Goal Of Data Collection Research Articles

A guide for construction practitioners to integrate robotic systems in their construction applications

Introduction: The use of robots can boost productivity and reduce costs in construction projects. However, choosing the right robotic platform for the right application can be challenging, costly, and time-consuming. This paper presents a guide for researchers and construction practitioners interested in using robotic systems.Methods: A methodology covering the different aspects to be considered when it comes to a robotic platform, such as the framework (both hardware and software), the environment the robot is going to be operating, the level of supervision (i.e., autonomy) the robot requires, different hardware required on board (i.e., sensors and computers), and the control strategies and systems and communication network, is presented. The methodology is implemented with a practical application where a semi-autonomous robotic system is designed and developed with the simple goal of data collection on construction sites, making sure that all the steps covered in the methodology are addressed.Results: The results show that the methodology is applicable to a wide range of solutions, with a focus on the development of the platform itself and not the final application.Discussion: This guide is meant to assist in developing a flexible open platform that can be customized to the specific requirements needed.

SignIt! An Android game for sign bilingual play that collects labelled sign language data

The Deaf or Hard-of-Hearing (DHH) community constitutes over 430 million people globally, with about 70 million of them using sign language as their primary means of communication. India has around 63 million DHH individuals. The DHH community in India faces several challenges, particularly in learning sign language and English, due to delayed diagnosis, stigma, oralism, and a diversity of languages. Digital games for spoken and sign language learning have gained popularity due to their advantages over traditional language learning methods, such as enhanced engagement and socialization, driving increased research and adoption over traditional methods. Moreover, the development of robust machine learning models for sign language recognition, which could significantly improve access for signers, is currently impeded by the scarcity of labelled sign language data. To address these challenges, we collaborated with NISH, an academic institution for the DHH community and developed SignIt!, an accessible and inclusive quiz platform that facilitates the learning of English and Indian Sign Language (ISL) with a secondary goal of data collection. To assess the game's usability, we conducted a study with 20 members of the DHH community, followed by interviewing 15 participants. Overall, our participants answered 2160 quiz questions and created 210 questions. The quiz creation resulted in the collection of three hours of labelled real-world sign language data. The interviews revealed novel insights, such as a preference for playing competitively with friends, empowerment by their agency to be content creators, and early signs of learning English, sign language, and quiz content by playing and creating quizzes. We plan to open-source and release SignIt! to increase its adoption among diverse DHH communities.

Variation in identifying children and adolescents with disability and developmental disability in population-based public health surveys

BackgroundThe term “developmental disability” (DD) is inconsistently defined and applied depending on purpose and across sources, including in legislation. ObjectiveThis project aimed to identify existing definitions of disability and DD and to determine the extent to which each definition could be operationalized to produce prevalence estimates using data from U.S. national surveys. MethodsUsing data among children <18 years from the 2016–2018 National Health Interview Survey (NHIS) and National Survey of Children's Health (NSCH), we estimated the prevalence of two definitions of disability (Washington Group Short Set on Functioning, American Community Survey) and seven definitions of DD [Health and Human Services (ever/current), Developmental Disabilities Assistance and Bill of Rights Act of 2000 (1+, 2+, or 3+ components), and Diagnostic and Statistical Manual of Mental Disorders, 5th ed (ever/current)]. Complex sample design variables and weights were used to calculate nationally representative prevalence. ResultsDisability (NHIS: 5.2–6.3%; NSCH: 9.2–11.9%) and DD prevalence (NHIS: 0.6–18.0% and NSCH: 0.2–22.2%) varied depending on the definition and data source. For the same definition, NSCH prevalence estimates tended to be higher than NHIS estimates. ConclusionsThe substantial variability in estimated prevalence of disability and DD among children in the United States may be in part due to the surveys not representing all components of each definition. Different or additional questions in national surveys may better capture existing definitions of disability and DD. Considering the data collection goals may help determine the optimal definition to provide useful information for public health action.

Using a CBPR Approach to Guide Successful Recruitment for an Online Questionnaire: The Measurement Approaches to Partnership Success (MAPS) Case Study.

