Efficiency Of Data Collection Research Articles

Port Digital Twin Development for Decarbonization: A Case Study Using the Pusan Newport International Terminal

The maritime industry is a major carbon emission contributor. Therefore, the global maritime industry puts every effort into reducing carbon emissions in the shipping chain, which includes vessel fleets, ports, terminals, and hinterland transportation. A representative example is the carbon emission reduction standard mandated by the International Maritime Organization for international sailing ships to reduce carbon emissions this year. Among the decarbonization tools, the most immediate solution for reducing carbon emissions is to reduce vessel waiting time near ports and increase operational efficiency. The operation efficiency improvement in maritime stakeholders’ port operations can be achieved using data. This data collection and operational efficiency improvement can be realized using a digital twin. This study develops a digital twin that measures and reduces carbon emissions using the collaborative operation of maritime stakeholders. In this study, the authors propose a data structure and backbone scheduling algorithm for a port digital twin. The interactive scheduling between a port and its vessels is investigated using the digital twin. The digital twin’s interactive scheduling for the proposed model improved predictions of vessel arrival time and voyage carbon emissions. The result of the proposed digital twin model is compared to an actual operation case from the Busan New Port in September 2022, which shows that the proposed model saves over 75 % of the carbon emissions compared with the case.

Understanding the Strengths andLimitations ofCurrent Methods forSurveying Partner Nation Medical Facilities.

During exercises or operations, there may be times when U.S. medical capabilities are not available and the next best or only option may be to use partner nation (PN) or host nation capabilities. Joint Publication 4-02 Joint Health Services states that "medical planners should always consider the quality, suitability, and availability of multinational and host-nation support." It is normal practice for medical planners to survey PN medical capabilities as part of the pre-deployment planning process. Currently, medical capability surveys are not conducted in a consistent and systematic manner across the DoD global health engagement enterprise. The lack of a systematic approach undermines medical operations planners' ability to conduct efficient and adequate pre-deployment surveys. The article presents the results of a descriptive analysis of 62 unclassified medical capability surveys of PN or host nation facilities from the U.S. Africa Command (USAFRICOM) area of responsibility that were conducted by U.S. DoD personnel. The team characterized the content and formats of surveys with respect to what medical capabilities were described, how the capabilities were described, and how the information was presented. These analyses focused on determining if a surveyor obtained information about a capability, not whether or not the facility had a capability. Approximately 75-80% of surveys included information describing the presence or absence of five key capabilities: Emergency department/trauma care, surgical services, intensive care unit, laboratory, and imaging. Conversely, 30-50% of surveys did not include any information describing the presence or absence of five other key capabilities: Pharmacy, blood bank, mass casualty plans, land evacuation, or air evacuation. Information on key capabilities and administrative information was not consistently reported across the sample of surveys. There was substantial variation in how capabilities were characterized, including number of staff, staff training, and available equipment. Additionally, the order in which information was presented in surveys varied within and across components. There are significant inconsistencies in the types of capabilities and services documented and how the quality of the capabilities and services is characterized. These inconsistencies can be attributed, in part, to the absence of information that explicitly confirmed whether or not the facility had a capability. Such variation results in obscured or incomplete depictions of facility capabilities, thereby undermining the ability of medical planners to coordinate effective medical readiness for engagements, exercises, or real-life operations. Guidance and survey templates could support better-informed decision-making by including information about survey methods and documenting the lack of confirmatory information. The DoD enterprise should consider how guidance and a standard survey template could improve the relevance, accuracy, and efficiency of data collection and reporting.

Vignette methodology in applied linguistics

Vignette methodology offers promising opportunities for the investigation of situation-specific dynamic (affective) variables, such as emotions and cognitions, in language learning, teaching, and use. Despite their frequent application in adjacent fields like education, sociology, and psychology, the use of vignettes and vignette methodology in applied linguistics is still scarce. Using emotion research as an example, this tutorial introduces applied linguists to vignette methodology, outlines a vignette design framework, illustrates administration strategies, and highlights its ability to bolster methodological consistency and research-practitioner collaborations across various research paradigms. The tutorial aims to advance the use of vignette methodology in applied linguistics by illustrating its potential to strengthen the validity of research findings, as well as its benefits regarding the efficiency of data collection, participant recruitment, and research costs.

