Data Collection Applications Research Articles

Application of the Open Data Kit for Data Collection Presence of Primary School Teachers

The Open Data Kit (ODK) originated as a google.org initiative where research efforts were focused on applying technology to assist developing countries in the health and environment sectors. ODK is an open-source tool that helps organizations, writers / researchers, field workers, and manage mobile data collection solutions. Its goal is to create open-source and standards-based tools that are easy to try, easy to use, easy to modify and easy to measure.
 This research was conducted to help teachers and Korwil Operators to collect and manage teacher attendance data at each elementary school in Ciater sub-district by utilizing the Open Data Kit as a medium for collecting and managing primary school teacher attendance data. This research was conducted by conducting literature studies, interviews and observations, and studying the use of the Open Data Kit. Furthermore, the stages will be carried out to define the needs of analysis, design and implementation.
 The final result of this research is a presence data collection application that utilizes the Open Data Kit as a medium consisting of ODK Collect as a data collection tool and ODK Aggregate as a tool for managing data.

Smartphone-Based Data Collection in Ophthalmology.

Due to their widespread use among the population and their wide range of functions and sensors, smartphones are suitable for data collection for medical purposes. App-supported input masks, patient diaries, and patient information systems, mobile access to the patient file as well as telemedical services will continue to find their way into our field of expertise in the future. In addition, the use of smartphone sensors (GPS and motion sensors, touch display, microphone) and coupling possibilities with biosensors (for example with Continuous Glucose Monitoring [CGM] systems), advanced camera technology, the possibility of regular and appointment independent checking of the visual system (visual acuity/contrast vision) as well as real-time data transfer offer interesting possibilities for patient treatment and clinical research. The present review deals with the current status and future perspectives of smartphone-based data collection and possible applications in ophthalmology.

A Pedestrian Network Construction System Based on Crowdsourced Walking Trajectories

With the promotion of low-carbon travel, pedestrian network plays an important role in many location-based applications, such as pedestrian navigation and refined traffic management. Due to the lack of systematic data acquisition mechanics, the accuracies and detail levels of pedestrian network data are hardly capable of satisfying the demands of such transportation applications. Presently, various mobile phone apps recorded and stored users’ movement trajectories, which provide a valuable data source for pedestrian network construction. Hence, this article proposes a crowdsourcing-based system for generating pedestrian network that encompasses three key components of crowdsourced walking trajectory data filtering, pedestrian network construction and evaluation of pedestrian network. Self-collected data and open platform data were used to evaluate the proposed system. Experimental results demonstrate that the proposed method can accurately and completely extract pedestrian network. Moreover, the pedestrian network can be updated in a timely manner by the proposed method. The data collection application and the collected data are available to the public.

IDENTIFYING USERS’ REQUIREMENTS OF GEOGRAPHIC INFORMATION FOR DISASTER RISK MANAGEMENT IN THE DOMINICAN REPUBLIC

Abstract. In recent years, the growth of public available geographic information and location-based services has been enabling more stakeholders from diverse backgrounds to participate in generating and sharing a comprehensive view of the territory to reduce the impact of severe phenomena in the communities. With the prediction of more disastrous phenomena in the Caribbean region, understanding of what and how to be prepared beforehand to meet users’ needs from different sectors should facilitate to react quickly and take full advantage of geospatial technology and resources to support disaster managers and citizens. This paper is mainly focused on the identification of users’ requirements of geographic information and services for disaster risk management (DRM) in the Dominican Republic. The results are built upon an online survey targeted to expert and non-expert users that intervene in the National System of Prevention, Mitigation and Response (SN-PMR, in Spanish). Our findings revealed seven major users’ requirements for DRM: (1) policy for sharing geo-information; (2) implementing a disaster-oriented SDI; (3) technical standards for real-time data collection; (4) simplified procedures for gathering and accessing of metadata; (5) mobile applications (App) for data collection and alerts visualization; (6) more capacity building programs; and, (7) closer community participation using social networks. This knowledge will contribute to a superior level of readiness to prevent future disasters in Dominican Republic and to support potential studies/practices in the Caribbean region and other Small Island Developing States in the World, which share similar challenges in terms of natural hazards and development issues.