The Measurement Approaches to Partnership Success (MAPS) study team effectively used a community-based participatory research (CBPR) approach to recruit 55 long-standing CBPR partnerships to participate in an online questionnaire to assess factors associated with partnership success. Our recruitment was guided by interconnected values of collaboration, transparency, and relationship-building to maintain fidelity to CBPR principles throughout the process. We operationalized these values into a series of strategies to recruit partnerships and sustain their involvement, including establishing primary points of contact, offering incentives for completion, personalizing recruitment materials, and practicing flexibility in our approach. We aim to inform public health researchers on the strategies that enabled our team to achieve 100% of our study recruitment goal, with the intent that our recommendations can be applied by others to enhance their recruitment efforts and reach their data collection goals for future public health research.

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

Intersectionality and Invisible Victims: Reflections on Data Challenges and Vicarious Trauma in Femicide, Family and Intimate Partner Homicide Research.

Rigorous, comprehensive and timely research are the cornerstone of social and transformative change. For researchers responding to femicide, family and intimate partner homicide, there are substantial challenges around accessing robust data that is complete and fully representative of the experiences and social identities of those affected. This raises questions of how certain social identities are privileged and how the lens of intersectionality may be constrained or enabled through research. Further, there is limited insight into the emotional labour and safety for researchers, and how they experience and mitigate vicarious trauma. We examine these issues through a shared critical reflection and conclude with key recommendations to address the challenges and issues identified. Four researchers examining and responding to femicide, family and intimate partner homicide in Australia, Canada and the United Kingdom shared and evaluated their critical reflection. We drew on our experiences and offer insights into processes, impacts and unintended consequences of fatality reviews and research initiatives. There are substantial limitations in accessibility and completeness of data, which has unintended consequences for the construction of social identities of those affected, including how multiple forms of exclusion and structural oppression are represented. Our experiences as researchers are complex and have driven us to implement strategies to mitigate vicarious trauma. We assert that these issues can be addressed by reconceptualizing the goals of data collection and fostering collaborative discussions among those involved in data collection and violence prevention to strengthen research, prevention efforts and safety for all involved.

Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning

Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning methods. We propose a multi-agent reinforcement learning (MARL) approach that, in contrast to previous work, can adapt to profound changes in the scenario parameters defining the data harvesting mission, such as the number of deployed UAVs, number, position and data amount of IoT devices, or the maximum flying time, without the need to perform expensive recomputations or relearn control policies. We formulate the path planning problem for a cooperative, non-communicating, and homogeneous team of UAVs tasked with maximizing collected data from distributed IoT sensor nodes subject to flying time and collision avoidance constraints. The path planning problem is translated into a decentralized partially observable Markov decision process (Dec-POMDP), which we solve through a deep reinforcement learning (DRL) approach, approximating the optimal UAV control policy without prior knowledge of the challenging wireless channel characteristics in dense urban environments. By exploiting a combination of centered global and local map representations of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large complex environments and state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints. Finally, learning a control policy that generalizes over the scenario parameter space enables us to analyze the influence of individual parameters on collection performance and provide some intuition about system-level benefits.

Personal Narrative Skills of Greek Children of Typical Development in the Age of 10 Years

The personal narrative constitutes a revival of past events and a preparation procedure of upcoming ones, developing a person’s self-concept. The purpose of the current research was to study Greek children’s personal narratives. Participants of the survey were 20 ten-year-old children, including 10 boys and 10 girls of typical development, where each one of them was asked to narrate 6 of their life events. The goal of data collection and analysis was to study the microstructure and macrostructure, as well as evaluate the potential results and investigate the impact of gender on them. The outcome numbers describe the children’s performances in scales being used. Important findings of the research were the absence of differences between the two genders. All in all, the results reveal a tendency of that age children’s personal narrative characteristics.

4X: A Hybrid Approach for Scaffolding Data Collection and Interest in Low-Effort Participatory Sensing

Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data. This paper explores a new, hybrid approach, in which collected data-even if low-fidelity initially-can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users' routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.

