Efficiency Of Data Collection Research Articles

Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways

Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida’s public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3026 left turn, 1210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.

Implementing a routine and standard approach for the automatic collection of socio-economic impact observations for impact-based forecasting and warning

Globally, there are different scientific methods being developed to support the provision of Impact-based Forecasts and Warnings. Impact data is critically valuable to many of these methods enabling the development of new hazard impact models and forecasting applications, and the evaluation of these different products. However, routine and consistent approaches to collect socio-economic impact information remain challenging, with traditional approaches being labour intensive, error prone and unsustainable. This paper describes the design of an automated workflow that can collect, standardise, and harmonise impact observations from a range of sources, routinely. The workflow is a 6-step process including web scraping, Natural Language Processing (NLP) and geoparsing. The workflow enables essential criteria (i.e., date, location, types and details of the societal impacts and the hazard(s) which led to them) to be captured faster than previous manual approaches. It also allows a wider range of hazard and impact types to be collected than was previously practical to do. Testing of the workflow indicates that it could benefit from further refinement of the NLP and geoparse modules to allow for the detection of finer scale impact locations. This in turn would allow for different scales of event detection to be developed and tested. Additionally, the applicability of the workflow for different types of impact data sources such as news and social media remains to be tested. Despite the need for further refinement, this workflow represents a step change in the speed and efficiency of impact data collection within the Met Office.

Neurophysiological recordings from parietal areas of macaque brain during an instructed-delay reaching task

Facilitating data sharing in scientific research, especially in the domain of animal studies, holds immense value, particularly in mitigating distress and enhancing the efficiency of data collection. This study unveils a meticulously curated collection of neural activity data extracted from six electrophysiological datasets recorded from three parietal areas (V6A, PEc, PE) of two Macaca fascicularis during an instructed-delay foveated reaching task. This valuable resource is now accessible to the public, featuring spike timestamps, behavioural event timings and supplementary metadata, all presented alongside a comprehensive description of the encompassing structure. To enhance accessibility, data are stored as HDF5 files, a convenient format due to its flexible structure and the capability to attach diverse information to each hierarchical sub-level. To guarantee ready-to-use datasets, we also provide some MATLAB and Python code examples, enabling users to quickly familiarize themselves with the data structure.

Efficient multiple unmanned aerial vehicle-assisted data collection strategy in power infrastructure construction

Efficient data collection and sharing play a crucial role in power infrastructure construction. However, in an outdoor remote area, the data collection efficiency is reduced because of the sparse distribution of base stations (BSs). Unmanned aerial vehicles (UAVs) can perform as flying BSs for mobility and line-of-sight transmission features. In this paper, we propose a multiple temporary UAV-assisted data collection system in the power infrastructure scenario, where multiple temporary UAVs are employed to perform as relay or edge computing nodes. To improve the system performance, the task processing model selection, communication resource allocation, UAV selection, and task migration are jointly optimized. We designed a QMIX-based multi-agent deep reinforcement learning algorithm to find the final optimal solutions. The simulation results show that the proposed algorithm has better convergence and lower system costs than the current existing algorithms.

The crossroads of tradition and modern technology: integrative approaches to studying carnivores in low density ecosystems

The study of large carnivores in semi-arid ecosystems presents inherent challenges due to their low densities, extensive home ranges, and elusive nature. We explore the potential for the synthesis of traditional knowledge (i.e. art of tracking) and modern technology to address challenges in conservation and wildlife research in these challenging environments. Our research focuses on the African lion (Panthera leo) in the Central Kalahari region of Botswana as a model system to demonstrate the potential of this integrative approach. Combining GPS tracking and traditional San trackers’ expertise, we present two case studies: (1) the individual identification of lions via a combination of tracking and footprint analysis and (2) the monitoring of territorial behavior through a combination of GPS technology (i.e. GPS collars and handheld GPS devices) and non-invasive tracking. These approaches enhance our understanding of carnivore ecology as well as support conservation efforts by offering a non-invasive, cost-effective, and highly accurate means of monitoring populations. Our findings underscore the value of merging traditional tracking skills with contemporary analytical and technological developments to offer new insights into the ecology of carnivores in challenging environments. This approach not only improves data collection accuracy and efficiency but also fosters a deeper understanding of wildlife, ensuring the conservation and sustainable management of these species. Our work advocates for the inclusion of indigenous knowledge in conservation science, highlighting its relevance and applicability across various disciplines, thereby broadening the methodologies used to study wildlife, monitor populations, and inform conservation strategies.

