Data Management Methods Research Articles

ELabFTW as an Open Science tool to improve the quality and translation of preclinical research

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-sceience labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management.

Relating Lifetime Activity Behavior to the Current Level of Physical Activity of Older Adults.

This study investigates methods of data gathering and management, along with the relationship of lifespan and older adults' activity. Community-dwelling older adults (n = 47, 81.7 ± 3.6years) completed the Lifetime Leisure Physical Activity Questionnaire. Current activity was assessed by use of accelerometers. The data were converted to the metabolic equivalent of task hours. Correlations between the main outcomes (metabolic equivalent of task/hour, Lifetime Leisure Physical Activity Questionnaire), as well as individual estimation errors for data adjustments, were computed. The accelerometer and Lifetime Leisure Physical Activity Questionnaire data for the last 12 months' activity were associated (r = .31, p = .033). The average overestimation in the self-reported data was 176%. The adapted data on lifetime physical activity reveals correlations between older adults' activity and the activity levels of three 15-year episodes (r = .354; r = .336; r = .323; each p < .05), as well as compliance with guidelines throughout life (Hotelling's T2 = 45-164; p ≤ .002). Our findings indicate a relationship between lifetime and older adults' activity and provide further support for lifelong engagement in physical activity.

ELabFTW as an Open Science tool to improve the quality and translation of preclinical research

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-science labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management.

Practicalities of language data collection and management in and around Indonesia

Researchers use different approaches when collecting and managing primary language materials during fieldwork. Yet it is important that this work is done in a transparent way, so that it can be used by other researchers, who might have other aims, as well as by the speaker community who might want to use or take note of the collected materials. In this article we use our research experience in language data collection in and around Indonesia in fieldwork projects of three kinds: descriptive fieldwork, linguistic surveys, and projects investigating language contact. Our aim is to provide an introductory and practical guide for students and professionals who are embarking on fieldwork in or around Indonesia. Describing practical methods of language data collection, processing, and management, our aim is to provide a guide for any research which involves the collection of language materials, including linguistic research, oral history or literature, and ethnography.

Leveraging integration facades for model-based tool interoperability

Data exchange and management methods are of paramount importance in areas as complex as the Architecture, Engineering and Construction industries and Facility Management. For example, Big Open BIM requires seamless information flow among an arbitrary number of applications. The backbone of such information flow is a robust integration, whose tasks include overcoming technological as well as semantic and pragmatic gaps and conflicts both within and between data models. In this work, we introduce a method for integrating the pragmatics at design-time and the semantics of independent applications at run-time into so-called “integration facades”. We utilize Model-driven Engineering for the automatic discovery of functionalities and data models, and for finding a user-guided consensus. We present a case study involving the domains of architecture, building physics and structural engineering for evaluating our approach in object-oriented as well as data-oriented programming environments. The results produce, for each scenario, a single integration facade that acts as a single source of truth in the data exchange process.

ELabFTW as an Open Science tool to improve the quality and translation of preclinical research

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small preclinical labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research.

Unmanned aerial vehicle (UAV) technical applications, standard workflow, and future developments in maize production – water stress detection, weed mapping, nutritional status monitoring and yield prediction

As a consequence of rapid ongoing technological developments and increasing integration into agricultural mechanization and agricultural intelligence, UAVs are gradually starting to play an increasingly important role in field crop management and monitoring. This review introduces and covers the development in four major applications of UAVs in maize production: (i) water stress detection, (ii) weed mapping, (iii) nutrient status monitoring and (iv) yield prediction. In addition, this review summarizes UAV data management methods, explains how expert systems work in UAV systems, and provides standardized workflow data for farmers in maize production. In addition, the strengths, weaknesses, opportunities, and threats of UAV use in maize production are analyzed. Based on more than eighty publications and our own research, the discussion and conclusions point out key issues in UAV usage in maize cropping and research gaps that need to be filled, along with a number of recommendations for the development of UAVs in maize production in the future.

