Visualization Options Research Articles

Welcome to #SpoonieTok: Understanding and supporting disability expertise storytelling abilities and collective information practices on TikTok and beyond

This work-in-progress poster explores individual and collective storytelling and information creation practices in chronic illness TikTok communities, with the ultimate goal to support these embodied, creative, and multimodal abilities on and off-app through codesigned storytelling support materials. Findings from ongoing data collection and grounded theory coding, include folks’ sharing of disability expertise: using platform features to visualize invisible disability; audio, visual, and community-specific mimetic options. Interviews with 11 community members illuminate how disabled creators on TikTok engage in algorithmically-mediated online health communities, which have slippery boundaries where information can be found serendipitously through algorithmic ranking, and foster opportunities to develop storytelling skills.

Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?

Background and purposeDuring the ESTRO 2023 physics workshop on “AI for the fully automated radiotherapy treatment chain”, the topic of deep learning (DL) segmentation was discussed. Despite its widespread use in radiotherapy, the time needed to evaluate and correct DL segmentations remains burdensome. While segmentation uncertainty could be beneficial for clinicians, there is a lack of understanding on what information should be presented to ease their task. This study aimed to gather insights from clinicians on uncertainty visualisation options. Materials and methodsTwo sessions of structured interviews were conducted across four institutions already using DL segmentation clinically. The first session focused on the main problems hindering the clinical use of DL. In the second session, ten visualisation options displaying uncertainty information at different levels (structure, slice, or voxel) with binary or continuous values were presented. Dosimetric information was also present in some visualisations. For each case, sixteen clinicians (radiation oncologists and radiation therapists) were asked to grade an overall score, the usability, the training required, and the expected time gain. ResultsParticipants preferred the binary voxel-level uncertainty visualisation, followed by binary structure-level uncertainty visualisation. Combining structure-level and voxel-level visualisation methods has been proposed as a promising approach. The benefits of dosimetric information were perceived diversely among participants since it complexifies the display but could be useful for the online adaptive workflow. ConclusionPreferences for uncertainty visualisation methods were assessed within a multi-institutional experienced group of clinicians. Further refinement of preferences may help in selecting the best options for clinical implementation.

Mitochondrial Labeling with Mulberrin-Cy3: A New Fluorescent Probe for Live Cell Visualization.

Mitochondria, crucial intracellular organelles, are central to energy metabolism, signal transduction, apoptosis, calcium homeostasis, and a myriad of other biological processes, making them a focal point in diverse research fields. The capacity to fluorescently label and visually track mitochondria is crucial for understanding their biological roles. We present mulberrin-Cy3, a novel small molecule fluorescent probe that selectively labels mitochondria in animal cells, including cancer cells, with relative ease. This protocol details the synthesis of mulberrin-Cy3 and its use for visualizing mitochondria in living cells. The synthesis is straightforward and time-efficient, and the labeling method is more accessible than traditional approaches, providing a cost-effective option for mitochondrial visualization at room temperature. The labeling is rapid, with effective labeling achieved within 5 min of incubation. The fluorescent signal is stable and brighter, offering a significant advantage over existing methods. Mulberrin-Cy3 represents a promising mitochondrial labeling compound, providing researchers with a novel experimental tool to explore the complex biological functions of mitochondria. This innovation has the potential to significantly advance our comprehension of mitochondrial dynamics and their role in cellular health and disease.

MetaboLink: A web application for Streamlined Processing and Analysis of Large-Scale Untargeted Metabolomics Data.

The post-processing and analysis of large-scale untargeted metabolomics data face significant challenges due to the intricate nature of correction, filtration, imputation, and normalization steps. Manual execution across various applications often leads to inefficiencies, human-induced errors, and inconsistencies within the workflow. Addressing these issues, we introduce MetaboLink, a novel web application designed to process LC-MS metabolomics datasets combining established methodologies and offering flexibility and ease of implementation. It offers visualization options for data interpretation, an interface for statistical testing, and integration with PolySTest for further tests and with VSClust for clustering analysis. Fully functional tool is publicly available at https://computproteomics.bmb.sdu.dk/Metabolomics/. The source code is available at https://github.com/anitamnd/MetaboLink and a detailed description of the app can be found at https://github.com/anitamnd/MetaboLink/wiki. A tutorial video can be found at https://youtu.be/ZM6j10S6Z8Q. Supplementary data are available at Bioinformatics online.