Near-quantum-limited haloscope search for dark-photon dark matter enhanced by a high- Q superconducting cavity

We report new experimental results on the search for dark photons based on a near-quantum-limited haloscope equipped with a superconducting cavity. The loaded quality factor of the superconducting cavity is 6×105, so that the expected signal from dark-photon dark matter can be enhanced by more than one order compared to a copper cavity. A Josephson parametric amplifier with a near-quantum-limited noise temperature has been utilized to minimize the noise during the search. Furthermore, a digital acquisition card based on field programmable gate arrays has been utilized to maximize data collection efficiency with a duty cycle being 100%. This work has established the most stringent constraints on dark photons at around 26.965 μeV. In the future, our apparatus can be extended to search for other dark matter candidates, such as axions and axionlike particles, and scrutinize new physics beyond the Standard Model. Published by the American Physical Society 2024

Technology impact: A study on the performance appraisal process in the IT industry

This study investigates the impact of technology on the performance appraisal process within the IT industry. Rapid advancements in technology have transformed traditional performance appraisal methods, and understanding the implications of these changes is crucial for organizations striving to optimize their performance management strategies. The use of technology for performance appraisals in the IT industry can introduce several problems that can negatively impact the evaluation process and employee morale. Automated systems may rely on irrelevant or incomplete metrics that do not capture important qualitative aspects of performance. These systems may be prone to errors, biases, and inaccuracies in data collection and interpretation. The findings of this study reveal the significant impact of technology on the performance appraisal process in the IT industry. There are potential solutions for mitigating performance appraisal problems in the IT industry. To ensure that performance appraisal systems are relevant and accurate, organizations can involve employees in the development of metrics and provide regular feedback. The integration of digital tools has enhanced the efficiency and accuracy of data collection, tracking, and analysis, leading to streamlined processes, reduced administrative burdens, and increased data reliability. Real-time communication channels have facilitated more frequent and timely feedback, fostering employee engagement and development.

Leveraging ChatGPT for Enhanced Logical Analysis in the Theory of Constraints Thinking Process

Abstract Background/Purpose Artificial intelligence (AI) has traditionally been used for quantitative analysis using explicit data. However, much of the information critical to decision making remains undocumented and is not stored in a structured way. This study explores the integration of AI, specifically ChatGPT, into Theory of Constraints (TOC) Thinking Process (TP) tools. Method In this study, we applied ChatGPT to a real-world IT project management case using a variety of research methods, including international literature analysis, observation, and personal experience. The use of the TOC TP allowed us to understand the decision-making process of ChatGPT and to systematically explore its advantages and limitations in creating logical trees of TOC TP. Results ChatGPT significantly enhanced efficiency and depth in TOC TP data collection and analysis, effectively addressing logical leaps for more coherent structures. It also promoted deeper analytical thinking and aided root cause identification. The integration of ChatGPT into the TOC TP process led to faster decision-making, reduced bias, and clearer analysis. Challenges of ChatGPT including the need for human oversight, specific TOC TP training, and ethical considerations were noted. Conclusion This study provides an initial investigation into the use of ChatGPT in TOC TP tools. The results suggest that ChatGPT has the potential to be a valuable tool for organizations seeking to improve their decision making and performance. However, further research is needed to validate these findings and explore the full potential of AI in TOC TP.