An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications.

Background: As medical knowledge is continuously expanding and diversely located, Health Information Technology (HIT) applications are proposed as a good prospect for improving not only the efficiency and the effectiveness but also the quality of healthcare services delivery. The technologies expected to shape such innovative HIT architectures include: Mobile agents (Mas) and NoSQL technologies. Mobile agents provide an inherent way of tackling distributed problems of accessing heterogeneous and spatially diverse data sources. NoSQL technology gains ground for the development of scalable applications with non-static and open data schema from complex and diverse sources. Methods and Design: This paper conducts a twofold study: It attempts a literature review of the applications based on the mobile agent (MA) and NoSQL technologies for healthcare support services. Subsequently, a pilot system evaluates the NoSQL technology against the relational one within a distributed environment based on mobile agents for information retrieval. Its objective is to study the feasibility of developing systems that will employ ontological data representation and task implementation through mobile agents towards flexible and transparent health data monitoring. Results and Discussion: The articles studied focus on applying mobile agents for patient support and healthcare services provision thus as to make a positive contribution to the treatment of chronic diseases. In addition, attention is put on the design of platform neutral techniques for clinical data gathering and dissemination over NoSQL. The experimental environment was based on the Apache Jena Fuseki NoSQL server and the JAVA Agent DEvelopment Framework -JADE agent platform. The results reveal that the NoSQL implementation outperforms the standard relational one.

PHP-based undergraduate data reporting and teaching quality evaluation information system

Teaching evaluation is an important means to evaluate the quality of university education. The traditional teaching evaluation process requires a lot of manpower and material resources to uniformly allocate resources with inconsistent data storage methods. Therefore, it is of great significance to develop a teaching evaluation system for data management and display. The system adopts the PHP development language, the mature oneThink development framework, the data management method is used to process the data, the data is stored in the MySQL database, and the MVC software design architecture and B/S mode are used for the front-end and server interaction. This article describes the key steps of the development system, and proved in practice that the system is stable and compatible with different operating systems and browsers. The results show that this system simplifies the process of teaching evaluation, improves the efficiency of university educational affairs, and has positive significance for the improvement of university teaching quality.

Integration of intelligent diagnosis system and augmented reality for electric motors

With the rapid development of industrial automation technology, electric motor control has also become an important technology. However, motor failure has many possible causes, and it is not easy to detect in advance. Therefore the fault diagnosis is an important issue for motors. The augmented reality (AR) technology is also developing towards various industrial and educational application in recent years. This research is focused on developing the intelligent diagnosis system and AR application for electric motors. The intelligent diagnosis system applies cloud data management and machine learning methods to predict the health status of electric motors. And the time-recurrent Long Short-Term Memory (LSTM) neural network algorithm is applied to establish a motor health diagnostic model. The experimental results show that the motor diagnosis method can predict the health status of motor effectively. The proposed system will also provide more industrial application services in the future.

Research on Data Management of Power Grid Enterprises Based on Data Middle Platform

At present, the construction process of power grid enterprise information system mainly adopts the “vertical integration” system structure, with high construction cost, long period, non-sharing of data and low quality. By introducing the concept of data middle platform.to reaggregate and reconstruct unified supervision of enterprise data, it provides accurate and reliable, shareable and reusable data application services with business logic for front-end business, and realizes intelligent interconnection and data sharing in all aspects of power system. This paper puts forward the idea of data center, and puts forward data management methods from master data, data warehouse and metadata management respectively, so as to provide data service support for the development of power grid business towards intelligent and efficient direction.

