Interactive Data Visualization Tool Research Articles

Assuring and Improving Learning Outcomes

Universities have a responsibility to measure and assure graduate learning outcomes ("Higher Education Standards Framework (Threshold Standards)," 2021; TEQSA, 2017). However, while assuring learning outcomes is a priority across the sector, many universities fail to collect meaningful data on student achievement of these outcomes (Martin & Mahat, 2017). Some institutions are investigating technologically enhanced processes to improve the efficiency and accuracy of learning-achievement data (Burrack & Musselwhite Thompson, 2021; El Marsafawy, 2022). Over the past four years, the University of Technology Sydney (UTS) Business School has made substantial enhancements to assurance of learning (AoL) practices, leveraging learning management system (LMS) functionality and data visualisation software to create a streamlined process, which better facilitates the continuous improvement of programs. This work is of particular interest to academic and professional staff involved in collecting and reporting on student achievement data, in program continuous improvement and evaluation, or in leveraging technology-enhanced learning analytics. The UTS Business School’s AACSB accreditation necessitates the collection, tracking, and reporting of student achievement of program-level learning outcomes (AACSB, 2020). Each program identifies subjects to "develop" and "assure" learning outcomes, with student achievement measured within specific assessment tasks and collated for program-level analysis. This process can be administratively complex, and historically involved multiple manually collated spreadsheets. This represented a substantial time cost for the faculty and often resulted in unreliable data. It was, for example, common practice to report overall subject marks rather than the specific learning outcome to determine achievement level, resulting in assessment practices that diverged from the recommended standards of constructive alignment (Biggs, 2014). In 2021, faculty T&L and central learning technologies teams commenced a pilot using LMS tools and the interactive data visualization tool PowerBI to streamline data collection, collation, and visualisation. Canvas Outcomes set up at the faculty level allows for centralised achievement-data collection, which, when linked to student and program information in PowerBI, can be used to obtain program-level insights. Beyond administrative efficiency, the project was also designed to improve and foreground the constructive alignment (Biggs, 2014) of measured learning outcomes. The revised process requires the embedding of learning-outcome-aligned assessment criteria within rubrics, resulting in improved constructive alignment and data transparency. The initial pilot was evaluated using Human Centred Design (IDEO.org, 2015) methods, which concluded the new system was an improvement on previous processes. Since the pilot, the faculty has completed a full rollout to over 60 subjects and 20 programs. Once this foundational AoL process was in place, program and subject-level continuous-improvement projects could be built upon it. Previous reporting methods had limited usefulness to Program Directors and were no longer fit for purpose due to changes in AACSB requirements. Reporting processes were therefore adjusted to focus on continuous improvement of key program metrics. In the new process, Program Directors and teaching teams review program data to identify curriculum or student-experience improvements that will enhance student achievement of learning outcomes. For example, a master's program identified that a high percentage of students were falling below expectations in two outcomes. Investigating the causes identified a need for substantial changes, with new subjects taking assurance responsibilities, and revisions to the program's overall assessment strategy. Recent data shows significant improvements in the target outcomes. This presentation will outline these updated technology-enhanced AoL processes demonstrating how the collected data can be used to improve university programs and the student learning experience.

Exploring Crossmodal Associations Between Sound and the Chemical Senses: A Systematic Review Including Interactive Visualizations.

This is the first systematic review that focuses on the influence of product-intrinsic and extrinsic sounds on the chemical senses involving both food and aroma stimuli. This review has a particular focus on all methodological details (stimuli, experimental design, dependent variables, and data analysis techniques) of 95 experiments, published in 83 publications from 2012 to 2023. 329 distinct crossmodal auditory-chemosensory associations were uncovered across this analysis. What is more, instead of relying solely on static figures and tables, we created a first-of-its-kind comprehensive Power BI dashboard (interactive data visualization tool by Microsoft) on methodologies and significant findings, incorporating various filters and visualizations allowing readers to explore statistics for specific subsets of experiments. We believe that this review can be helpful for researchers and practitioners working in the food and beverage industry and beyond these scopes (e.g., cosmetics). Theoretical and practical implications discussed in this article point to computational approaches that facilitate decision-making regarding multisensory experimental methodology design.

The pear genomics database (PGDB): a comprehensive multi-omics research platform for Pyrus spp.

