difficulties due to its small size on the screen. Conclusions We propose the newly-introduced slide interface as a plausible alternative for smartphone screens. This might be more attractive for elderly patients that may exhibit more challenges with dexterity and vision than our test subjects employed here.

Dynamic Adaptive Mechanism Design and Implementation in VSS for Large-Scale Unified Log Data Collection

This paper studies the collection of large-scale log data of information system and puts forward a dynamic adaptive mechanism for large-scale unified log data collection. Furthermore, we design and implement our method for pan-government industry safety operation management platform. The data flow processing architecture based on message queue is adopted to realize the decoupling of log collection, log processing and log reporting. The traffic peak clipping technology of message queue is adopted to ensure the safety and reliability of log transmission. According to the characteristics of log traffic, a design mode supporting dynamic adjustment of consumption group is proposed to meet the high-performance requirements of the system. The whole system can meet the centralized analysis, security threat perception and intelligent analysis of various security data. Meanwhile, we analyzed and compared with the traditional open-source log collection technology, our proposed method and system has clear advantages.

A multi-scale attributes fusion model for travel mode identification using GPS trajectories

ABSTRACT Travel mode recognition is a key issue in urban planning and transportation research. While traditional travel surveys use manual data collection and have limited coverage, poor timeliness, and insufficient sample capacity, recent advancements in Global Positioning System (GPS) technology allow large-scale data collection and offer novel opportunities to enhance travel mode recognition. However, existing studies often neglect regular differences and changes in motion states across different travel modes and fail to fully integrate multi-scale spatio-temporal features, which limits the accurate classification of travel modes. To fill this gap, this study proposes a multi-scale spatio-temporal attribute fusion (MSAF) model for precise travel mode identification using solely GPS trajectories without altering their sampling rate. The MSAF model segments GPS trajectories into various temporal and spatial scales, extracting local motion states and spatial features at multiple scales. The spatio-temporal feature extraction module is constructed to extract local motion states and capture spatio-temporal dependencies. Additionally, the model incorporates a multi-scale feature fusion module, which effectively combines features of various scales through a series of fusion techniques to obtain a comprehensive representation, enabling automatic and accurate travel mode identification. Experiments on real-world datasets, including the GeoLife Trajectories dataset and the Sussex-Huawei Locomotion-Transportation (SHL) dataset, demonstrate the effectiveness of the MSAF model, achieving a competitive accuracy of 95.16% and 91.70%. This represents an improvement of 2.50% to 7.95% and 0.8% to 6.62% over several state-of-the-art baselines, effectively addressing sample imbalance challenges. Moreover, the experiments demonstrate the significant role of multiscale feature fusion in improving model performance.

The intra- and interrater reliability of isometric muscle contractions using a novel fixation system for a hand-held dynamometer.

A novel fixation system for a hand-held dynamometer (HHD) was designed to enable isometric muscle measurements on various muscle groups of strong, healthy individuals in a field setting. The objective of this study was to evaluate the intra- and interrater reliability of the system and determine its suitability for use by multiple researchers within large-scale data collections during field activities. Four researchers tested eight healthy subjects, who each completed eight different maximal isometric muscle strength assessments using the HHD fixation system. Intraclass correlation coefficients (ICC) results were evaluated with a 95% confidence interval. ICC results for interrater reliability demonstrated excellent agreement of all eight measurements tested. ICC results for intrarater reliability demonstrated excellent agreement for six out of eight measurements. This system provides a new opportunity for several different high-quality maximal muscle strength measurements to be collected by multiple data collectors on large numbers of strong, healthy individuals in a field setting.

Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

Advancing English Language Teaching through Survey Research: Methodologies, Impacts and Applications

Background: A survey is a tool used to gather data that describes one or more attributes of a particular group. This tool, as a research design concentrates on examining the attitudes, opinions, and characteristics of a particular group. Survey research design in English Language Teaching (ELT) involves using questionnaires or interviews to collect data from a sample of participants to generalize findings about larger populations. Methods: This article comprehensively explores the use and methodologies of survey research within various academic fields, particularly emphasizing its impact on English Language Teaching (ELT). The paper explores the methodical process of gathering and analyzing data, emphasizing how surveys are used to record a wide range of viewpoints on teaching methods, learner opinions, and educational results. Results: This design is commonly used in ELT and applied linguistics research to gather information on language learning strategies, teacher attitudes, student satisfaction, or the effectiveness of teaching methods. This data assists educators and administrators in making well-informed choices about the development of curriculum, training of teachers, and allocation of resources. Conclusion: The paper describes the primary methods, techniques, and tools used in survey research design. Moreover, the paper provides an outline for survey research that English teachers can utilize in the classroom. Novelty: Survey research in ELT captures diverse data on teaching methods, learner achievements, and educational contexts. It facilitates large-scale data collection, enabling generalization of findings, and investigates correlations between teaching practices and student outcomes.