Assessment of measurement uncertainty using longitudinal calibration data in the forensic context

Suppose that results of the scientific literature were presented in courts the same way they are presented in publications. In the present investigation, it is assumed that this is the case, and it is shown that it may potentially lead to issues with results that are being presented to the court. The determination of synthetic cannabinoids at low concentrations in blood, urine and other matrices is challenging to forensic science. Methods based upon the separation of the compounds by HPLC and detection by tandem mass spectrometry provides results at ultra-low concentrations. The uncertainty of measurements has become the key parameter of interest for decision making, and expert witnesses need to state the correct level of uncertainty. Recent developments in quality assurance indicate issues with reliability, owing to contradictory statements that originate from different methods of data management and interpretation of results that eventually may lead to compromising the truth. The levels of relative uncertainty of measurement that were close to 5% were found to be unrealistically low when synthetic cannabinoids were analysed at ultra-low concentrations. It was proposed to introduce the principle of pooled calibrations to obtain correspondence between predicted and observed uncertainty, following standards of scientific methodology. Ten synthetic cannabinoids were analysed with pooled calibrations, and the results indicated that the uncertainty of measurement was found at levels much higher than expected, and with two out of ten synthetic cannabinoids that were impossible to quantify with relative uncertainties reaching levels over 70%.

Management Point Cloud Data based on GeoSOT-3D Subdivision Model

Owing to the rapid development of earth observation technology, the volume of point cloud dataset is growing rapidly. The efficient management and retrieval method is very important for effective use of the large point cloud datasets. Current methods for point cloud data management are generally based on file which makes it difficult to describe internal structure of the modelling objects, while current indexing is generally based on coordinates which is quite time-consuming for large volume cloud point data. To solve these problems, we proposed a method to manage and index point cloud data with a global grid model GeoSOT. The subdivision model of three-dimensional point cloud data was defined to identify the global position of point clouds and internal points with GeoSOT-3D grids. The management and indexing of point clouds were implemented based on the GeoSOT-3D grid codes integrated with the metadata table. A 3D prototype platform for showing the method was developed based on the Cesium. Experimental results show that our method is able to manage, query, visualize massive point clouds as well as other data sources in a unified global position framework.

WITHDRAWN: Computer Aided Management System of Sports Horse Registration Based on Distributed Storage System and Deep Fusion Learning

Abstract The great success of Deep Learning (DL) in the past has prompted front line accomplishments in different fields, for example, picture acknowledgment and common language handling. One reason for this achievement is the expanded size of the DL model and the expansion of preparing information accessible. To improve the exhibition of continuous DL, it is necessary to improve the scalability of the DL system. This study conducted an extensive and in-depth survey of scalable DL challenges, technologies, and tools on distributed infrastructure. It is built into the infrastructure for DL for parallel DL training, multi-tenant resource scheduling and training, and model data management methods. The board and mentors commonly take a gander at details dependent on manual information and reports to consistently check player details. There is an absence of investigation and utilization of player details to improve players and organizations’ abilities and organizations by coaches. In this study, decision support in sports can be applied so that the correct approach to business intelligence methods can be applied to improve the ability of athletes and organizations to develop sports science and its management and coaching. The proposed system is an analysis based on the FPGA tool, and its performance is better than the existing system.

Data literacy and management of research data – a prerequisite for the sharing of research data

PurposeThe purpose of this paper is to investigate the knowledge and attitude about research data management, the use of data management methods and the perceived need for support, in relation to participants’ field of research.Design/methodology/approachThis is a quantitative study. Data were collected by an email survey and sent to 792 academic researchers and doctoral students. Total response rate was 18% (N = 139). The measurement instrument consisted of six sets of questions: about data management plans, the assignment of additional information to research data, about metadata, standard file naming systems, training at data management methods and the storing of research data.FindingsThe main finding is that knowledge about the procedures of data management is limited, and data management is not a normal practice in the researcher's work. They were, however, in general, of the opinion that the university should take the lead by recommending and offering access to the necessary tools of data management. Taken together, the results indicate that there is an urgent need to increase the researcher's understanding of the importance of data management that is based on professional knowledge and to provide them with resources and training that enables them to make effective and productive use of data management methods.Research limitations/implicationsThe survey was sent to all members of the population but not a sample of it. Because of the response rate, the results cannot be generalized to all researchers at the university. Nevertheless, the findings may provide an important understanding about their research data procedures, in particular what characterizes their knowledge about data management and attitude towards it.Practical implicationsAwareness of these issues is essential for information specialists at academic libraries, together with other units within the universities, to be able to design infrastructures and develop services that suit the needs of the research community. The findings can be used, to develop data policies and services, based on professional knowledge of best practices and recognized standards that assist the research community at data management.Originality/valueThe study contributes to the existing literature about research data management by examining the results by participants’ field of research. Recognition of the issues is critical in order for information specialists in collaboration with universities to design relevant infrastructures and services for academics and doctoral students that can promote their research data management.

