Usage Data Research Articles

Consensus for Operating Room Multimodal Data Management: Identifying Research Priorities for Data-Driven Surgery.

This study aimed to identify research areas that demand attention in multimodal data-driven surgery for improving data management in minimally invasive surgery. New surgical procedures, high-tech equipment, and digital tools are increasingly being introduced, potentially benefiting patients and surgical teams. These innovations have resulted in operating rooms evolving into data-rich environments, which, in turn, requires a thorough understanding of the data pipeline for improved and more intelligent real-time data usage. As this new domain is vast, it is necessary to identify where efforts should be focused on developing seamless and practical data usage. A modified electronic Delphi approach was used; 53 investigators were divided into the following groups: a research group (n=9) for problem identification and a narrative literature review, a medical and technical expert group (n=14) for validation, and an invited panel (n=30) for two electronic survey rounds. Round 1 focused on a consensus regarding bottlenecks in surgical data science areas and research gaps, while round 2 prioritized the statements from round 1, and a roadmap was created based on the identified essential and very important research gaps. Consensus panelists have identified key research areas, including digitizing operating room (OR) activities, improving data streaming through advanced technologies, uniform protocols for handling multimodal data, and integrating AI for efficiency and safety. The roadmap prioritizes standardizing OR data formats, integrating OR data with patient information, ensuring regulatory compliance, standardizing surgical AI models, and securing data transfers in the next generation of wireless networks. This work is an international expert consensus regarding the current issues and key research targets in the promising field of data-driven surgery, highlighting the research needs of many operating room stakeholders with the aim of facilitating the implementation of novel patient care strategies in minimally invasive surgery.

ChatGPT and other Chatbots in Psychiatry

Aim: Artificial intelligence (AI) is making significant inroads into the field of psychiatry, offering new tools and applications. ChatGPT, a specific chatbot, is at the forefront of this digital revolution. AI’s use in psychiatry ranges from identifying psychiatric symptoms, predicting treatment responses, and improving medication adherence to assisting in patient education, monitoring, and bridging gaps in mental health care. Materials and Methods: This review used a literature study method. Results: ChatGPT functions as a clinical decision support tool. It can analyse patient data and provide diagnostic insights, recommend evidence-based treatments, and offer drug information. It has demonstrated proficiency in generating summaries from medical records, saving clinicians time and enabling them to focus on patient care. Additionally, chatbots like ChatGPT serve as therapist assistants, offering emotional support between therapy sessions and potentially conducting psychotherapy. Studies have shown positive outcomes, with chatbots reducing depression, anxiety symptoms, and providing 24/7 availability for crisis situations. Users find them non-judgmental and comfortable for discussing sensitive issues. Despite their potential, chatbots have limitations, such as the risk of incorrect or biased information due to their training data. They lack genuine understanding, creativity, and the ability to clarify user input. Ethical considerations regarding responsibility and data usage are paramount. Conclusion: AI, particularly ChatGPT, holds substantial promise in modern psychiatry, enhancing diagnostics, patient education, monitoring, and therapeutic support. Its integration into everyday psychiatric practice requires careful use, continuous oversight, and ethical considerations. Psychiatrists must become more familiar with AI tools to leverage their benefits in patient care.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology

Background Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. Methods To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates: 1. An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications. 2. The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets. 3. The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. Results EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. Conclusions The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers

Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces

To improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines.

Robust Structural Health Monitoring of CFRP Structures under Varying Loads using NSFD and Machine Learning with Combined Usage of Acceleration and Strain Data

Structural health monitoring (SHM) of objects in situ often requires a suitable combination of different methods and measurement techniques. In the field of aerospace applications, the previous research project "Combined acoustic and modal structure monitoring" dealt with the development of a robust SHM system for damage detection in carbon-fibre-reinforced polymer (CFRP) structures under varying realistic loads. The methods combined within the project were based on guided waves, acoustic emission, different vibration monitoring techniques and strain measurement. The present paper deals with the application of the nullspace-based fault detection algorithm (NSFD) combined with a machine learning approach based on strain measurements to classify different load conditions of the structure. Due to the high sensitivity to changes in the statistical properties of the measurement data, the NSFD algorithm reacts very sensitively to structural changes, but also to changes in the environmental conditions, such as changes in the external loads and the resulting stress state of the structure. In the paper, the impact of varying external loads on the NSFD damage indicator is analysed and shown. To prevent the NSFD algorithm from causing false alarms due to changes in the load conditions, but still being sensitive to small structural changes due to impact damages, the strain sensor data is used to predict the external loads on the structure. For this, machine learning is used to create clusters of similar stress states. For every cluster, a baseline for the NSFD algorithm must be made. The resulting approach is robust against changes in external loads and sensitive to small changes in the structure due to damages at the same time. The theory and the algorithms are successfully tested with measured data sets from a CFRP-airplane structure.

