Interactive Visualization Platform Research Articles

The Development of an Automated System for a Quality Evaluation of Engineering BIM Models: A Case Study

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents

The road transportation of explosives is highly concerning due to its substantial impact on social safety. For the safety management of explosive transportation, e.g., transport route planning and emergency rescue, explosion consequence evaluation is of paramount importance. The consequence evaluation of explosion accidents is affected by many factors, especially spatial features, such as the location of transport vehicles, the distribution of buildings, and the presence of individuals around the road, etc. However, there is still a lack of quantification methods for building damage evaluation, human casualty evaluation that considers real-time population density, and efficient interactive damage evaluation methods. In this paper, we formalize three typical scenarios of damage evaluation for explosive road transportation accidents, i.e., explosion point-based, road segment-based, and route-based damage evaluation. For each scenario, we propose a Height-aware Hierarchical Building Damage (HHBD) model and a Shelter-aware Human Casualty (SHC) model for building damage evaluation and human casualty evaluation, respectively. We also develop a GIS-based interactive visualization platform that integrates multi-source geospatial data and that enables efficient geospatial computation. In addition, a case study of liquefied natural gas (LNG) transportation in Wuhan is demonstrated in order to verify the effectiveness and efficiency of the proposed system. The research results can support the decision-making process of explosive transportation safety warnings and emergency rescue.

Learning to see, seeing to learn: Impacts of an online tool on volunteers' observational practices during aquatic macroinvertebrate identification

AbstractScientific observation is a disciplinary‐informed way of looking at the world that requires the coordination of domain knowledge and perceptual skills with specialized tools and techniques to systematically identify objects, organisms, specimens, or phenomena of interest. Identification is a particular form of skilled observational practice where the observer recognizes key features for classification. Learning to see like a scientist is crucial for many citizen science activities, yet participants often have limited access to training and resources needed to practice. We posit that observational fluency development in taxonomic identification activities for citizen science has two components: (1) increased accuracy in noticing relevant features, and (2) use of more disciplinary‐based explanatory language; and self‐efficacy is crucial while developing these competencies. This study explores the extent to which an interactive visual identification platform supports learners' observational fluency development in the context of aquatic macroinvertebrate identification in environmental education. We examined changes in water quality monitoring volunteers' accuracy, self‐efficacy, and explanatory language use after participation in a training that integrated the online identification (ID) tool. Methods included surveys, focus groups, and an ID accuracy task before and after training. Our findings indicate that learners, especially novices, demonstrated increased self‐efficacy and accuracy in specimen identifications after participating in a training utilizing the online tool. They also shifted their ID justifications, incorporating more scientific terminology and salient diagnostic characters, and fewer generic descriptive adjectives. Results suggest that trainings using dynamic visual tools can positively impact observational fluency development and the quality of data contributed by volunteers.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations.

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Towards a cloud optimized data lake for archived water column sonar data

Due to their value to the ocean science and fisheries management communities, NOAA National Centers for Environmental Information (NCEI), with NOAA Fisheries and University of Colorado Cooperative Institute for Research in Environmental Sciences, established a national archive for water column sonar data. There are currently 210 TB of data freely and publicly available, and that volume is growing rapidly as sonar technology advances. The spatially and temporally diverse archive is accessible through its dedicated data portal and Amazon Web Services. Throughout 2023, we will develop a cloud-optimized data lake of echosounder files representing a ∼100 TB subset of the archive holdings. The echosounder files will be translated from their complex, binary and proprietary file format into zarr files following the Earth Science Information Partners analysis-ready cloud-optimized standards. The resulting data lake will serve as the foundation for building analytical capabilities that can cost-effectively tap into the archive’s sonar holdings, especially when coupled with compute power. The zarr stores will subsequently feed into EchoFish, the archive’s AWS-hosted interactive data visualization platform to facilitate subsetting and prevent the data lake from becoming a data swamp. The progress and potential applications of this NOAA Center for Artificial Intelligence funded project will be presented.

SimWrapper, an open source web-based platform for interactive visualization of microsimulation outputs and transport data

A new open-source, web-based, configurable data visualization platform is presented that is specifically designed to support large-scale transportation simulations including MATSim and ActivitySim. It produces a wide array of interactive charts, maps, animations and analysis dashboards that are generally useful in the transportation domain. Interactive visualizations can be created and viewed locally on an analyst's laptop, or public web-based dashboards can be published for viewing on the open Internet. The details of software design are provided along with several examples of implementation at public agencies. User feedback shows the platform is found to be very flexible, while the straightforward configuration approach enables efficient development and deployment of web-based interactive visualizations. While it is not intended to replace geographic information systems or commercial software packages, the smaller curated set of capabilities is found by users to warrant its current adoption at several public agencies. Further work is needed to add more useful features, improve the platform's quality and user experience, and extend documentation.