Workflow Analysis Driven Recommendations for Integration of Electronically-Enhanced Sexually Transmitted Infection Screening Tools in Pediatric Emergency Departments.

Adolescents are disproportionately affected by sexually transmitted infections (STIs). Failure to diagnose and treat STIs in a timely manner may result in serious sequelae. Adolescents frequently access the emergency department (ED) for care. Although ED-based STI screening is acceptable to both patients and clinicians, understanding how best to implement STI screening processes into the ED clinical workflow without compromising patient safety or efficiency is critical. The objective of this study was to conduct direct observations documenting current workflow processes and tasks during patient visits at six Pediatric Emergency Care Applied Research Network (PECARN) EDs for site-specific integration of STI electronically-enhanced screening processes. Workflow observations were captured via TaskTracker, a time and motion electronic data collection application that allows researchers to categorize general work processes and record multitasking by providing a timestamp of when tasks began and ended. Workflow was captured during 118 patient visits across six PECARN EDs. The average time to initial assessment by the most senior provider was 76min (range 59-106min, SD = 43min). Care teams were consistent across sites, and included attending physicians, advanced practice providers, nurses, registration clerks, technicians, and students. A timeline belt comparison was performed. Across most sites, the most promising implementation of a STI screening tool was in the patient examination room following the initial patient assessment by the nurse.

Research Needs, Challenges, and Strategic Approaches for Natural Hazards and Disaster Reconnaissance

Natural hazards and disaster reconnaissance investigations have provided many lessons for the research and practice communities and have greatly improved our scientific understanding of extreme events. Yet, many challenges remain for these communities, including improving our ability to model hazards, make decisions in the face of uncertainty, enhance community resilience, and mitigate risk. State-of-the-art instrumentation and mobile data collection applications have significantly advanced the ability of field investigation teams to capture quickly perishable data in post-disaster settings. The NHERI RAPID Facility convened a community workshop of experts in the professional, government, and academic sectors to determine reconnaissance data needs and opportunities, and to identify the broader challenges facing the reconnaissance community that hinder data collection and use. Participants highlighted that field teams face many practical and operational challenges before and during reconnaissance investigations, including logistics concerns, safety issues, emotional trauma, and after-returning, issues with data processing and analysis. Field teams have executed many effective missions. Among the factors contributing to successful reconnaissance are having local contacts, effective teamwork, and pre-event training. Continued progress in natural hazard reconnaissance requires adaptation of new, strategic approaches that acquire and integrate data over a range of temporal, spatial, and social scales across disciplines.

Using Electronic Data Capture for Cardiovascular Electrophysiology Invasive Procedures: An Important Step Towards Interoperable Clinical Registries

Introduction: The capture and integration of structured data from point of care into clinical registries has been a challenge. However, this effort is very important toward a qualitative patient care and research. Collection, organization and interpretation of clinical data can help to improve evidence-based medicine practices. Worksheets data capture are prevalent, but, not flexible, protected, workflow pleasant, and user friendly and do not support the creation of standardized and interoperable data. The aim of this study was to design and implement an Electronic Data Capture (EDC) instruments to be use in context of cardiovascular invasive procedures.Material and Methods: This descriptive and developmental study conducted in three phases as follows. (1) data standardization according national and international data element templates published by specialized societies; (2) developing of an initial data collection and clinical research workflow (3), establishing of electronic case reports using Research Electronic Data Capture (REDCap) in accordance with the Health Insurance Portability and Accountability Act (HIPAA) privacy rule.Results: Three case report forms was developed that included demographics, medical history, physical examination, laboratory tests, radiology examinations, Electrophysiology (EP) procedures, as well as medications and follow-up information. Data-entry validation criteria have been implementing in electronic data collection instrument to assure validity and precision when data enter in electronic form.Conclusion: This paper describes the process used to create an EDC application. Data collection applications were successfully develop as an a priori step in a clinical research for assisting data collection and management in a case of cardiovascular EP invasive procedures.