Students’ Readiness for the Adoption of Artificial Intelligence for Support Services: Qualitative Evidence from Al-Hikmah University, Nigeria

This study investigates students' perceived readiness for the adoption of artificial intelligence (AI) support services in Nigerian universities, focusing on Al-Hikmah University as a case study. The data were collected from 45 students who were selected via stratified, purposive and convenience sampling techniques. Thematic analysis was employed to analyze the interview transcripts. Factors influencing students' readiness include perceived usefulness, ease of use, and concerns about privacy and job security. The findings suggest that students at Al-Hikmah University are generally positive and open-minded about the potential of AI to enhance their learning experiences and support services. The study identified several benefits of AI-based support services, including personalized learning experiences, access to information and resources at any time, and the potential to improve efficiency in administrative processes. While students are generally receptive to AI, the study also highlighted some concerns and challenges. These include privacy concerns related to data collection and usage, and the need for adequate training to effectively use AI tools. In conclusion, the study on students' perceived readiness for the adoption of artificial intelligence (AI)-based support services in Nigerian universities, focusing on Al-Hikmah University, provides valuable qualitative evidence on the attitudes and perspectives of students toward AI technology in the context of higher education.

Exploring the Synergy of Web Usage Data and Content Mining for Personalized Effectiveness

In light of the exponential growth of web data and user volume, individuals are increasingly overwhelmed by information overload on the internet. Addressing this challenge, our study focuses on enhancing web information retrieval and presentation by leveraging web data mining techniques to uncover intrinsic relationships within textual, linkage, and usability data. Specifically, we aim to improve the performance of web information retrieval and presentation by analysing web data features. Our approach centres on web usage mining to identify usage patterns and integrate this knowledge with user profiles for personalized content delivery. Personalization, tailored to user’s characteristics and behaviours, serves to enhance engagement, conversion, and long-term commitment to websites. The objective of our research is to develop a web personalization system that enables users to access relevant website content without the need for explicit queries. This paper presents an extensive survey of various approaches proposed by researchers in the field of web personalization. It highlights the diverse methodologies and techniques employed to enhance user experience and engagement on the web. The paper identifies key challenges that require urgent attention to advance the field of web personalization.

Towards Carbon Footprint Management in Hybrid Multicloud

Enterprises today aspire to optimize the operating costs and carbon footprint (CFP) of their IT operations jointly without compromising their business imperatives. This has given rise to a hybrid approach in which enterprises retain the dynamic choice to leverage private data centers and one or more public clouds in conjunction. While cloud service providers (CSPs) have long provided APIs for estimating, reconciling, and optimizing operating costs, they have only recently started exposing APIs related to CFP. Indeed, this is a step in the right direction. Nevertheless, our analyses of these APIs reveals many gaps that need to be addressed to facilitate sizing and placement decisions that can factor in carbon. First, there is a lack of standardized, transparent methodology for CFP quantification across different CSPs. Second, the coarse granularity of the CFP data provided today can help with post-facto reporting but is not suitable for proactive fine-grained optimization. Last, enterprises themselves are unable to independently compute the current CFP or estimate potential CFP savings since CSPs do not share the required power usage data. To address these gaps, enterprises have started developing their own carbon assessment methodologies and tools to estimate the CFP of workloads running on public clouds using the available user-facing APIs. These systems hold the promise for an independent and unbiased evaluation and estimation of relative savings between different deployment options by cloud users. We describe and analyze the details of CSP-native carbon-reporting tools and their quantification methodology, and the "outside-of-the-cloud" estimation approaches. Finally, we present opportunities for future research in the direction of trustworthy, fine-grained, public cloud workload CFP estimation, which is a prerequisite for meaningful realization of carbon optimization.