BackgroundPears are among the most important temperate fruit trees in the world, with significant research efforts increasing over the last years. However, available omics data for pear cannot be easily and quickly retrieved to enable further studies using these biological data.DescriptionHere, we present a publicly accessible multi-omics pear resource platform, the Pear Genomics Database (PGDB). We collected and collated data on genomic sequences, genome structure, functional annotation, transcription factor predictions, comparative genomics, and transcriptomics. We provide user-friendly functional modules to facilitate querying, browsing and usage of these data. The platform also includes basic and useful tools, including JBrowse, BLAST, phylogenetic tree building, and additional resources providing the possibility for bulk data download and quick usage guide services.ConclusionsThe Pear Genomics Database (PGDB, http://pyrusgdb.sdau.edu.cn) is an online data analysis and query resource that integrates comprehensive multi-omics data for pear. This database is equipped with user-friendly interactive functional modules and data visualization tools, and constitutes a convenient platform for integrated research on pear.

RetroBioCat Database: A Platform for Collaborative Curation and Automated Meta-Analysis of Biocatalysis Data.

Despite the increasing use of biocatalysis for organic synthesis, there are currently no databases that adequately capture synthetic biotransformations. The lack of a biocatalysis database prevents accelerating biocatalyst characterization efforts from being leveraged to quickly identify candidate enzymes for reactions or cascades, slowing their development. The RetroBioCat Database (available at retrobiocat.com) addresses this gap by capturing information on synthetic biotransformations and providing an analysis platform that allows biocatalysis data to be searched and explored through a range of highly interactive data visualization tools. This database makes it simple to explore available enzymes, their substrate scopes, and how characterized enzymes are related to each other and the wider sequence space. Data entry is facilitated through an openly accessible curation platform, featuring automated tools to accelerate the process. The RetroBioCat Database democratizes biocatalysis knowledge and has the potential to accelerate biocatalytic reaction development, making it a valuable resource for the community.

Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System.

Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles.

Exploring News Innovations, Journalistic Routines, and Role Perceptions during the COVID-19 Pandemic: A Case Study of Egyptian Journalists

ABSTRACT Drawing on in-depth interviews with 21 Egyptian journalists, this study offers a comprehensive understanding of how the COVID-19 pandemic has reconfigured news work and journalistic routines in Egyptian newsrooms. In so doing, the paper examines the various technical and logistical challenges encountered by Egyptian journalists as well as their changing role perceptions during the emergency health crisis. It further investigates the innovative practices adopted by newsrooms, on both the news content and newsroom levels, to overcome those challenges in terms of sourcing norms, news production routines, and storytelling formats. Findings demonstrate that Egyptian journalists struggled at the beginning of the pandemic, but started to find creative solutions to overcome the many practical and infrastructural challenges. The pandemic accelerated the digitization of editorial news production systems and the unprecedented adoption of news automation and artificial intelligence technologies. More dependence on interactive digital storytelling formats and data visualization tools was reported. Journalistic innovation occurred therefore as recombination of already existing products or services, but not new inventions. In terms of role perception, there was a shift to a more facilitative role, in which science and more quantitatively oriented forms of journalism play a central role.

Firefly: A Browser-based Interactive 3D Data Visualization Tool for Millions of Data Points

We present Firefly, a new browser-based interactive tool for visualizing 3D particle data sets. On a typical personal computer, Firefly can simultaneously render and enable real-time interactions with ≳10 million particles, and can interactively explore data sets with billions of particles using the included custom-built octree render engine. Once created, viewing a Firefly visualization requires no installation and is immediately usable in most modern internet browsers simply by visiting a URL. As a result, a Firefly visualization works out-of-the-box on most devices including smartphones and tablets. Firefly is primarily developed for researchers to explore their own data, but can also be useful to communicate results to researchers and/or collaborators and as an effective public outreach tool. Every element of the user interface can be customized and disabled, enabling easy adaptation of the same visualization for different audiences with little additional effort. Creating a new Firefly visualization is simple with the provided Python data preprocessor that translates input data to a Firefly-compatible format and provides helpful methods for hosting instances of Firefly both locally and on the internet. In addition to visualizing the positions of particles, users can visualize vector fields (e.g., velocities) and also filter and color points by scalar fields. We share three examples of Firefly applied to astronomical data sets: (1) the FIRE cosmological zoom-in simulations, (2) the SDSS galaxy catalog, and (3) Gaia Data Release 3. A gallery of additional interactive demos is available at alexbgurvi.ch/Firefly.