Opportunities and Challenges in Health Insurance Statistics in the Era of Big Data

With the rapid advancement of big data technology, health insurance statistics are undergoing unprecedented transformations. This study explores the opportunities and challenges of health insurance statistics in the context of big data, offering theoretical frameworks and strategic recommendations. Through a comprehensive literature review and theoretical analysis, the study systematically examines related research on the application of big data in health insurance statistics globally. It analyzes how big data technology enhances the efficiency of data collection, processing, and analysis in health insurance, while also addressing issues like data security, privacy protection, and talent shortages. The research involves an in-depth analysis of big data technology characteristics and a reevaluation of health insurance statistical workflows. The findings indicate that big data offers opportunities for improved data processing capabilities and enhanced decision support in health insurance statistics, while simultaneously posing challenges in data governance, privacy protection, and talent development. To fully leverage the advantages of big data, an improved data management system, strengthened data security and privacy measures, and the cultivation of professionals skilled in big data analysis are necessary. This study provides theoretical support and practical guidance for the development of health insurance statistics in the big data context.

Classification of Population Data of Nagari Based on Economic Level Using The K-Nearest Neighbor Method

The process of collecting data and classifying the level of economic status of the residents of Nagari is currently manual, so the efficiency in data collection is less than ideal. As a result, the population of Nagari is not well controlled by government authorities due to the lack of detailed government supervision. Therefore, a classification application system is needed that can overcome these problems. In making a classification application system, it will be analyzed using the K-Nearest Neighbor method for grouping the economic level of the community, where data from each occupant of Nagari is used as criteria. It is hoped that this classification application can facilitate the grouping of the economic class of the population, so that the regent of village can make decisions on assistance from the system.

RANCANGAN BANGUN APLIKASI ABSENSI ANGKATAN DARAT BERBASIS WEBSITE Studi: Gudang Pusat Perbekalan Angkutan Angkatan Darat

Attendance is a very important function for the employee payroll process. Calculating attendance according to the number of working days means that the listing of attendance and subsequent filing must be controlled. The Central Depot of Supplies and Transport of the Army or GUPSBEKANGAD is the central bank institution of the Indonesian armed forces headquarters, whose main mission is to provide combat logistics services and military transportation. GUPSBEKANGAD's personnel handling system is still manual, namely using attendance books and signatures. This of course affects the efficiency of data collection, data recovery and calculation of time-consuming attendance reports, so the risk of errors that lead to the loss of attendance data is getting higher. Thus, there is a requirement for an application capable of capturing attendance data and overseeing attendance reports. This research will use a prototype development methodology. It is hoped that this Army Personnel Attendance Web Application will provide convenience in the attendance process, data collection, and attendance tracking, and reduce the risk of loss and errors in listing attendance data at GPUSBEKANGAD.

High-Throughput Morphological Chirality Quantification of Twisted and Wrinkled Gold Nanorods.

Chirality in gold nanostructures offers an exciting opportunity to tune their differential optical response to left- and right-handed circularly polarized light, as well as their interactions with biomolecules and living matter. However, tuning and understanding such interactions demands quantification of the structural features that are responsible for the chiral behavior. Electron tomography (ET) enables structural characterization at the single-particle level and has been used to quantify the helicity of complex chiral nanorods. However, the technique is time-consuming and consequently lacks statistical value. To address this issue, we introduce herein a high-throughput methodology that combines images acquired by secondary electron-based electron beam-induced current (SEEBIC) with quantitative image analysis. As a result, the geometric chirality of hundreds of nanoparticles can be quantified in less than 1 h. When combining the drastic gain in data collection efficiency of SEEBIC with a limited number of ET data sets, a better understanding of how the chiral structure of individual chiral nanoparticles translates into the ensemble chiroptical response can be reached.

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Machine learning for public wellness: optimizing hygiene practices and pollution monitoring in smart cities