Observational Bias Within Hospital-Wide Hand Hygiene Program

Background: Hand hygiene (HH) is critical to prevent hospital-acquired infections. Running a successful HH program requires valid and accurate HH data to monitor the status and progress of HH improvement efforts. HH data are frequently subject to variable forms of bias, for which considerations must be made to enhance the validity of HH data. Objective: We assessed the extent to which observers may be prone to report more favorable HH rates when observing healthcare workers from the same professional group versus members of other job categories. Methods: We analyzed HH data from 48,543 electronically collected observations conducted by frontline healthcare workers in a 793-bed acute-care hospital from January 1, 2019, through July 31, 2019. All auditors received training on HH observations and proper use of the data collection application. Compliance data were sorted into peer versus nonpeer observations by profession. We compared HH compliance rates for members of each professional group when monitoring peers versus nonpeers. We further stratified results by ancillary professions (central transport, unit associates, food services, pharmacy, phlebotomy, rehabilitation services, and respiratory therapy) versus nonancillary professions (doctors, nurses, physician assistants, patient care assistants). Results: Of 12,488 ancillary observations, 7,184 (57.5%) were peer observations and 36,055 were nonancillary observations, of which 15,942 (44.2%) were peer observations. The percentage of peer-to-peer observations versus nonpeer observations varied by profession, ranging from 96% of central transport workers and 91% of environmental services observations to 21% of patient care assistants and 34% of physician’s assistants. Average compliance rates for peer versus nonpeer observations in ancillary groups were 98% (95% CI, 98.7%–99.2%) versus 83% (95% CI, 82.5%–84.5%). Average compliance rates nonancillary groups were 92% (95% CI, 92.0%–92.8%) for peers versus 88% (95% CI, 87.8%–88.7%) for nonpeers (Table 1). Conclusions: We documented a propensity for some categories of healthcare workers to record discrepant rates of HH compliance when observing members of the same peer group versus others. This effect was more pronounced amongst ancillary versus nonancillary services. This study adds to the literature of potential sources of bias in HH monitoring programs. Operational changes in HH program data collection may be warranted to try to mitigate these biases such as increasing the frequency of validation exercises conducted by nonaffiliated observers, weighting peer versus nonpeer observations differently, or switching to automated electronic monitoring systems.Funding: NoneDisclosures: None

MHealth for Pneumonia Prevention

Abstract Introduction Pneumonia contributes to 14% of under-five mortality in Pakistan. Delayed care seeking is one of the causes of death due to this disease. One of the most impactful strategies can be timely healthcare-seeking through CHW's. We, therefore propose to develop an intervention package for educating caregivers on pneumonia by Lady Health Workers (LHWs). Methodology This first phase of the feasibility study comprised of a systematic review (SR) on best mobile health-based approaches for CHWs on the management of childhood illness followed by formative research (FR). In the systematic review, 6 databases were searched, and 22 articles were included. Based on findings of SR and FR along with guidelines of WHO and UNICEF, a mobile application-based intervention package will be developed in the second phase. Recruited LHWs will use the mobile application in their monthly visits for 6 months. Text and voice messages will be sent to each caregiver. Pre and Post-intervention surveys will be conducted with the recruited caregivers to assess the impact of the intervention and FGDs with the recruited LHWs to get feedback on the intervention. Findings Findings of the systematic review suggest that the most common approaches of mobile health for community health workers is the use of a customized application for disease management and data collection. Findings of formative research suggest that the majority of the caregivers were not aware of the danger signs of pneumonia. Respondents agreed to the importance of an educational intervention package on pneumonia and its prevention by LHWs through mHealth. Acknowledgment This research was commissioned by the National Institute of Health Research using Official Development Assistance (ODA) funding. The views expressed in this publication are those of author(s) and not necessarily those of NHS, the National Institute of Health Research, or the development of Health. Key messages mHealth intervention employing community health workers can be a useful mode of improving healthcare services. Use of technology to raise awareness and delivered health-related information at community level can prove tremendously helpful for illness management.