Researching the Appropriateness of Care in the Complementary and Integrative Health Professions: Part I

ObjectivesThe purpose of this article is to report on the Center of Excellence for Research on Complementary and Alternative Medicine at RAND Corporation. The overall project examined the appropriateness of chiropractic spinal manipulation and mobilization for chronic low back pain and chronic cervical pain using the RAND and University of California Los Angeles Appropriateness Method, including patient preferences and costs, to acknowledge the importance of patient-centered care in clinical decision-making. MethodsThis article is a narrative summary of the overall project and its inter-related components (ie, 4 Research Project Grants and 2 centers), including the Data Collection Core, whose activities and learning will be the subject of a following series of methods articles. ResultsThe project team faced many challenges in accomplishing data collection goals. The processes we developed to overcome barriers may be of use to other researchers and for practitioners who may want to participate in such studies in complementary and integrative health, which previously was known as complementary and alternative medicine. ConclusionFor this large, complex, successful project, we gathered online survey data, collected charts, and abstracted chart data from thousands of chiropractic patients. The present article delineates the challenges and lessons that were learned during this project so that others may gain from the authors’ experience. This information may be of use to future research that collects data from independent practitioners and their patients because it provides what is needed to be successful in such studies and may encourage participation.

Near-Optimal Incentive Allocation for Piggyback Crowdsensing

Piggyback crowdsensing (PCS) is a novel energy- efficient mobile crowdsensing paradigm that reduces the energy consumption of crowdsensing tasks by leveraging smartphone app opportunities (SAOs). This article, based on several fundamental assumptions of incentive payment for PCS task participation and spatial-temporal coverage assessment for collected sensor data, first proposes two alternating data collection goals. Goal 1 is maximizing overall spatial-temporal coverage under a predefined incentive budget constraint; goal 2 is minimizing total incentive payment while ensuring predefined spatial-temporal coverage for collected sensor data, all on top of the PCS task model. With all of the above assumptions, settings, and models, we introduce CrowdMind -- a generic incentive allocation framework for the two optimal data collection goals, on top of the PCS model. We evaluated CrowdMind extensively using a large-scale real-world SAO dataset for the two incentive allocation problems. The results demonstrate that compared to baseline algorithms, CrowdMind achieves better spatial-temporal coverage under the same incentive budget constraint, while costing less in total incentive payments and ensuring the same spatial-temporal coverage, under various coverage/incentive settings. Further, a short theoretical analysis is presented to analyze the performance of Crowd- Mind in terms of the optimization with total incentive cost and overall spatial-temporal coverage objectives/constraints.

&lt;italic&gt;iCrowd&lt;/italic&gt;: Near-Optimal Task Allocation for Piggyback Crowdsensing

This paper first defines a novel spatial-temporal coverage metric, $k$ -depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd , a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and $k$ -depth coverage objectives/constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals—i.e., Goal. 1 near-maximal $k$ -depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined $k$ -depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher $k$ -depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same $k$ -depth coverage constraint.

Remote sensing for Marine Spatial Planning and Integrated Coastal Areas Management: Achievements, challenges, opportunities and future prospects

This paper addresses the past and current uses of remote sensing technologies that are supporting Marine Spatial Planning (MSP) and Integrated Coastal Area Management (ICAM). Satellite and airborne remote sensing have a key role to play in studying the marine and coastal environment. The paper introduces the characteristics of remote sensing systems of interest for studying the oceans and coastal ecosystems. Secondly, a conceptual framework is defined which relates all important components of ICAM/MSP: 1) Ecosystem health and pollution, 2) Natural (coastal) hazards, 3) Marine Space and Use, 4) Coastal land cover and use, 5) population (dynamics), with their respective data collection goals and the most appropriate state-of-the-art sensor technologies to study them, summed up in a comprehensive table. A summary of achievements of remote sensing in each component of ICAM and MSP is given, with a particular interest for developing countries where their implementation is made difficult by several technical and governance issues. Opportunities are also presented to nuance those challenges in the form of programs and initiatives to increase capacity and resources to exploit RS in a MSP/ICAM context, but also to facilitate RS data accessibility and usability. Finally, future satellite missions of particular interest for ICAM and MSP are introduced. Overall, the word “Integrated” in ICAM suggests that a multidisciplinary approach is needed to understand the dynamics of marine and coastal environments, and remote sensing is identified as a piece of the puzzle which coastal and ocean managers should not hesitate to integrate in their practices. This paper acknowledges the need for more in-depth understanding of the underlying structures and ecological functioning of ecosystems, their habitats and their species, before RS can become a truly reliable tool in biophysical variable monitoring.