SrpELTeC: A Serbian Literary Corpus for Distant Reading

The article presents SrpELTeC, a corpus developed within the COST action Distant Reading for European Literary History (CA16204). All novels in SrpELTeC were selected, prepared, and annotated using the common principles established for all language collections in the European Literary Text Collection (ELTeC). The challenges and solutions in preparing SrpELTeC from scratch are outlined. All novels were manually encoded in TEI with rich metadata and structural annotation. The automatic annotation included POS-tagging, lemmatization, and named entities, relying on Natural Language Processing resources developed and maintained by the JeRTeh Language Resources and Technologies Society. The integration of SrpELTeC with Wikidata was supported with a set of SPARQL queries for the retrieval of metadata with different visualization options. Recent activities within the COST Action NexusLinguarum—European Network for Web-centred Linguistic Data Science (CA18209) are related to the linked data version of SrpELTeC using the NLP Interchange Format. All versions of SrpELTeC are freely available under the CC-BY license.

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.

To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.

Modernization Data Analysis and Visualization for Food Safety Research Outcomes

Appropriate data collection and using reliable and accurate procedures are the first steps in conducting an experiment that will provide trustworthy outcomes. It is key to perform an assertive statistical analysis and data visualization for a correct interpretation and communication of results. A clear statistical summary and presentation of the data is critical for the reader to easily process and comprehend experimental results. Nowadays, there are a series of different tools to perform proper statistical analysis and create elaborate graphs that will help readers to understand the data, identify trends, detect outliers, evaluate statistical outputs, etc. However, researchers that are beginning to navigate experiments do not frequently encounter a guide that can provide basic principal concepts to begin their statistical analysis and data presentation. Therefore, the objective of this article is to provide a guide or manual to analyze and presents results focused on different types of common food safety experiments, including method comparisons, intervention studies, pathogen presence experiments, bio-mapping, statistical process control, and shelf life experiments. This review will provide information about data visualization options and statistical analysis approaches for different food safety experiments. In addition, basic concepts about descriptive statistics and possible solutions for issues related to microbiological measurements will be discussed.

Ficus Genome Database: A Comprehensive Genomics and Transcriptomics Research Platform

Ficus is a significant genus within the Moraceae family, primarily native to tropical and subtropical regions. It plays a crucial role in the study of co-evolution and genetics in the fig–fig wasp symbiosis. Advancements in sequencing technology have facilitated whole-genome sequencing of several Ficus species, accumulating vast amounts of genomic and transcriptomic data available in public databases. To streamline data integration, display, and analysis, we developed the Ficus Genome Database (FGD), a consolidated platform for the genomic data of five Ficus species, and self-assembled transcriptome data for 24 fig ostiolar bracts. The FGD is currently home to a diverse array of data, encompassing genome and gene sequences, annotations of genes, transcriptome analyses, biochemical pathways, non-coding RNA, and findings from comparative genomic studies, such as collinear blocks across different Ficus genome assemblies. To enhance translational and practical research concerning Ficus, FGD provides an extensive suite of accessible query interfaces, analytical instruments, and visualization options. These include the NCBI BLAST sequence search tool and the JBrowse/GBrowse genome browser. FGD also offers several distinct tools, including a genome Synteny Viewer, expression heatmap display, gene family identification, Gene Ontology terms enrichment, and pathway enrichment analysis.

MetaAnalysisOnline.com: A new easy-to-use bioinformatic tool for the meta-analysis of clinical trials.

e23012 Background: A meta-analysis is a formal, quantitative study design in epidemiology and clinical medicine that systematically assesses and consolidates previous research findings to derive comprehensive conclusions on a specific topic. Here, we aimed to establish a new tool for the visualization options useful for conducting a meta-analysis specifically designed for oncology trials. Methods: A meta-analysis can be based on binary data, continuous data, or time-to-event data. The most crucial statistical models, including the random effect model and the fixed effect model, are outlined alongside their respective statistical methods. Furthermore, we implemented graphical representations such as the forest plot, funnel plots, and Z-scope plots. While the forest plot effectively illustrates heterogeneity and pooled results, a funnel plot can reveal potential publication bias, and a Z-score plot shows the robustness of the used sample number. Results: The models and visualization options outlined have been integrated into a new, online web portal which can be accessed without the need for registration. The web application operates on an Ubuntu server running Apache and enables users to conduct meta-analyses using results typically used in oncology trials including binary (total and event numbers), continuous (mean and standard deviation data), and time-to-event data (hazard rate and CI data). Study results from common spreadsheet applications like Excel can be directly input into the system. The online platform utilizes the meta and metafor packages in the R programming environment (R version 4.2.2) for calculations and plots. The Shiny user interface is powered by shinyjs, shinydashboard, and rhandsontable R packages. The portal generates a forest plot to summarize meta-analysis results, a funnel plot for visual detection of potential bias, and a Z-score plot showing the sufficiency of the cumulative sample number. The analysis results of the tool were validated using IBM SPSS v28. The new bioinformatic tool is available at www.metaanalysisonline.com and requires no programming knowledge or command line use. Conclusions: Our platform aims to provide a user-friendly meta-analysis tool for epidemiological studies and oncology clinical trials, offering a swift and reproducible way to integrate the results from multiple studies.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