REFUGEE BASED DATA COLLECTION IN DISASTER RESPONSE

<p>Indonesia's high disaster risk requires an information system that can improve effectiveness in disaster response. Therefore, there is a need for an application that could be a means for various information in disaster response in Indonesia. This research uses the Development Life Cycles or SDLC Software, specified with the Waterfall Model. This model has 4 phases, as following: requirement analysis, system analysis, implementation, verification, and maintenance. From this research, a victim-based data collection application can collect data at the disaster site regularly. It also can replace the previous conventional methods so that it can facilitate the National Disaster Management Authority (BNPB) in carrying out information to the community. The results of this study are the prototype of data collection applications in helping disaster response.</p>

Renewable generation monitoring platform and its applications

Monitoring a large and geographically dispersed solar generation fleet can be a challenging undertaking. At Dominion Energy, there is currently over 1.7 GW of transmission and distribution-connected solar generation. Performing the post-mortem analysis and ensuring the safe and reliable operation on more than 100 solar facilities can take substantial engineering efforts. In this study, a renewable generation data collection platform is presented to address the increasing need for solar generation monitoring. The data platform consists of data collection, event storage, real-time alerts, and data analytics applications. The data analysis system can support various technical aspects in solar integration, including grounding monitoring, inverter fault response and model validation, power quality monitoring, and equipment overvoltage protection. The proposed data platform greatly improves the visibility of solar generation and ensures its successful integration.

Using Data Collection Applications in Early Childhood Settings to Support Behavior Change

Social–emotional development in early childhood (EC) is an important factor to their later development and adjustment. While all young children display unwanted behaviors at some time during development, challenging behaviors that occur across settings and over a period of time should be identified and intervened; thus, data collection efforts need to be efficient and accurate as time can be a barrier. There are currently applications for children in a K–12 setting that can be used to collect data and share positive behavior support plans; however, they may not necessarily be tailored to the EC population. A variety of data collection apps will be discussed as they relate to identifying a behavior to data collection skills. Suggestions will be given on what apps should contain for data collection and how to make them applicable to EC settings as well as collaborating with multiple professionals.

ANALISIS KUALITAS SISTEM APLIKASI DAPODIK PADA KOORDINATOR WILAYAH BIDANG PENDIDIKAN KECAMATAN KUTAWALUYA MENGGUNAKAN MODEL WEBQUAL 4.0

AbstractDapodik application is a web-based data collection application which is one of the main sources in the formulation of government policies. In the Dapodik application, there are some managed data including school data, infrastructure, students, teachers and education personnel. The data or information generated is influenced by the quality of the Dapodik application itself. Therefore this study aims to analyze the quality of the Dapodik application system using the Webqual 4.0 model. This research is quantitative descriptive. From the results of the study showed that the ease of users (usability), the quality of information (information quality) and the quality of service interactions (service interaction) on the Dapodik application has been very well felt by users.Keyword: Dapodik, Webqual, System Quality

Дослідження та реалізація системи моніторингу стану автошляхів

A software package consisting of a mobile application for data collection and a web application for saving and displaying data on damage and the condition of roads was developed. This software will be relevant for use in logistics, navigation systems, utilities and government organizations, and just in everyday life for drivers. During the design and development, alternatives were discovered and analyzed, which made it possible to highlight the functional capabilities that the system should possess. An algorithm has been developed for determining damage to the road surface, which will digitally reflect the degree of damage and mark it on the map using the coordinates of the global positioning system. This algorithm uses sensors available in any modern smartphone, such as an accelerometer, which makes the software widely available.

Achieving secure big data collection based on trust evaluation and true data discovery

Data collection is an important process in the life cycle of big data processing. It is the key part that must be completed first in all kinds of data applications, which determines the results of data analysis and application service quality. However, untrusted data sources and transmission links expose the data collection process to attacks and malicious threats such as counterfeiting, replay, and denial of service, and ultimately lead to untrustworthy data. In order to cope with the threat of data collection process and ensure data quality, this paper proposes trust evaluation scheme for data security collection based on wireless sensor network, one of the data collection applications, including direct trust, recommendation trust, link trust, and backhaul trust. Meanwhile, in order to realize the dynamic update of the trust of the data sources, a true data discovery and trust dynamic update mechanism based on ω-FCM (Weight Fuzzy C-Mean) algorithm is proposed. The results of a large number of simulation experiments show that the proposed scheme, model and algorithm can effectively evaluate the trust of data sources and ensure the authenticity of the collected data.