How KoboToolbox versus Unipark platform are selected in data survey to detect child maltreatment in Vietnam: A discussion paper

Background: Due to the importance of good data collection and management in research, studies increasingly rely on digital tools to make this process more efficient and error-free. For the topic on child maltreatment in Vietnam, we considered the Unipark and KoboToolbox to conduct our questionnaire and collect, manage, and visualize our data. This discussion paper is about which KoboToolbox or Unipark tools are better for data survey of child maltreatment studies in Vietnam. Methods: We conducted three phases: document review, experts experimented with functions, lean operation, skip logic questions of tool and validation criteria to limit errors in filling by respondents and a pilot survey was conducted by 242 students at a secondary school in Bac Giang province. Results: Through three phases in comparison with Unipark, KoboToolbox dominates. KoboToolbox is convenient and time-saving in constructing a complex questionnaire with more than four hundred questions and skips logic by Excel software and free fee. It has a validation criteria function to mitigate data bias due to filling and clustering related questions in each part, creating a friendly user interface for experts’ experience and students. Kobo toolbox’s offline data collection platform capability addressed challenges related to unstable internet connectivity in local contexts. Conclusions: Kobotoolbox was chosen and employed for optimizing constructing questionnaires, data collection efficiency, and quality, ultimately contributing to a more comprehensive understanding of child maltreatment in Vietnam. Keywords: KoboToolbox, Unipark, platform, software, child maltreatment, data collection, data management.

Adaptive fusion based on physics-constrained dictionary learning for fault diagnosis of rotating machinery

Modern manufacturing systems rely on multiple types of sensors to monitor the manufacturing processes and machine faults to ensure product quality. The costs associated with sensor installation, maintenance, data transmission, and data storage are high. In this paper, a new data fusion method based on physics-constrained dictionary learning is proposed to improve the efficiency of data collection and the accuracy of fault diagnosis. In the proposed dictionary learning method, the measurement, basis, and classification matrices are optimized for the effective use of compressed sensing technique in process monitoring. With the optimized matrices, full-scale high-resolution signals can be reconstructed from a small number of sensor measurements. At the same time, machine states can be identified based on the sparse measurements. An adaptive weight scheme is introduced to combine low-cost sensor data so that the accuracy of fault diagnosis can be improved. The proposed method is tested with an experimental dataset of gearbox vibrations caused by gear cracks and different levels of crack severities are classified. The results show that up to 70% of data collection can be saved with the new approach while more than 95% of diagnosis accuracy is achieved. The sensitivities of the performance with respect to the number of measurements and number of sensors are also studied.

Metode Design Thinking Pada Sistem Informasi Presensi Pegawai Kejaksaan Negeri Kota Bogor

Employee presence is an important factor for an agency or company to achieve goals, this is related to discipline and has an impact on the performance of each employee. But in reality, there are still several agencies that still use manual absences that have not been computerized, one of which is the Kejaksaan Negeri Kota Bogor's Office. The system used by the Kejaksaan Negeri Kota Bogor in the attendance process is still manual, namely using a daily attendance book which affects the efficiency and effectiveness of data collection, data search, and calculations and requires a relatively long time. Therefore, it is necessary to have special data collection to record attendance and absence so that work activities can be recorded in real-time and properly, one of which is by using a computerized system with Information Systems. The information system built on a website basis uses the CodeIgniter 3 framework and MySQL. The method used is the Design Thinking Method which has 5 stages. The output of this study is known to be tested with usability testing by user on the Learnability aspect of 75%, then on the efficiency aspect of 100% and the memorable aspect of 66.77%. The average result of the test is quite good at 80.56%. So by making this information system, it can facilitate the process of being on time, searching for data, calculating and summarizing timeliness and minimizing errors when recording presence data.