A Multiscale Geospatial Dataset and an Interactive Visualization Dashboard for Computational Epidemiology and Open Scientific Research.

The coronavirus disease (COVID-19) continued to strike as a highly infectious and fast-spreading disease in 2020 and 2021. As the research community actively responded to this pandemic, we saw the release of many COVID-19-related datasets and visualization dashboards. However, existing resources are insufficient to support multiscale and multifaceted modeling or simulation, which is suggested to be important by the computational epidemiology literature. This work presents a curated multiscale geospatial dataset with an interactive visualization dashboard under the context of COVID-19. This open dataset will allow researchers to conduct numerous projects or analyses relating to COVID-19 or simply geospatial-related scientific studies. The interactive visualization platform enables users to visualize the spread of the disease at different scales (e.g., country level to individual neighborhoods), and allows users to interact with the policies enforced at these scales (e.g., the closure of borders and lockdowns) to observe their impacts on the epidemiology.

E-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

Extended reality platform for minimally invasive endoscopic evacuation of deep-seated intracerebral hemorrhage: illustrative case

BACKGROUNDExtended reality (XR) offers an interactive visualization platform that combines virtual reality (VR) for preoperative planning and augmented reality (AR) for intraoperative navigation overlay.OBSERVATIONSXR was used for treating a case of spontaneous intracerebral hemorrhage (ICH) requiring neurosurgical intervention to decompress a hemorrhage in the subcortical area involving the thalamus that was starting to compress the midbrain. The selected surgical technique was an endoscopic aspiration combined with neurosurgical navigation. Because of the deep-seated location of this ICH, a patient-specific 360XR model rendered using Surgical Theater was used for preoperative planning and intraoperative navigation to allow for enhanced visualization and understanding of the pathology and surrounding anatomy.LESSONSThe XR platform enabled visualization of critical structures near the ICH by extracting and highlighting the white matter tracts from magnetic resonance imaging (MRI) with tractography, which improved preoperative planning beyond using state-of-the-art neuronavigation techniques alone. Once the trajectory was set, the model was integrated with the neuronavigation system, and the planned approach was referenced throughout the procedure to evacuate the clots without further injuring the brain. The patient tolerated the procedure well and was doing well 11 months after his spontaneous ICH.

Tree House Explorer: A Novel Genome Browser for Phylogenomics.

Tree House Explorer (THEx) is a genome browser that integrates phylogenomic data and genomic annotations into a single interactive platform for combined analysis. THEx allows users to visualize genome-wide variation in evolutionary histories and genetic divergence on a chromosome-by-chromosome basis, with continuous sliding window comparisons to gene annotations, recombination rates, and other user-specified, highly customizable feature annotations. THEx provides a new platform for interactive phylogenomic data visualization to analyze and interpret the diverse evolutionary histories woven throughout genomes. Hosted on Conda, THEx integrates seamlessly into new or pre-existing workflows.

Virtual reconstruction of the ancient city of Karakorum

AbstractRuins of ancient cities hold valuable information for historians and archeologists and it allows them to learn about the society and culture of these cities. This paper explores the first attempt to virtually rebuild the historic city of Karakorum based on ruins and archeological findings from the 13th century AD. Further, the analytical and practical methods used to discover its unique urban planning and architectural form by reconstructing the city using various resources from archeological documents to historical science documents are introduced. We explore a digitized version of Karakorum city in an immersive and embodied manner because of the latest technological advances in virtual reality, and this allows us to conserve and visualize its cultural heritage. The research objectives of this study are to (1) create a three‐dimensional visual model of the Karakorum city as close as possible to its real counterpart, and (2) use a game engine as a development platform for integration and interactive visualization.

CATHAI: cluster analysis tool for healthcare-associated infections.

Whole genome sequencing (WGS) is revolutionizing disease surveillance where it facilitates high-resolution clustering of related organism and outbreak detection. However, visualizing and efficiently communicating genomic data back to clinical staff is crucial for the successful deployment of a targeted infection control response. CATHAI (cluster analysis tool for healthcare-associated infections) is an interactive web-based visualization platform that couples WGS informed clustering with associated metadata, thereby converting sequencing data into informative and accessible clinical information for the management of healthcare-associated infections (HAI) and nosocomial outbreaks. All code associated with this application are free available from https://github.com/FordeGenomics/cathai. A demonstration version of CATHAI is available online at https://cathai.fordelab.com.