O clã Bolsonaro e o Twitter: comunicação política e influência na rede social

Esta pesquisa investiga o uso do Twitter pelo presidente eleito Jair Bolsonaro e seus três filhos que também seguem uma carreira política: Carlos, Eduardo e Flávio Bolsonaro, que parte dos mídia brasileiros denomina “clã Bolsonaro”. Conforme verificado a narrativa divulgada nas redes sociais pelo clã Bolsonaro seja distinta em ano de campanha eleitoral. O objetivo é saber como o clã Bolsonaro utilizou o Twitter como instrumento de comunicação política em 2017 e 2018. O recorte temporal desta pesquisa é a partir de 1 de janeiro de 2017 até 3 de novembro de 2018. A metodologia de pesquisa é quantitativa e análise de conteúdo com utilização do aplicativo Twitonomy para coleta dos dados. Os resultados demonstram que há um comportamento distinto no discurso dos políticos da família Bolsonaro no ano político de 2017, priorizando em campanha negativa, para o ano eleitoral de 2018, onde predomina a construção da imagem dos candidatos.

Mobile Application Development and Testing for Work Zone Activity Real-Time Data Collection

Work zones are prevalent in the United States as the infrastructure is increasingly in need of maintenance. Lack of reliable data is one of the main obstacles in work zone research. Reliability suffers because of underreporting of crashes and inclusion in the analysis of irrelevant activities that are not attributable to work zones. In addition, the work zone environment is very dynamic, resulting in differing reasons for crashes. These are barriers to gaining an accurate understanding of safety in work zones. The objective of this paper is to design, develop, and deploy a mobile application (app) for real-time work zone data collection to address these issues. The development process consisted of the following steps. First, a user interface was designed to enable users to collect various work zone activity information. Second, taking advantage of recent advances in cloud computing, a real-time database was designed for efficient storage and instantaneous communication of work zone activity data. Field tests were then conducted at 13 work zone sites in Columbia, Missouri. Finally, the performance of the app was evaluated based on scalability, precision, and user friendliness. The app was able to respond to queries at real-time speeds even as the size of the database and the number of users increased. The precision of sensors was within appreciable accuracy for the geolocation. The app’s user friendliness was acknowledged by the users. The successful deployment of this mobile app would lead to accurate work zone data which is very useful for work zone management, traveler information, contract monitoring, safety analysis, and project coordination.

Predicting geographical suitability of geothermal power plants

A large and increasing number of countries use geothermal energy as power source for domestic and industrial applications. Geothermal power plants produce energy out of this natural and renewable source in a sustainable way and contribute to reduce global warming. However, power plants effectiveness depends on the suitability of an area to geothermal energy production, which is a complex and unknown combination of many environmental factors. Nowadays, geothermal suitability assessments require invasive inspections, high costs, and legal permissions. Thus, having a global suitability map of geothermal sites as reference would be useful prior knowledge during assessments, and would help saving time and money. In this paper, the first suitability map of potential geothermal sites at global scale is presented. The map is the result of the application of data collection and preparation processes, and a Maximum Entropy model, to geospatial data potentially correlated with geothermal site suitability and geothermal plants operation. The reliability of our map is assessed against currently active and planned geothermal power plants. Our approach follows the Open Science paradigm that guarantees results reproduction and transparency, and allows stakeholders to reuse the produced standardised data, services, and Web interfaces in other experiments or to generate new maps at regional scale. Overall, our results can help scientists, industry operators, and policy makers in geothermal sites assessments. Also, our approach supports communication with citizens whose territories are involved in probing and assessments, in order to transparently inform them about the reasons driving the selection of their territory and the potential future benefits.

Good Practices for Species Distribution Modeling of Deep-Sea Corals and Sponges for Resource Management: Data Collection, Analysis, Validation, and Communication

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, analysts should integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys), include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.

The Development of Mobile and Server Application for Traffic Data Collection Based on Location-Based Services (LBS)

Traffic data includes various data such as the number of vehicles, vehicle velocity, or travel time that might lead to meaningful information for the traffic engineers. This research aims to collect the traffic data based on the Location Based Services (LBS) by utilizing the Global Positioning System (GPS) in a smartphone. The drivers bring the smartphone and collecting the traffic data while driving the vehicle. It is done in real-time by running the mobile application in background service. The data includes the date, timestamp, latitude, longitude, device ID, and vehicle velocity. These data are temporarily stored in the internal smartphone database, then synchronized to the database in the server application. According to the result, the large number of online taxi drivers and a longer time to collect the data should be considered to cover a wide area.