Use of serial smartphone-based assessments to characterize diverse neuropsychiatric symptom trajectories in a large trauma survivor cohort

The authors sought to characterize adverse posttraumatic neuropsychiatric sequelae (APNS) symptom trajectories across ten symptom domains (pain, depression, sleep, nightmares, avoidance, re-experiencing, anxiety, hyperarousal, somatic, and mental/fatigue symptoms) in a large, diverse, understudied sample of motor vehicle collision (MVC) survivors. More than two thousand MVC survivors were enrolled in the emergency department (ED) and completed a rotating battery of brief smartphone-based surveys over a 2-month period. Measurement models developed from survey item responses were used in latent growth curve/mixture modeling to characterize homogeneous symptom trajectories. Associations between individual trajectories and pre-trauma and peritraumatic characteristics and traditional outcomes were compared, along with associations within and between trajectories. APNS across all ten symptom domains were common in the first two months after trauma. Many risk factors and associations with high symptom burden trajectories were shared across domains. Both across and within traditional diagnostic boundaries, APNS trajectory intercepts, and slopes were substantially correlated. Across all domains, symptom severity in the immediate aftermath of trauma (trajectory intercepts) had the greatest influence on the outcome. An interactive data visualization tool was developed to allow readers to explore relationships of interest between individual characteristics, symptom trajectories, and traditional outcomes (http://itr.med.unc.edu/aurora/parcoord/). Individuals presenting to the ED after MVC commonly experience a broad constellation of adverse posttraumatic symptoms. Many risk factors for diverse APNS are shared. Individuals diagnosed with a single traditional outcome should be screened for others. The utility of multidimensional categorizations that characterize individuals across traditional diagnostic domains should be explored.

CrisisReady's novel framework for transdisciplinary translation: Case-studies in wildfire and hurricane response

Extreme weather events including wildfires and hurricanes are becoming increasingly hazardous due to climate change, and often result in transient or permanent population displacements. Disaster-related disruptions in infrastructure, workforce, wages, and social networks can combine with population displacements to result in interruptions in health care access and prolonged impacts on morbidity and mortality. The data needed to make health systems and emergency management approaches more resilient to these hazards, and more responsive to the needs of affected populations, are sequestered in silos across private corporations and public agencies. In two case studies, we describe how our research team at CrisisReady negotiated access to privately held and novel data sources like anonymized geolocation data from cell-phones, while striking a balance between data security and public health utility. We describe how our analytic tools are embedded into disaster response workflows by co-developing our research questions and outputs with responders and policy-makers. ReadyMapper, an interactive data visualization tool to track population mobility, infrastructure damage, and health system capacity, in near real-time, was deployed during wildfires in California and during the Hurricane Ida response in Louisiana. The Data-Methods-Translational framework we have developed is scalable and relies on sharing science and co-creating products with policy makers and response agencies to ensure real-world applicability. These attributes make the framework particularly useful for formulating evidence-based approaches to protect human health through climate change adaptation.

Implementation of business intelligence system to analyze the data for mining mechanization – case study

In this article, we focused on the experimental implementation of the Power BI application in the field of mining mechanization. This application was created to demonstrate how an interactive data visualization tool can streamline data exploration and provide new insights that can be used to optimize selected processes. The monitored company uses a large number of reports in connection with the organization of the company. They collect many data from the attendance system and the electronic resource planning system, but also a lot of operational data related to the performance of mining mechanization. These data are evaluated in the company only at one-month intervals. Our intention was to transform the data for the year 2021 and create various visual reports through the power BI program, which, thanks to detailed analysis and evaluation, will help to improve the management of mining mechanization. The source of information in our case were tables that contained very poorly arranged data that could not be imported into the Power BI program in the given state. After collecting the data, we had to transform them, which was a very time-consuming process. Due to limited resources, we had to adjust our original plan. From the entire portfolio of mining mechanization, which consists mainly of mining drills, loaders and trucks, we decided to only process data from mining trucks. The result will be a demonstration of the usability of the power BI system in a mining company to manage information related to mining trucks, but the same approach can be used for all types of mining mechanisms. Implementation of the power BI system was performed on data from one of the biggest underground mines in Slovakia.