Sistem Pencatatan Dan Pelaporan Surveilans Infeksi Nosokomial Di RS Premier Surabaya

Nosocomial infection is infection that occur in patients who are hospitalized. One of infection control programs is the surveillance activities. Refer to the Instructions Practical Hospital nosocomial infections Surveillance by the Ministry of health (2011) that computer usage in surveillance activities will help increase the efficiency of data collection and analysis. The propuse of this study is to develop recording and reporting nosocomial infection surveillance system. Methods: this study type is action research with system development life cycle (SDLC) methods , the stages of SDLC is planning, analysis, design, implementation and usage. The instruments used in this study by creating an DFD,ERD, data dictionary and continued with the development of applications using PHP and mySQL. Data collection through interviews and observations. The study results in the form of web-based applications further tested using the method of Technology Accepatnce Mode (TAM). Keyword: Nosocomial infection surveillance, recording and reporting, system development, rs premier surabaya

Improving data collection efficiency in small-molecule serial femtosecond crystallography at X-ray free-electron lasers

Improving data collection efficiency in small-molecule serial femtosecond crystallography at X-ray free-electron lasers

Estimation of the Forest Stand Biomass and Greenhouse Gas Emissions Using Lidar Surveys

At the research points, the relationship between the Normalized Difference Vegetation and Normalized Green Red Difference indices is characterized by a determination coefficient equal to 0.52. The estimation of the emission of carbon dioxide and nitrogen oxide in the forest air at an altitude of 40 m above the level of the soil cover during the growing season showed differences in their values during the daytime and at night. The results helped determine promising methods of inventory of the carbon landfill forest area for aboveground woody biomass assessment based on data obtained from several sources and land forest estimation research. The research involved: 1) integration of an unmanned aerial vehicle -based digital camera and lidar sensors in order to optimize the efficiency and cost of data collection; 2) taking advantage of high-resolution aerial photographs and sparse lidar point clouds using an information fusion approach and the ability to compensate for their shortcomings.

"CAPABILITIES OF NB-IoT TECHNOLOGY FOR SOLVING TASKS OF MONITORING, ACCOUNTING AND COLLECTING DATA ON ELECTRICITY CONSUMPTION "

"Problems with accounting, collection and correct readings of electricity meters are of great importance when preparing and invoicing for electricity, especially in neighborhoods with private households, where the process of monitoring electricity consumption is not always provided for various reasons beyond the control of the consumer of services. The development of modern information technologies, including mobile communications using the technology of ΝΒ-ΙοΤ, allows us to solve problems related to improving the efficiency of monitoring, accounting, and data collection of electricity consumption in microdistricts not only quickly and efficiently, but also increase the management efficiency of the process of collecting meter readings, as well as reducing the costs of the Service Provider for transport services, personnel and other items of expenditure. The article presents an analysis of various brands of metering devices used in the Urker microdistrict and the use of metering devices with the support of technology ΝΒ-ΙοΤ. A simulation model of access to metering devices using the technology of ΝΒ-ΙοΤ is shown. The issues related to the causes and problems of electricity theft are touched upon. The article will be useful to students, doctoral students, engineers, anyone who is related to automation and control of technological processes and productions, as well as anyone who deals with the problems of implementing the ASUE, IoT technology. "

Cohort Establishment and Operation at Pusan National University School of Medicine

Pusan National University School of Medicine (PNUSOM) began analyzing the cohort of pre-medical students admitted in 2015 and has been conducting purposeful analyses for the past 3 years. The aim of this paper is to introduce the process of cohort establishment, cohort composition, and the utilization of cohort analysis results. PNUSOM did not initially form a cohort with a purpose or through a systematic process, but was able to collect longitudinal data on students through the establishment of a Medical Education Information System and an organization that supports medical education. Cohort construction at our university is different in terms of a clear orientation toward research questions, flexibility in cohort composition, and subsequent guideline supplementation. We investigated the relevance of admission factors, performance improvements, satisfaction with the educational environment, and promotion and failure rate in undergraduate students, as well as performance levels and career paths in graduates. The results were presented to the Admissions Committee, Curriculum Committee, Learning Outcomes Committee, and Student Guidance Committee to be used as a basis for innovations and improvements in education. Since cohort studies require long-term efforts, it is necessary to ensure the efficiency of data collection for graduate cohorts, as well as the validity and ethics of the study.