Ecosystem monitoring for ecosystem‐based management: using a polycentric approach to balance information trade‐offs

Ecosystem monitoring for ecosystem‐based management: using a polycentric approach to balance information trade‐offs

BIOMAS: A bio-economic modelling and assessment system for fisheries management strategy evaluation

The primary goal of data collection on exploited fish stocks is to enable the development of credible stock assessments. Ideally, enough information will be collected to enable management strategy evaluations on the stock, providing guidance on future management decisions. In many fisheries management agencies, however, the ability to rapidly and cost-effectively build and run models to evaluate management strategies is as much an impediment to their development as the lack of data.The BIOMAS software provides users with the ability to rapidly develop single or multi-species, length-based population dynamics models for the exploration of alternative management strategies in a Bayesian-based framework. The system is theoretically capable of incorporating any number of species, fishing fleets and spatial compartments in a single model. Further complexity is provided with the ability to implement management and environmental effects on stocks as well as the tracking of economic costs and revenue from fishing activity. However, in practice model complexity will be limited by the needs of the modelling project, by the availability of data and by the modelling project timeline.By incorporating Bayesian methods, including prior probability distributions, the BIOMAS system enables users to explore the uncertainty surrounding a limited number of parameter values and to incorporate this uncertainty into the model results. Sensitivity analyses can be run against virtually all model parameters providing insight into the key drivers for the model and likely candidates for Bayesian priors. A number of management indicators are provided for all stocks and fleets in the model including stock depletion, catch weights, age- and length-frequencies, discard mortality estimates and fleet profits – each with Bayesian-based confidence intervals. Such indicators become the primary means of comparing the effectiveness of alternative management strategies when using the system forecasting capabilities. Through the inclusion of stochastic processes, such as process error and environmental and pricing fluctuations, each management strategy can also be evaluated for its robustness to such uncertainty.The ability to reflect more of our uncertainty in modelled outcomes, which tools such as BIOMAS provide, can unfortunately come at the expense of clarity in the outcomes. This is only because these models better encompass our lack of knowledge about fishery systems. Fortunately, such modelling tools can also provide us with a clearer understanding of the main sources of this uncertainty. Furthermore, the unique design of the BIOMAS system allows users to undertake ‘research strategy evaluations’ in which the costs and benefits of different forms of research can be compared, allowing managers and research leaders to see more clearly the benefits of targeted research to reduce these uncertainties.

Accounting for complex entities: implications for ARMS

PurposeThe purpose of this research is to examine the effect of accounting for complex organizational forms on data collection with the Agricultural Resource Management Survey (ARMS).Design/methodology/approachThis research reviews the literature from accounting theory along with the goals of data collection for policy analysis to draw conclusions about the applicability of accounting pronouncements.FindingsHistorically, the financial data collected in ARMS were based on financial accounting standards which were adequate for most purposes. However, this study develops the fact that many of these financial accounting standards were created to provide information for equity market transactions. The complexities of accounting for consolidations will provide valuable information, but implementing these standards will require accounting sophistication that is not prevalent in agriculture.Originality/valueBy drawing accounting theory together with the targeted use of data, this study offers guidelines to improve the data quality for a growing complex US agriculture.