MalView: Interactive Visual Analytics for Comprehending Malware Behavior

Malicious applications are usually comprehended through two major techniques, namely static and dynamic analyses. Through static analysis, a given malicious program is parsed, and some representative artifacts (e.g., control-flow graphs) are produced without any execution; whereas, the given malicious application needs to be executed when conducting dynamic analysis. These two mainstream techniques for analyzing the given software are effective in detecting certain classes of malware. More specifically, through static analysis, the patterns and signature of the malware are exposed, helping in detecting any known malicious payload hidden in or injected into the code. On the other hand, behavioral and run-time execution patterns of software are explored through dynamic analysis. To ease the analysis process, a third analysis approach, known as the visual representation of the artifacts created by both static and dynamic analysis tools, would also be a supplementary asset for malware experts. This paper introduces “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MalView</i> ”, an interactive visualization platform, for malware analysis by which pattern matching techniques on both signature-based and behavioral analysis artifacts can be utilized to 1) classify malware, 2) identify the intention and location of the malicious payload in the artifacts, 3) analyze unknown malware (i.e., zero-day malware) by recognizing any unusual signature or behavior, and 4) explore the time dependencies and thus the system components affected or tampered by the underlying malware. The results of several case studies conducted in this work show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MalView</i> offers more features and information compared to some other visualization tools, facilitating the malware analysis process.

The Grading Model of College Minority Students Based on Visual Information Technology

At present, the most effective teaching tool is the interactive visualization platform, which directly or indirectly promotes the development of the education field. In addition, the development of this technology will affect many existing fields. The purpose of visual education is to help learners grasp relevant concepts in teaching more clearly. At the same time, visualization technology has a wide range of applications in various teaching fields of computer technology. In programming and data structure courses, the use of visualization teaching platform can deepen the understanding of learners. Based on the visualization information technology, this paper makes an in-depth study on the grading model of minority students in colleges and universities. This paper designs a scoring model by using visualization technology, and uses the model to collect teachers’ scoring results of various indicators of ethnic minority students in colleges and universities, and finally analyses the data.

An Apple Watch Dashboard for HiGHmed Heart Insufficency Patients.

Wearables are commercially available devices allowing continuous monitoring of users' health parameters. Their easy availability, increasing accuracy and functionality render them relevant for medical practice, specifically for longitudinal monitoring. There are clear benefits for the health care system, such as the opportunity of timely interventions by monitoring a patient during his daily life, resulting in a cost reduction in medical care and improved patient well-being. However, some tools are essential to enable the application of wearables in medical daily practice. For example, there is a need for software solutions that allow clinicians to quickly and easily analyze data from devices of their patients. The goal of this study was to develop a dashboard for physicians, which allows rapid data interpretation of longitudinal data from the Apple Watch. The prototype dashboard is an interactive web-based visualization platform utilizing Plotly. The dashboard displays the most important parameters like heart rate, steps per day, activity, exercise collected by the Apple Watch in a user-friendly and accessible way. Clear visualization makes it easy to identify trends or deviations in the data and see how these changes in daily behaviour affect patients' health. Our software is a key component to monitor patients with heart failure who participate in the HiGHmed use case cardiology project.

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

Modeling the dynamics of epidemics helps to propose control strategies based on pharmaceuticaland non-pharmaceutical interventions (contact limitation, lockdown, vaccination,etc). Hand-designing such strategies is not trivial because of the number of possibleinterventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning methods such as deep reinforcement learning might bring significant value. However, the specificity of each domain|epidemic modeling or solving optimization problems|requires strong collaborationsbetween researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers inepidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on QLearning with deep neural networks (DQN) and evolutionary algorithms (NSGA-II) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies fordynamical on-o lockdown control under the optimization of the death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (SEIR) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choicesto be made by health decision-makers. Trained models can be explored by experts and non-experts via a web interface.&#x0D; This article is part of the special track on AI and COVID-19.

Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

BackgroundDirect-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data.ResultHere, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization.ConclusionsSequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia.

CPA: a web-based platform for consensus pathway analysis and interactive visualization.

In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

INetModels 2.0: an interactive visualization and database of multi-omics data.

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint

GenomePaint (https://genomepaint.stjude.cloud/) is an interactive visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data of tumor samples. Its design captures the inter-relatedness between DNA variations and RNA expression, supporting in-depth exploration of both individual cancer genomes and full cohorts. Regulatory non-coding variants can be inspected and analyzed along with coding variants, and their functional impact further explored by examining 3D genome data from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and supports custom data upload. We used GenomePaint to unveil aberrant splicing that disrupts the RING domain of CREBBP, discover cis activation of the MYC oncogene by duplication of the NOTCH1-MYC enhancer in B-lineage acute lymphoblastic leukemia, and explore the inter- and intra-tumor heterogeneity at EGFR in adult glioblastomas. These examples demonstrate that deep multi-omics exploration of individual cancer genomes enabled by GenomePaint can lead to biological insights for follow-up validation.