Application of a Technology-Based Data Collection System for the Writing Class of the IAIN Cirebon Student Da'wah Institute

Effective data management plays a crucial role in optimizing the performance of educational institutions. This study aims to explore the implementation of a technology-based data management system for writing classes in the Student Preaching Institution of the State Islamic Institute (IAIN) Cirebon. The objective is to improve the efficiency and effectiveness of data collection, storage, and analysis, ultimately enhancing the overall quality of the writing classes. This research utilizes a mixed-methods approach, incorporating qualitative and quantitative data collection techniques. Firstly, a comprehensive review of the existing data management practices is conducted to identify the limitations and challenges faced by the institution. Secondly, a technology-based data management system is developed, integrating modern tools and techniques to streamline the data collection process. The system is designed to facilitate easy data entry, retrieval, and analysis, ensuring accurate and reliable information for decision-making purposes. The implementation of the technology-based data management system is evaluated through a series of case studies and interviews with relevant stakeholders. The findings reveal several significant benefits, including increased efficiency in data management, improved accuracy in reporting, and enhanced collaboration among the stakeholders involved in the writing classes. Moreover, the system enables the institution to track the progress of individual students, identify their strengths and weaknesses, and provide targeted interventions to support their learning and development. The results of this study contribute to the growing body of literature on the application of technology in educational institutions. The findings highlight the importance of leveraging technology to optimize data management processes, particularly in the context of writing classes in the Student Preaching Institution of IAIN Cirebon. The insights gained from this research can serve as a valuable resource for other educational institutions seeking to enhance their data management practices and improve the overall quality of their programs.

Energy optimization routing for hierarchical cluster based WSN using artificial bee colony

Technology advancement in Wireless sensor network (WSNs) has attracted much attention in smart computing and potential usage across numerous applications fields. WSN composed of tiny and self-configured sensor nodes with battery operated. Sensors are restricted to limited energy and resources. Unbalanced nodes energy results in more energy consumption and affects network lifetime. Achieving energy efficiency remains a challenging issue in designing WSN routing. Approaches for minimising node energy usage through clustering techniques have offered optimal solutions. However, the existing clustering scheme fails to balance nodes energy without considering energy parameters, low energy node being chosen as cluster head, node density, and scalability. Existing scheme limits to global exploration possibilities and restricted to only specific search regions. In this paper, we propose an energy optimization routing using improved artificial bee colony (EOR-iABC) for cluster based WSN. The proposed EOR-iABC routing scheme intends to achieve energy optimization and prolong network's lifetime. The EOR-iABC adapts distinctive search policy using improved artificial bee colony algorithm for selection of energy efficient cluster heads (CH) in periodic time intervals through integration of crossover and mutation. The delay convergence is eliminated by associating employee and onlooker-bee stages, which helps to reform local search strategies. An optimal path from CH to base station (BS) is discovered through energy-efficient fitness node to improve network data collection efficiency. Grenade explosion method (GEM) and Cauchy operator are employed to extend search policies dynamically from one region to another for large-scale WSN. Simulation results show EOR-iABC outperforms in terms of energy efficiency by 27% compared to OCABC scheme and by 16% compared to IABCOCT scheme.

Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks

The deployment of unmanned aerial vehicles (UAVs) has significantly improved the efficiency of data collection for wireless sensor networks (WSNs). The freshness of collected information from sensors can be measured by the age of information (AoI), which is an important factor to consider in data collection. For data collection during long-term mission, the energy limitation of UAVs may cause mission interruption, which makes supplementation of the UAVs’ energy more necessary. To this end, we introduce the mobile unmanned vehicle (MUV) to guarantee the UAVs’ energy supplementation. In this paper, we investigate the problem of multi-UAVs and single-MUV cooperative trajectory planning (MUSM-CTP) for data collection in WSNs with consideration for the AoI the collected data and the limited battery capacity of UAVs. The objective of this problem is to find cooperative flight trajectories for multiple UAVs and to determine the MUV’s travel plan to replace batteries for the UAVs, such that the average AoI of all collected data is minimized. We prove the NP-hardness of the problem and design the algorithm via three phases to solve this: determining candidate hover points based on the affinity propagation (AP) clustering method, constructing the flight trajectories of multiple UAVs based on the genetic algorithm (GA), and designing a travel plan for the MUV. The simulation results verify the effectiveness of the proposed algorithm in improving the freshness of the information collected from all of the sensors.