Introduction. Public health in urban areas is of paramount importance, particularly in the context of smart cities where technology plays a vital role. The integration of sophisticated infrastructure and data-driven systems in smart cities has the potential to significantly enhance public health outcomes. This improvement hinges on optimizing various factors, especially in the realms of hygiene standards and pollution monitoring. The ability to adhere to stringent hygiene procedures and closely monitor pollutants is essential for mitigating health risks in densely populated urban environments. As metropolitan areas become increasingly complex, there is a pressing need to prioritize the optimization of these processes.
 Materials and Methods. To address the challenges associated with public health optimization in smart cities, this study introduces Optimized Public Wellness using Machine Learning (OPWML). OPWML employs advanced machine learning techniques to augment hygiene protocols and pollution surveillance in smart urban areas. The proposed approach incorporates real-time validation, enhanced data-collecting efficiency, intelligent intervention impact, and increased throughput. The methodology aims to streamline processes and overcome the limitations of current approaches, providing more precise and prompt outcomes.
 Results. Simulation findings demonstrate the superior performance of OPWML compared to other methods. The average estimate accuracy achieved by OPWML is 86.76%, showcasing its efficacy in delivering accurate results. Real-time validation latency is notably low at 12.99 ms, indicating the system’s responsiveness. With a data collection efficiency of 22.96 GB/hour, OPWML demonstrates its ability to efficiently gather relevant data. The smart intervention impact of 33.20% underscores the system’s effectiveness in implementing intelligent interventions. Additionally, the throughput of 314.67 kbps signifies the high processing capacity of OPWML.
 Limitations. While OPWML exhibits promising results, it is essential to acknowledge certain limitations in this study. The simulation-based nature of the findings may not fully capture real-world complexities. Additionally, the generalizability of the results to diverse urban contexts requires further investigation. Limitations such as data privacy concerns and potential technological barriers should also be considered when implementing OPWML in practical settings.
 Conclusion. In conclusion, Optimized Public Wellness using Machine Learning (OPWML) emerges as a powerful tool for transforming public health processes in smart cities. The study highlights OPWML’s capacity to significantly enhance hygiene protocols and pollution surveillance, ensuring a healthier and environmentally sustainable urban setting. While acknowledging certain study limitations, the overall outcomes emphasize the potential of OPWML in revolutionizing public health practices and contributing to the well-being of urban populations in the era of smart cities.

3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment.

In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and DenseNet, further enhanced by robot-assisted technology to improve the precision and efficiency of data collection. The GANs in our model are responsible for generating highly realistic and diverse 3D human motion data, while SVM aids in the effective classification of this data. DenseNet is utilized for the extraction of key features, facilitating a comprehensive and integrated approach that significantly elevates both the data augmentation process and the model's ability to process and analyze complex human movements. The experimental outcomes underscore our model's exceptional performance in motion quality assessment, showcasing a substantial improvement over traditional methods in terms of classification accuracy and data processing efficiency. These results validate the effectiveness of our integrated network model, setting a solid foundation for future advancements in the field. Our research not only introduces innovative methodologies for 3D human pose data enhancement but also provides substantial technical support for practical applications across various domains, including sports science, rehabilitation medicine, and virtual reality. By combining advanced algorithmic strategies with robotic technologies, our work addresses key challenges in data augmentation and motion quality assessment, paving the way for new research and development opportunities in these critical areas.

Machine Learning in Financial Transaction Fraud Detection and Prevention

In this paper, we delve into the application of machine learning technology in financial transaction fraud detection and prevention, including how it works, real-world examples, the challenges it faces, and its potential impact on the future of the financial security space. As the digitalisation process of the financial industry accelerates, the means of financial transaction fraud are becoming increasingly complex and varied, bringing great risks to individuals, enterprises and even the entire financial system. In this context, traditional fraud detection methods are increasingly difficult to effectively respond to emerging frauds due to their inherent limitations. In contrast, machine learning, with its powerful data processing capability, complex pattern recognition ability, and self-learning and adaptation, is considered a powerful tool against financial transaction fraud. Through real-world case studies, we demonstrate how the application of machine learning techniques in financial fraud detection and prevention has helped financial institutions improve detection efficiency and accuracy. Covering a wide range of financial fraud types from credit card fraud to account hijacking to money laundering, these cases illustrate how machine learning models can play a role in monitoring transaction activities in real time, identifying unusual behaviours, and adapting to new fraudulent tactics. In addition, we also discuss the main challenges faced when implementing machine learning techniques, including data quality and privacy protection, model interpretability, the cost of implementing the technology, and integration issues with existing systems. While machine learning shows great potential for financial transaction fraud detection and prevention, there are a number of technical and practical challenges that need to be overcome in order to take full advantage of its benefits. These include improving the efficiency of data collection and processing, ensuring transparency and interpretability of models, reducing the cost of technology implementation and enhancing cross-industry collaboration. In the future, as machine learning and related technologies continue to advance, and as financial institutions and regulators gain a deeper understanding of these technologies, it is reasonable to believe that machine learning will play an even more important role in the financial security field, contributing to the building of a safer, more efficient and smarter financial system. The goal of this paper is to provide financial institutions, technology developers, and policy makers with a comprehensive view of the application of machine learning in financial transaction fraud detection and prevention, pointing out future directions and potential strategies by analysing its advantages, challenges, and practical examples. Through this research, we hope to facilitate broader industry discussions, promote innovation and development of financial security technologies, and jointly address the challenges of financial fraud.

Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.

Beyond the Clinic Walls: Examining Radiology Technicians' Experiences in Home-Based Radiography.

In recent years, the landscape of diagnostic imaging has undergone a significant transformation with the emergence of home radiology, challenging the traditional paradigm. This shift, bringing diagnostic imaging directly to patients, has gained momentum and has been further accelerated by the global COVID-19 pandemic, highlighting the increasing importance and convenience of decentralized healthcare services. This study aims to offer a nuanced understanding of the attitudes and experiences influencing the integration of in-home radiography into contemporary healthcare practices. The research methodology involves a survey administered through Computer-Aided Web Interviewing (CAWI) tools, enabling real-time engagement with a diverse cohort of medical radiology technicians in the health domain. A second CAWI tool is submitted to experts to assess their feedback on the methodology. The survey explores key themes, including perceived advantages and challenges associated with domiciliary imaging, its impact on patient care, and the technological intricacies specific to conducting radiologic procedures outside the conventional clinical environment. Findings from a sample of 26 medical radiology technicians (drawn from a larger pool of 186 respondents) highlight a spectrum of opinions and constructive feedback. Enthusiasm is evident for the potential of domiciliary imaging to enhance patient convenience and provide a more patient-centric approach to healthcare. Simultaneously, this study suggests areas of intervention to improve the diffusion of home-based radiology. The methodology based on CAWI tools proves instrumental in the efficiency and depth of data collection, as evaluated by 16 experts from diverse professional backgrounds. The dynamic and responsive nature of this approach allows for a more allocated exploration of technicians' opinions, contributing to a comprehensive understanding of the evolving landscape of medical imaging services. Emphasis is placed on the need for national and international initiatives in the field, supported by scientific societies, to further explore the evolving landscape of teleradiology and the integration of artificial intelligence in radiology. This study encourages expansion involving other key figures in this practice, including, naturally, medical radiologists, general practitioners, medical physicists, and other stakeholders.

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.

Supplementing a Paper Questionnaire with Web and Two-way Short Message Service (SMS) Surveys

Abstract When deciding which modes to offer, researchers consider cost, known respondent contact information, and potential mode effects. For a short survey on employment, we evaluate the effect of adding one of two new electronic data collection modes to a mailed questionnaire. We sent a survey to principals who previously responded to the National Center for Education Statistics’ (NCES) National Teacher and Principal Survey (NTPS) asking about their current job status. This questionnaire, known as the Principal Follow-up Survey (PFS), has typically been administered as a short paper form that is mailed to NTPS respondents. In 2022, the PFS introduced two new modes of completion, and principals were randomly assigned to receive: (i) a paper form only; (ii) a paper form, as well as emails with a direct link to complete a web survey; or (iii) a paper form, as well as invitations by text message to complete an automated two-way short message service text survey by responding to texted “yes/no” questions. This article compares overall response rates and time-to-response by mode to determine respondent preferences for completing short surveys. Adding either electronic mode significantly increased response rates and decreased the number of days in which completed surveys were received, compared to offering only a paper questionnaire. Although email and text messages are both forms of electronic communication that may be accessible on a smartphone, the added text message survey resulted in higher response rates than the added web survey. This suggests that respondents interact differently with emails and text messages they receive and that offering an option to complete a survey by text message can increase the speed and efficiency of data collection for short surveys.