Utilizing predictive analytics to boost customer loyalty and drive business expansion

In the contemporary business landscape, leveraging data to enhance customer experiences and drive strategic decisions has become paramount. Predictive analytics, a key component of advanced data analytics, offers a powerful tool for businesses seeking to boost customer loyalty and drive business expansion. This review explores the utilization of predictive analytics to understand customer behavior, anticipate their needs, and create personalized experiences that foster loyalty and promote growth. Predictive analytics involves analyzing historical data to identify patterns and trends that can forecast future behaviors and outcomes. By integrating predictive models into customer relationship management (CRM) systems, businesses can gain valuable insights into customer preferences, purchasing habits, and potential churn risks. These insights enable companies to tailor their marketing strategies, optimize customer interactions, and deliver targeted promotions that resonate with individual customers. One significant advantage of predictive analytics is its ability to enhance customer retention. By identifying at-risk customers early, businesses can implement proactive measures to re-engage them, such as personalized offers, targeted communication, and loyalty programs. This not only improves customer satisfaction but also reduces the costs associated with acquiring new customers. Moreover, predictive analytics facilitates market segmentation and helps businesses identify high-value customer segments. By understanding the characteristics and behaviors of these segments, companies can develop focused marketing campaigns and product offerings that cater specifically to their needs. This targeted approach not only maximizes the return on marketing investment but also drives business expansion by capturing new markets and increasing market share. Additionally, predictive analytics can support product development and innovation. By analyzing customer feedback and usage data, businesses can identify emerging trends and unmet needs, guiding the creation of new products and services that align with customer demands. This fosters a culture of innovation and continuous improvement, further strengthening customer loyalty and driving long-term growth. In conclusion, the integration of predictive analytics into business strategies is a powerful means to boost customer loyalty and drive business expansion. By leveraging data-driven insights to personalize customer experiences, anticipate needs, and optimize marketing efforts, businesses can achieve sustainable growth and maintain a competitive edge in the dynamic market landscape.

Water Consumption Using Artificial Neural Network Modelling in Commercial Aircraft

Amidst the current period of increased environmental consciousness and the aviation industry's dedication to sustainability, the delicate balance between water-carrying and fuel usage in commercial aircraft has become a crucial issue due to the rapid development of aviation technology. The primarypurpose of this study is to evaluate thewater usage of commercial aeroplanes and to optimise the aeroplanes' usage of water. Moreover, the study focuses on analysing the comparison between water consumption estimated using the Artificial Neural Networks (ANN) andhistorical data collected precisely. By referring to the actual operational data of water tank usage of the Boeing 787-9 fleet in a commercial airline, the flight hours, total passengers, and also actual portable water tank wereobtained. The data weregathered over twoyears,starting from January 2022 until December 2023,for day and night flight time. After all the collected data werecompleted, the optimisation of the water consumption was conducted and analysed using ANN. The optimised results were then compared using two conventional methods,IATA and Boeing. In conclusion, the ANNmethod has the least errorscompared to IATA and Boeing.The ANN method is suggested as a method to optimise thewater tank volume for commercial aircraft.

Nexus of Big Data and Big Data Analytics for Managerial Business Decision-Making: A Systematic Review Analysis

The growing usage of big data and big data analytics in business has prompted academics and professionals to widen their examination of their implications in business decision-making procedures. Until now, academics and business leaders have concentrated solely on the technical components of big data and analytics, ignoring the impact they have on the effectiveness of commercial decision-making systems. To begin, this paper intends to review the literature on the study of the relationship between the use of big data and big data analytics for its effectiveness in business industrial decision-making systems. Second, it gives important facts to assess whether big data and big data analytics catalyze the deployment of sophisticated business intelligence and informed decision-making representations. In this regard, the paper identifies the essential concerns that underpin the business-driven decision-making processes such as efficiency, and preciseness among others. Fundamentally, the current work contributes to the literature on big data and big data analytics for business-driven decision-making in both a theoretical fashion and provides a shot for future agenda possibilities to develop knowledge in this area.

Security and Privacy for Smart Transportation Management using Big Data Analytics

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

Design and simulation of solar pumping system using PVsyst, case study: TSABIT - ADRAR in Algeria

In isolated sites, the extension of the electricity network requires a very high capital cost. For agriculture work, irrigation is a necessity. A possible solution is using renewable energy sources like solar power, which is environmentally friendly and available for free. This paper presents the design and simulation of a photovoltaic water pumping system for irrigation of a farm located at a place named Tsabit in Adrar southwest Algeria. A detailed approach for the design of an optimized PV water pumping system based on real water usage data is proposed. Besides, system design work and performance assessment were carried out based on hourly climatic conditions. PV SYST software is used to carry out this work. A comparison was also made between two water pumping systems with and without use of a sun tracker. From the obtained results, the use of this kind of system could have an important contribution in the social and economic development of a country, especially in the presence of a sun tracker device, where we recorded more efficiency.