A Note on Opportunism and Parsimony in Data Collection

ABSTRACT Out of precaution, opportunism, and a general tendency towards thoroughness, researchers studying wildlife often collect multiple, sometimes highly correlated measurements or samples. Although such redundancy has its benefits in terms of quality control, increased resolution, and unforeseen future utility, it also comes at a cost if animal welfare (e.g., duration of handling) or time and resource limitation are a concern. Using principle components analysis and bootstrapping, we analyzed sets of morphometric measurements collected on 171 brown bears in Sweden during a long‐term monitoring study (1984–2006). We show that of 11 measurements, 7 were so similar in terms of their predictive power for an overall size index that each individual measurement provided little additional information. We argue that when multiple research objectives or data collection goals compete for a limited amount of time or resources, it is advisable to critically evaluate the amount of additional information contributed by extra measurements. We recommend that wildlife researchers look critically at the data they collect not just in terms of quality but also in terms of need.

Novel method for the evaluation of data quality based on fuzzy control

One of the goals of data collection is preparing for decision-making, so high quality requirement must be satisfied. Rational evaluation of data quality is an effective way to identify data problem in time, and the quality of data after this evaluation is satisfactory with the requirement of decision maker. A fuzzy neural network based research method of data quality evaluation is proposed. First, the criteria for the evaluation of data quality are selected to construct the fuzzy sets of evaluating grades, and then by using the learning ability of NN, the objective evaluation of membership is carried out, which can be used for the effective evaluation of data quality. This research has been used in the platform of ‘data report of national compulsory education outlay guarantee’ from the Chinese Ministry of Education. This method can be used for the effective evaluation of data quality worldwide, and the data quality situation can be found out more completely, objectively, and in better time by using the method.

Confidentiality, consent and autonomy in the physician-patient relationship.

In the practice of medicine there has long been a conflict between patient management and respect for patient autonomy. In recent years this conflict has taken on a new form as patient management has increasingly been shifted from physicians to insurers, employers, and health care bureaucracies. The consequence has been a diminishment of both physician and patient autonomy and a parallel diminishment of medical record confidentiality. Although the new managers pay lip service to the rights of patients to confidentiality of their records, in fact they advocate very liberal medical records access policies. They argue that a wide range of parties has a need to know the contents of individually identifiable medical records in order to control costs, promote quality of care, and undertake research in the public interest. Broad interpretations of the need to know, however, are at odds with strict interpretations of the right to confidentiality. Strict confidentiality policies require that, with few exceptions, patient consent be obtained whenever a patient's record is used outside the treatment context. The traditional criterion for overriding the consent requirement has been that without the override some harm would directly result. This rule is now challenged by the claim that patients have a duty to make their records available for a wide range of research and public health purposes. The longstanding tension between physician responsibility for patient welfare and respect for patient autonomy is being replaced by a debatable requirement that both physician and patient autonomy be subordinated to the goals of data collection and analysis.

Soil remediation decision making in presence of uncertainty in crop yield response

Compacted soil has a characteristic “hard pan” layer that impedes root penetration resulting in reduced crop yield. Soil remediation measures such as deep ripping would improve yield; however, such remediation is relatively expensive. We show how the economic benefit of remediation that is, the expected increase in crop yield can be assessed in the presence of sparse sampling. Geostatistical tools are used for uncertainty characterization. Remediation is recommended when the discounted expected profit due to increased yield exceeds the cost of remediation. A 320×200 m field near Leduc, Alberta, Canada was extensively studied. A penetrometer was used to measure the depth of the compacted soil layer. Elevation and soil characteristics were recorded in 1997 while yield was measured for 3 consecutive years. The goals of data collection were to (1) predict the yield response of remediation of compact soil, and (2) develop an economic model for cost/benefit analysis of remediation in the presence of sparse data. Multivariate statistics combined with geostatistical simulation techniques are used to build maps of all needed variables: elevation, depth to compacted soil, and the local soil landform class. The critical relationship between yield response and remediation is established from the historical yield data. This relationship depends on elevation and local soil landform class. Since deep ripping is expensive, determining the precise areas to remediate can result in substantial savings. An economic model is used with the geostatistical maps to quantify risk and the expected benefit of remediation.