AbstractFault structure reconstruction is an important aspect of magnetic prospecting as it contributes to understanding tectonic history, hazard assessment, and infrastructure planning. Metaheuristics have proven useful in estimating fault structure parameters from geophysical anomalies. Recently, a modified version of the Backtracking Search Optimization Algorithm (BSA), named mBSA, has been proposed that combines mutation steps of BSA and Differential Evolution (DE) algorithms to achieve a better balance between exploration and exploitation. Here, we present imBSA as an efficient approach that builds on the improvements of mBSA. It uses an adaptive amplitude control factor in the mutation step derived from a normally distributed random number obtained via an exponential function. The performances of these approaches were tested on some widely used benchmark functions such as Rosenbrock, Rastrigin, Griewank, and Himmelblau. Synthetic magnetic fault anomalies were evaluated with and without noise, as well as three real anomalies from the Dehri (India), Perth Basin, and Lachlan Fold Belt (Australia). Prior to the optimizations, the solvability of the model parameters was examined using some error topography images. The obtained solutions in the synthetic cases were evaluated using principal component analysis. In addition, a novel visualization option for high‐dimensional optimization problems was introduced. The experiments showed that imBSA has a faster convergence rate and better optimization statistics compared to BSA and mBSA. Therefore, it is believed to be an efficient and good alternative tool to widely used metaheuristics such as Particle Swarm Optimization and DE in explaining geophysical anomalies and optimization problems in other geoscientific disciplines.

Esra (elevator simulation, research & analysis): an open-source software tool for elevator traffic simulation, research, and analysis

ABSTRACT Today, although elevator companies typically have their own elevator simulation and analysis tools, these remain inaccessible to the public due to commercial concerns and rivalry in the sector. Consequently, due to the lack of modular open-source software tools, interest in the design and development of elevator systems in academia is quite limited, contrary to the vast potential of the field. This paper addresses this gap introducing an open-source software tool, the Elevator Simulator for Research and Analysis (ESRA), developed using the MATLAB programming language and an object-oriented approach. ESRA features built-in control methods, and visualization options with the capability to save and reuse traffic data, facilitating in-depth comparative analysis. Moreover, its modular architecture allows a design and development basis for further research in the field. The proposed software aims to enable developers to design, implement, analyse, and compare their control algorithms with other methods.

OralExplorer: a web server for exploring the mechanisms of oral inflammatory diseases.

Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis. Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC. A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1β, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group. OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/ (Alternate URL: http://robinl-lab.com/OralExplorer ).

Improvements to a mobile app for community noise and assessment data collection

A mobile phone application was developed to collection health history, real time noise levels and participants' perceptions. The goal of this application is to collect data that can directly link community response, noise levels and community health from a large number of participants. This will help identify where noise is causing issues related to physical and mental health on a community scale. In order to identify these effects at a community level large numbers of responses are critical. Ease of use is essential to ensure that participants remain willing to participate in the study and continue to submit samples. This needs to be balanced with the usefulness of the data collected by the public. Previously, a beta version of the app was deployed to evaluate the useability and effectiveness of data collection. Several issues with the data collection were identified and improvements to the app were implemented. Evaluation of the data and data visualization options will be presented using the most recent version of the application.

Interactive electronic visualization formats in student teaching

In the context of contact restrictions, conventional teaching is currently in need of optimization and expansion. The range of digital teaching formats in student training is very heterogeneous and their effectiveness uncertain. This study aims to investigate the extent to which an electronic ward round can be used as an alternative to the conventional ENT attendance practical course, and whether the use of electronic teaching formats has an influence on the quality of teaching. Instead of regular attendance practicals, bedside teaching took place once aweek in real time as avideo stream via tablet. Atotal of 43students in the seventh semester (winter semester 2020/2021) were included in the prospective study. Evaluation forms were used to examine the subjective didactic value of different visualization formats for the students. Examination results from previous years were used for comparison. The majority of students reported knowledge gain from the electronic rounds (93.02%) and that they were agood alternative to the traditional attendance clerkship (69.77%). The quality of the video and audio transmission as well as the comprehensibility of the case studies presented were consistently rated as good to very good. The students' examination results tended to be slightly worse in the test group compared to the control students of previous years. Integration of innovative interactive visualization options into teaching shows promising prospects as asupplement to conventional face-to-face teaching. The results of this study can contribute to the further expansion of digital teaching. Scaling up this model could be considered especially in countries with limited availability of face-to-face teaching.