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm.

DIRECTIONS OF APPLICATION OF ARTIFICIAL INTELLIGENCE IN GEODESIC MONITORING OF BUILDINGS AND STRUCTURES

The study determined the necessity of using artificial intelligence in solving the tasks of geodetic monitoring of buildings and structures. According to the results of the research, examples of the world experience of involving artificial intelligence in solving various issues of geodetic monitoring were found. The issues of using modern instrument equipment in conducting geodetic monitoring with the use of artificial intelligence are investigated. Therefore, the application of artificial intelligence in geodetic monitoring of buildings and structures can significantly increase the efficiency and accuracy of data collection and analysis, reduce risks and increase the safety of workers and the public, as well as reduce costs and increase the productivity of processes. There are various examples of the application of artificial intelligence in geodetic monitoring of buildings and structures, such as the use of machine learning to predict landslides, the use of autonomous drones to collect data, and automatic image processing systems to detect defects and damage to buildings. The use of artificial intelligence in geodetic monitoring of buildings and structures has great potential for improving the safety, efficiency and productivity of construction processes. In addition, the development of artificial intelligence and its application in geodesy can help solve complex tasks, such as predicting floods, earthquakes and other dangerous phenomena related to geodesy and construction. The application of artificial intelligence can also increase the accuracy and reliability of surveying and mapping, which can be useful for solving many different problems related to surveying and construction. In this regard, it can be argued that artificial intelligence has already found its application in geodesy and construction, and its capabilities are still being explored and developed. This opens up many opportunities to improve the efficiency and accuracy of construction processes, reduce risks and increase the safety of workers and the public, as well as to create new innovations and achievements in the field of surveying and construction. Keywords: artificial intelligence, geodetic monitoring, geoinformation system, automation, camera works, monitoring principles, building and construction monitoring data, geospatial analysis, construction objects.

Upper Mantle Velocity Structure Beneath the Yarlung–Tsangpo Suture Revealed by Teleseismic P-Wave Tomography

We applied teleseismic tomography to investigate the 3D P-wave velocity (Vp) structure of the crust and upper mantle at depths of 50–400 km beneath the Yarlung–Tsangpo suture (YTS), by using 6164 P-wave relative travel-time residuals collected from 495 teleseismic events recorded at 20 three-component broadband seismograms. A modified multi-channel cross-correlation method was adopted to automatically calculate the relative arrival-time residuals of all teleseismic events, which significantly improved the efficiency and precision of the arrival-time data collection. Our results show that alternating low- and high-Vp anomalies are visible beneath the Himalayan and Lhasa blocks across the YTS, indicating that strong lateral heterogeneities exist beneath the study region. A significant high-Vp zone is visible beneath the southern edge of the Lhasa block at 50–100 km depths close to the YTS, which might indicate the rigid Tibetan lithosphere basement. There exists a prominent low-Vp zone beneath the Himalayan block to the south of the YTS extending to ~150 km depth, which might be associated with the fragmentation of the underthrusting Indian continental lithosphere (ICL) and induce localized upwelling of asthenospheric materials from the upper mantle. In addition, significant low-Vp anomalies were observed beneath the Yadong–Gulu rift and the Cona–Sangri rift extending to ~300 km depth, indicating that the tearing of the subducted ICL might provide pathways for the localized asthenospheric materials upwelling, which contributes to the widespread distribution of north–south trending rifts and geothermal activities in southern Tibet.