High-resolution harvester data for estimating rolling resistance and forest trafficability

AbstractInformation on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient ($$\mu _{RR}$$ μ RR ), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, $$\mu _{RR}$$ μ RR can be computed for harvest areas. This study describes an extensive, high-resolution data on $$\mu _{RR}$$ μ RR collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating $$\mu _{RR}$$ μ RR on steeper slopes, enabling the calculation within a $$-10^{\circ }$$ - 10 ∘ to $$+10^{\circ }$$ + 10 ∘ slope range with a speed range of 0.6–1.2 ms$$^{-1}$$ - 1 . We characterise the variation in $$\mu _{RR}$$ μ RR both between and within 11 test sites harvested during the April-August period. The site mean $$\mu _{RR}$$ μ RR varies from $$\sim$$ ∼ 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of $$\mu _{RR}$$ μ RR within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the $$\mu _{RR}$$ μ RR extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining $$\mu _{RR}$$ μ RR from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.

Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and google street view

IntroductionWearing a helmet reduces the risk of head injuries substantially in the event of a motorcycle crash. Countries around the world are committed to promoting helmet use, but the progress has been slow and uneven. There is an urgent need for large-scale data collection for situation assessment and intervention evaluation.MethodsThis study proposes a scalable, low-cost algorithm to estimate helmet-wearing rates. Applying the state-of-the-art deep learning technique for object detection to images acquired from Google Street View, the algorithm has the potential to provide accurate estimates at the global level.ResultsTrained on a sample of 3995 images, the algorithm achieved high accuracy. The out-of-sample prediction results for all three object classes (helmets, drivers, and passengers) reveal a precision of 0.927, a recall value of 0.922, and a mean average precision at 50 (mAP50) of 0.956.DiscussionThe remarkable model performance suggests the algorithm’s capacity to generate accurate estimates of helmet-wearing rates from an image source with global coverage. The significant enhancement in the availability of helmet usage data resulting from this approach could bolster progress tracking and facilitate evidence-based policymaking for helmet wearing globally.

Scaling a common assessment of associative ability: Development and validation of a multiple-choice compound remote associates task.

The assessment of creativity as an individual difference has historically focused on divergent thinking, which is increasingly viewed as involving the associative processes that are also understood to be a key component of creative potential. Research on associative processes has proliferated in many sub-fields, often using Compound Remote Associates (CRA) tasks with an open response format and relatively small participant samples. In the present work, we introduce a new format that is more amenable to large-scale data collection in survey designs, and present evidence for the reliability and validity of CRA measures in general using multiple large samples. Study 1 uses a large, representative dataset (N = 1,323,480) to demonstrate strong unidimensionality and internal consistency (α = .97; ωt = .87), as well as links to individual differences in temperament, cognitive ability, occupation, and job characteristics. Study 2 uses an undergraduate sample (N = 685) to validate the use of a multiple-choice format relative to the traditional approach. Study 3 uses a crowdsourced sample (N = 357) to demonstrate high test-retest reliability of the items (r =.74). Finally, Study 4 uses a sample that overlaps with Study 1 (N = 1,502,922) to provide item response theory (IRT) parameters for a large set of high-quality CRA items that use a multiple-choice response mode, thus facilitating their use in future research on creativity, insight, and related topics.

The Patient Population at Homeopathic Outpatient Clinics across India: A Clinical Data Collection Study.

Even though several initiatives have been undertaken in different locations worldwide to collect clinical data in homeopathy, it is important to further investigate these aspects in the context of health care in India. The study aimed to gather and analyze patients' clinical data and to derive insights into homeopathic treatment using an internet-based software program for data storage, retrieval and repertorization. A multi-center observational study was conducted across 14 homeopathy outpatient clinics in India that are affiliated with the Central Council for Research in Homoeopathy (CCRH). Patient symptoms and demographic details were documented anonymously, and prescriptions were guided by repertorial suggestions from the Vithoulkas Compass software. During follow-up visits, treatment outcome was also recorded using an online assessment form. A retrospective analysis of data on patients' demographics, follow-up visits, morbidity (International Classification of Diseases 11th Revision), rubrics used, prescribed medicines and the level of improvement was achieved using Microsoft Excel-generated pivot tables. Throughout the study duration of one year a total of 2,811 patients attended the 14 outpatient clinics, of whom 2,468 were new patients with a total of 2,172 initial homeopathic prescription entries. Across the study, there were 3,491 prescriptions and 1,628 follow-up consultations for 868 follow-up patients, all of which data were thoroughly analyzed. The highest frequency of patients was in the 20-49 age group, and a higher proportion of the patients overall was female. Musculoskeletal, dermatological and respiratory complaints were the most frequently reported. The rubrics "Desire for sweets" and "Desire for spices" emerged as the most commonly used in the repertorizations. Further, Sulphur stood out as the most commonly prescribed medicine overall. With homeopathic treatment, some degree of clinical improvement was reported in 86% of the follow-up cases. Homeopathy is prescribed in CCRH outpatient clinics for a wide range of ailments in people across India, with at least some clinical improvement noted in a high proportion of those patients. The large-scale systematic data collection in these clinics has provided clear insights into the use and clinical value of homeopathy in India, with the potential to build a substantive nationwide data inventory over time.