MODERN METHODS FOR DIAGNOSTICS OF GLAUCOMA

Glaucoma is a chronic optical neuropathy characterized by loss of ganglion cells with the development of specific changes in the optic nerve head (ONH) and retinal nerve fiber layer (RNFL). Early detection of the disease plays an important role in preventing the development structural abnormalities and irreversible vision loss. Diagnosis of glaucoma is based on assessing the safety of optic nerve structures and visual functions results clinical examination of the optic disc and RNFL are subjective and vary greatly. In connection with this, research in recent years has been aimed at developing additional objective methods for diagnosing glaucoma. The possibility of using confocal scanning laser ophthalmoscopy, scanning laser perimetry and optical coherence tomography to assess the condition of the optic disc. In order to ensure early detection of visual field defects options are currently being considered replacing standard automated perimetry (SAP) with selective, which includes short-wavelength automated perimetry (SWAP) and perimetry with the illusion of spatial doubling frequencies (frequency-doubling technology perimetry, FDT). The article is an overview modern method for diagnosing glaucoma in the context of their use in clinical practice.

Deploying Machine Learning for Radiography of Aerospace Welds

Artificial intelligence is providing new possibilities for analysis in the field of industrial radiography. As capabilities evolve, there is the need for knowledge concerning how to deploy these technologies in practice and benefit from the new automatically generated information. In this study, automatic defect recognition based on machine learning was deployed as an aid in industrial radiography of laser welds in an aerospace component, and utilized to produce statistics for improved quality control. A multi-model approach with an added weld segmentation step improved the inference speed and decreased false calls to improve field use. A user interface with visualization options was developed to display the evaluation results. A dataset of 451 radiographs was automatically analysed, yielding 10037 indications with size and location information, providing capability for statistical analysis beyond what is practical to carry out with manual annotation. The distribution of indications was modeled as a product of the probability of detection and an exponentially decreasing underlying flaw distribution, opening the possibility for model reliability assessment and predictive capabilities on weld defects. An analysis of the indications demonstrated the capability to automatically detect both large-scale trends and individual components and welds that were more at risk of failing the inspection. This serves as a step towards smarter utilization of non-destructive evaluation data in manufacturing.

Creation of 3D landscape in AutoCAD Civil 3D according to data from MapInfo

The article presents the technology of constructing a 3D model of landscape in AutoCAD Civil 3D based on data from Mapinfo. The source material is a raster cadastral map. The resulting 3D model of landscape and visualization options are presented in this work.

Customizable automated cleaning of multichannel sleep EEG in SleepTrip.

While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.

The impact of sound and immersive experience on learners when using virtual reality and tablet: A mixed‐method study

Multisensory‐rich VR experiences, which encompass visual, auditory, and haptic stimuli, have the potential to enhance engagement, motivation, and learning. However, extensive sensory stimuli could also compromise learning through sensory overload. In museum settings, visitors who are inundated with excessive stimuli such as unrelated background music or competing visual options may not experience optimal learning. This mixed‐methods study addressed this potential problem by exploring the impact of sound on learning, enjoyment, sense of presence, and the development of interest among museum attendees (N = 255) who used two different types of technology: high‐immersion VR or a tablet. Results from a one‐way MANOVA revealed that learning and sense of presence were unaffected by the technology used unless sound was added. Using a tablet with sound lowered the sense of presence. Participants in the VR condition with or without sound had significantly higher enjoyment scores than those in either tablet condition. The development of interest was not significantly affected by any condition, regardless of whether sound was used. The research findings have implications for implementing VR for learning in museum settings. For instance, VR can enhance a sense of presence in museum exhibits, but a sense of presence may be significantly diminished when museum visitors use tablets with sound. Additional recommendations for the use of multimodal VR in museums are provided. Practitioner notesWhat is already known about this topic VR can support an immersive and engaging experience for users. VR is more immersive than tablets. When designed effectively, sound can increase immersion and engagement. When integrated into the visual environment, sound can add authenticity to the learning content. VR can complement museum exhibits as it allows museum visitors to extend their learning experiences. What this paper adds VR enriched with sound can significantly and positively impact sense of presence. Compared with a soundless VR exhibition, VR enriched with sound enhances engagement of museum visitors by drawing their attention to the content of the exhibits. VR with and without sound can support learning and add enjoyment to museum visitor experiences. Implications for practice and/or policy Developers interested in adding sound to VR should ensure that the visual and auditory experiences are directly related to the desired content delivery. Museum exhibit creators and curators should consider the use of VR to provide dynamic learning experiences that increase enjoyment and presence.