Pemanfaatan Aplikasi Sistem Pendataan Anggota UMKM Paguyuban (THB) Kota Bekasi

The UMKM Association of Bekasi City has challenges in collecting data on members of micro, small and medium enterprises (MSMEs) in Taman Harapan Baru RW 23, Pejuang Village, Medan Satria District, Bekasi City. Limitations in making periodic reports cause difficulties for cooperatives in providing accurate MSME member data information to the MSME Cooperative Service. To overcome this problem, we developed a computer and database system that would make data collection easier. This system enables efficient and easy monitoring of member data, considering the large number of members who are members of the community. Through community service activities, we have succeeded in producing a website application that uses JavaScript programming language technology with the Next.js Library and the MongoDB database. This application assists the community in collecting data and searching for member data, as well as creating member business identities through online cards that can be accessed from anywhere via the internet. The information system that has been developed provides a solution to improve the efficiency and accuracy of data collection for MSME members, as well as facilitating easier access to information for related parties, such as the MSME Cooperative Office. With this website application, UMKM Paguyuban Kota Bekasi can carry out data collection activities more effectively and efficiently. In addition, the information system that has been developed can also support data-based decision making for MSME empowerment programs in the region.

BatNet: a deep learning‐based tool for automated bat species identification from camera trap images

AbstractAutomated monitoring technologies can increase the efficiency of ecological data collection and support data‐driven conservation. Camera traps coupled with infrared light barriers can be used to monitor temperate‐zone bat assemblages at underground hibernacula, where thousands of individuals of multiple species can aggregate in winter. However, the broad‐scale adoption of such photo‐monitoring techniques is limited by the time‐consuming bottleneck of manual image processing. Here, we present BatNet, an open‐source, deep learning‐based tool for automated identification of 13 European bat species from camera trap images. BatNet includes a user‐friendly graphical interface, where it can be retrained to identify new bat species or to create site‐specific models to improve detection accuracy at new sites. Model accuracy was evaluated on images from both trained and untrained sites, and in an ecological context, where community‐ and species‐level metrics (species diversity, relative abundance, and species‐level activity patterns) were compared between human experts and BatNet. At trained sites, model performance was high across all species (F1‐score: 0.98–1). At untrained sites, overall classification accuracy remained high (96.7–98.2%), when camera placement was comparable to the training images (<3 m from the entrance; <45° angle relative to the opening). For atypical camera placements (>3 m or >45° angle), retraining the detector model with 500 site‐specific annotations achieved an accuracy of over 95% at all sites. In the ecological case study, all investigated metrics were nearly identical between human experts and BatNet. Finally, we exemplify the ability to retrain BatNet to identify a new bat species, achieving an F1‐score of 0.99 while maintaining high classification accuracy for all original species. BatNet can be implemented directly to scale up the deployment of camera traps in Europe and enhance bat population monitoring. Moreover, the pretrained model can serve as a baseline for transfer learning to automatize the image‐based identification of bat species worldwide.

Using a Cascading Approach to Improve Multisite Study Procedures.

Multisite studies offer larger, more diverse samples to successfully capture populations and clinical practices of interest at the point of care. However, investigators face challenges with site recruitment and sampling, differences in clinical practices across sites, and data integrity. Addressing these issues a priori can improve the rigor and reproducibility of the research. This article aims to describe a cascading approach to multisite research. An exemplar is provided of a study using this approach, which aimed to evaluate the prevalence of pain and the pain management practices provided to critically ill children in pediatric intensive care units in the United States. The cascading approach includes two or more pilot study procedures with a progressively increasing number of sites prior to a full-scale study. Following each pilot, study procedures are evaluated; feedback was obtained from site personnel and content experts; procedures were revised accordingly; approvals were obtained; sites were trained; and the revised procedures are repeated with a larger, more diverse number of sites. In the exemplar provided, improvements in the efficiency and integrity of data collection were noted for the full-scale study following the pilots. All sites that completed the agreements and approvals for study participation were retained for the duration of the two pilots and full-scale study. Borrowing from principles of process improvement, the cascading approach allows knowledge to be gained regarding site differences and informs the revision of study procedures while potentially maximizing efficiency and data integrity, minimizing site burden, and maintaining site engagement for multisite studies.