A Blockchain-Assisted Massive IoT Data Collection Intelligent Framework

Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this article, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework: 1) we design a large-scale heterogeneous WSNs collaborative identity verification protocol to ensure reliable data sources; 2) we build a hierarchical massive data aggregation scheme to collect massive IoT data efficiently and securely; and 3) we depict a blockchain-based massive IoT data management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.

Deepint.net: A Rapid Deployment Platform for Smart Territories.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

Study on Data Management of Electric Power Distribution Dispatch and Cutting-through Based on Big Data

With the development of technologies such as big data, Internet of Things, and cloud computing, power companies’ information management systems for dispatch, finance, projects, marketing, equipment, and materials have also made great progress. The construction of the data platform of the electric power company has realized the unified collection of data resources for the entire business of the power grid, which provides strong support for cross-professional, cross-departmental, internal and external data association analysis and in-depth data value mining. Based on the data management and management of power distribution dispatching, big data technology is adopted to optimize the coding and structure of power system distribution data to realize data cutting-through. Through experimental testing and analysis, it is proved that the research content of the paper can meet the current power operation needs. Compared with other data management methods, it can achieve higher management accuracy and coverage, and play a data guarantee effect for power distribution work.

Research on data fusion scheme of power internet of things based on cloud and NFV

Abstract With the development of the cloud platform and power Internet of Things, how to effectively connect the physical world and the digital world, realize the interconnection of all things in power terminals, deeply integrate data, and tap the economic value of data is an important issue. Based on the company’s business and technology development status, this paper proposes the power Internet of Things data fusion based on cloud and NFV, discusses data storage, circulation and management methods in the power Internet of Things cloud platform environment, and completes accurate and efficient data analysis. Responding to the access of a large number of distributed energy sources, multiple business concurrency and joint data analysis needs, and improve data analysis and sharing capabilities.

Towards Collaborative and Intelligent Learning Environments Based on Eye Tracking Data and Learning Analytics: A Survey

The current pandemic has significantly impacted educational practices, modifying many aspects of how and when we learn. In particular, remote learning and the use of digital platforms have greatly increased in importance. Online teaching and e-learning provide many benefits for information retention and schedule flexibility in our on-demand world while breaking down barriers caused by geographic location, physical facilities, transportation issues, or physical impediments. However, educators and researchers have noticed that students face a learning and performance decline as a result of this sudden shift to online teaching and e-learning from classrooms around the world. In this paper, we focus on reviewing eye-tracking techniques and systems, data collection and management methods, datasets, and multi-modal learning data analytics for promoting pervasive and proactive learning in educational environments. We then describe and discuss the crucial challenges and open issues of current learning environments and data learning methods. The review and discussion show the potential of transforming traditional ways of teaching and learning in the classroom, and the feasibility of adaptively driving learning processes using eye-tracking, data science, multimodal learning analytics, and artificial intelligence. These findings call for further attention and research on collaborative and intelligent learning systems, plug-and-play devices and software modules, data science, and learning analytics methods for promoting the evolution of face-to-face learning and e-learning environments and enhancing student collaboration, engagement, and success.