Improving Early Sepsis & Severe Sepsis Identification and Management on Internal Medicine service at the Detroit John D. Dingell VA Medical Center

Background: Sepsis is a life-threatening organ dysfunction due to infection, with an incidence of nearly 50 million people each year, causing about 11 million in mortality worldwide. Adhering to Sepsis protocols is a quality metric monitored by both the Centers for Medicare & Medicaid Services (CMS) via the SEP 1 measure and the VA Healthcare System through SEP 90, which consists of an all-or-nothing score comprised of individual metrics. These metrics include specific actions such as early management bundle utilization, lactate monitoring, timely fluid resuscitation, and vasopressor administration, all within defined timeframes. Based on FY 2023 Data, the John D. Dingell VAMC performed nationally in the last quintile with > 72% fallouts. Given that the vast majority of our Sepsis patients are treated and managed by the inpatient Internal Medicine resident service, we initiated a Quality Improvement project to educate them and thereby decrease our number of fallouts. We aim to improve Sepsis, severe Sepsis, & Septic shock Identification and Management by 50% over the course of 8 months on the internal medicine service. Methods: Internal medicine residents are central to the identification and management of early Sepsis and Severe Sepsis at the Detroit VA. Given the monthly turnover of resident teams, we focused on our model for improvement on interventions at the start of each month with targeted educational sessions to define Sepsis, severe Sepsis, and septic shock (and their management). We have also implemented targeted EMR training on utilizing a recently developed “Sepsis Bundle order set” for patients who have been identified as Sepsis. Sepsis Menu data usage was collected based on health factor tags on patients with Sepsis as their ICD-10 code on discharge from the internal medicine service. Results: Internal Medicine Resident educational sessions and EMR training commenced on July 1st, 2024. Sepsis Bundle was implemented in the EMR late 2022 due to rising fallouts of Sepsis and severe sepsis cases. However, its usage remained minimal, as depicted in Figure One, when compared to the number of discharged patients. Following the educational sessions and EMR training, both the identification and management of Sepsis have increased. Prior to intervention, from October 2022 to June 2023, a mean sepsis bundle usage rate of 10% was observed for identified sepsis patients, whereas post-intervention, from July 2023 to March 2024, the mean has increased to 30%. Conclusion/Discussion: Based on the early data, we can confidently note that our monthly education sessions, paired with our process measure (Sepsis bundle use), have been effective. Ongoing opportunities for improvement are improved coordination and handoff between ER and IM/MICU. We continue to see the need for ongoing resident education on identification and appropriate documentation of Sepsis severity along with obtaining labs within a certain time frame.

직업계열 고등학교 교사의 교육데이터 활용 현황과 요구 분석

As technology that enables data collection, accumulation and analysis develops, the use of data is being emphasized as the key of evidence-based policies. The purpose of this paper is to identify the current status of data collection related to vocational high school, analyze the current situation and needs of data usage by vocational high school teachers and suggest implications for future educational data construction. The main results of this paper are as follows. First, most of the collected data included vocational high school students and graduates as part of all high school students, young people or candidates for qualification. In particular, it was verified that data surveys using vocational high schools as parent population and linking administrative statistics and were insufficient. Second, vocational high school teachers who participated in this paper answered their data using frequency as ‘more than once a year'(28.0%), ’more than once a quarter’(26.1%), and ‘not at all'(23.3%). Teachers were using data for ‘writing a project plan or result report', ‘providing career counseling for students' and ‘developing and organizing a curriculum'. Teachers hoped to ‘link and integrate vocational high school-related data’ and data such as ‘students' interests and career plans’, ‘graduates' major retention rate’ and ‘predicting the supply and demand of high school graduates/college graduates by region/major/industrial field’ to be offered in the future,. Third, it is necessary to include vocational high school teachers to consider the context of vocational high school when promoting the collection and release of educational data, the use of administrative and learning data and the establishment of big data.

Berinovasi dengan TikTok: UMKM Lokal dan Langkah-Langkah Menghadapi Dinamika E-Commerce di Tahun 2024

In the growing digital era, the use of TikTok by local Micro, Small, and Medium Enterprises has become a significant concern in an effort to expand markets and increase sales. This article analyzes the challenges and strategies that can be adopted by in facing e-commerce dynamics and utilizing the TikTok Platform as E-Commerce effectively. The research method used is using a qualitative approach method by conducting literature studies and literature studies. The method for data collection is obtained from the official website (e-commerce usage data) and studying theories from various literatures related to the research. The results show the importance of efficient advertising budget management, optimization of logistics and delivery, improved skills in content creation, strategic collaboration, and campaign performance monitoring as key steps in facing challenges and taking advantage of opportunities in TikTok Commerce. Through a deep understanding of market dynamics and the implementation of appropriate strategies, MSMEs can strengthen their presence in TikTok Commerce and increase their competitiveness in the competitive e-commerce ecosystem. Translated with DeepL.com (free version)

HEALET-PATH TO WELLNESS

Abstract— In This research paper explores Healet, a platform designed to promote wellness and improve mental health. Healet integrates various tools and techniques to assist individuals in their journey towards better well-being. Through this platform, users can access resources such as guided meditation, stress management techniques, exercise routines, and personalized wellness plans.The paper investigates the effectiveness of Healet in enhancing mental health outcomes, particularly in reducing stress, anxiety, and depression. It examines user satisfaction with the platform and its impact on daily life functioning. Additionally, the research explores the accessibility and usability of Healet across different demographics. Through surveys, interviews, and usage data analysis, the study aims to provide insights into the strengths and limitations of Healet as a wellness resource. It also discusses potential areas for improvement and future directions for research and development in the field of digital mental health interventions. Index Term : Frontend development HTML, CSS, React, Backend NodeJS, Express, MongoDB

Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects

In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

Canva-based Smart Apps Creator Media to Enhance Comprehension Skills of Informational Text for Third-Grade Students

Learning media, as a tool for delivering information from teachers to students, aims to facilitate the teaching and learning process in school. Smart Apps Creator is a software with several advantages for developing interactive learning media, one of which is with the assistance of Canva application as its online design application. This research aims to create Canva-based Smart Apps Creator media to enhance the ability to understand the content of information texts in third-grade elementary schools. The research method used is Research and Development (R&D) based on Sugiyono's research with the Borg & Gall development model modified into eight stages, namely: potential and problems, data collection, product design, design validation, design revision, product testing, product revision, and usage testing. This research utilizes data collection techniques through tests and non-tests. The data analysis techniques used include both qualitative and quantitative data analysis. The material expert validation result obtained a percentage of 90%, and the media expert validation obtained a percentage of 92%, both categorized as "highly feasible." Using Canva-based Smart Apps Creator media in learning activities is quite effective through N-gain testing, with students' learning outcomes reaching 0.66 (65.6%), and the average pre-test score increased from 55 to 83 during the learning process (post-test). From these results, the Canva-based Smart Apps Creator media is declared highly suitable for learning and sufficiently effective in improving the ability to comprehend the content of information texts in Indonesian language classes for third-grade elementary school.

Comparison of datasets citation coverage in Google Scholar, Web of Science, Scopus, Crossref, and DataCite

The rapid increase of Earth science data from remote sensing, models, and ground-based observations highlights an urgent need for effective data management practices. Data repositories track provenance and usage metrics which are crucial for ensuring data integrity and scientific reproducibility. Although the introduction of Digital Object Identifiers (DOIs) for datasets in the late 1990s has significantly aided in crediting creators and enhancing dataset discoverability (akin to traditional research citations), considerable challenges persist in establishing linkage of datasets used with scholarly documents. This study evaluates the citation coverage of datasets from NASA’s Earth Observing System Data and Information System (EOSDIS) across several major bibliographic sources ‒ namely Google Scholar (GS), Web of Science (WoS), Scopus, Crossref, and DataCite—which helps data managers in making informed decisions when selecting bibliographic sources. We provide a robust and comprehensive understanding of the citation landscape, crucial for advancing data management practices and advancing open science. Our study searched and analyzed temporal trends across the bibliographic sources for publications that cite approximately 11,000 DOIs associated with EOSDIS datasets, yielding 17,000 unique journal and conference articles, reports, and book records linked to 3,000 dataset DOIs. GS emerged as the most comprehensive source while Crossref lagged significantly behind the other major sources. Crossref’s record references revealed that the absence of dataset DOIs and shortcomings in the Crossref Event data interface likely contributed to its underperformance. Scopus initially outperformed WoS until 2020, after which WoS began to show superior performance. Overall, our study underscores the necessity of utilizing multiple bibliographic sources for citation analysis, particularly for exploring dataset-to-document connections.