‘The tool didn’t make decisions for us': metrics and the performance of accountability in environmental governance

ABSTRACT Governments use metrics made possible by new data technologies to allocate budgets, manage pandemics, valorize ecosystems, and demonstrate how these actions are legitimate. Big data is pointed to as providing objective answers that emerge untampered from observations of the world as it is – a view from nowhere. While data is more valued than ever in environmental governance, so too are arrangements that seek stakeholders’ input and otherwise address their subjective interests – a view from everywhere. Different kinds of metrics perform state actors as accountable in both registers: metrics that are responsive to dynamic conditions; that account for specific stakeholders; that can be prioritized against one another in interactive data visualization tools. Louisiana, USA’s Coastal Master Plan is an attempt to stem wetlands loss through fine-scale modeling of large volumes of data and calculation of these kinds of social and environmental metrics. State actors there make accountability claims that appear contradictory: their decisions are legitimate because they are driven by the best available coastal science and technology, while their data tools ‘didn’t make decisions for us.’ As state actors deploy environmental big data and metrics to make sense of it, we should be able to explain these apparently contradictory stances and the controversies that result. STS theory on metrics in environmental governance benefits from characterizing how ‘modes of authorized seeing’ are given expression by different metrical forms and what brings modes into contact and conflict.

A Method to Explore Variations of Ventilator-Associated Event Surveillance Definitions in Large Critical Care Databases in the United States.

Retrospective, multicenter, cross-sectional analysis. Three hundred forty critical care units across 209 hospitals, comprising 261,910 patients in both the electronic Intensive Care Unit Clinical Research Database and Medical Information Mart for Intensive Care III databases. A total of 14,517 patients undergoing MV for 4 or more days. We designed a statistical analysis framework, complemented by a custom interactive data visualization tool to depict how changes to the VAC surveillance definition alter its prognostic performance, comparing patients with and without VAC. This methodology and tool enable comparison of three clinical outcomes (hospital mortality, hospital length-of-stay, and ICU length-of-stay) and provide the option to stratify patients by six criteria in two categories: patient population (dataset and ICU type) and clinical features (minimum Fio2, minimum positive end-expiratory pressure, early/late VAC, and worst first-day respiratory Sequential Organ Failure Assessment score). Patient population outcomes were depicted by heatmaps with mortality odds ratios. In parallel, outcomes from ventilation setting variations and clinical features were depicted with Kaplan-Meier survival curves. We developed a method to examine VAC using information extracted from large electronic health record databases. Building upon this framework, we developed an interactive tool to visualize and quantify the implications of variations in the VAC surveillance definition in different populations, across time and critical care settings. Data for patients with and without VAC was used to illustrate the effect of the application of this method and visualization tool.

Interactive visual study for residential energy consumption data

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions’ responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic.

New Jersey maternal mortality dashboard: an interactive social-determinants-of-health tool.

The United States maternal mortality (MM) rate is the highest amid developed/industrialized nations, and New Jersey's rate is among the highest. Healthcare professionals, public health officials, and policy makers are working to understand drivers of MM. An interactive data visualization tool for MM and health-related information (New Jersey Maternal Mortality Dashboard [NJMMD]) was recently developed. NJMMD is an open-source application that uses data from publicly available state/federal government sources to provide a cross-sectional, high-level depiction of potential relationships between MM and demographic, social, and public health factors. MM rates or ratios (maternal deaths/1,000 women aged 15-49 years or 100,000 live births, respectively) are available by year (2005-2017), age (5-year [15-49] periods), and race/ethnicity (non-Hispanic White, Black, or Asian; Hispanic; or other), and by contextual social determinants of health (percent insured; percent covered by Medicaid; difference in nulliparous, term, singleton, vertex Cesarian birth rate from New Jersey goal; number of obstetrician/gynecologists or midwives per capita; and poverty rate). Bar graphs also can be produced with these variables. NJMMD is the first publicly available, interactive, state-focused MM tool that takes into account the intersection of social and demographic determinants of health, which play important roles in health outcomes. Trends and patterns in variables associated with MM and health can be identified for New Jersey and each of its 11 counties, and inform areas of focus for further analysis. Outputs may enable researchers, policy makers, and others to develop appropriate interventions and be better positioned to set benchmarks, allocate resources, and evaluate outcomes.

America's HIV Epidemic Analysis Dashboard: Protocol for a Data Resource to Support Ending the HIV Epidemic in the United States.

BackgroundThe Ending the HIV Epidemic (EHE) plan aims to end the HIV epidemic in the United States by 2030. Having timely and accessible data to assess progress toward EHE goals at the local level is a critical resource to achieve this goal.ObjectiveThe aim of this paper was to introduce America’s HIV Epidemic Analysis Dashboard (AHEAD), a data visualization tool that displays relevant data on the 6 HIV indicators provided by the Centers for Disease Control and Prevention. AHEAD can be used to monitor progress toward ending the HIV epidemic in local communities across the United States. Its objective is to make data available to stakeholders, which can be used to measure national and local progress toward 2025 and 2030 EHE goals and to help jurisdictions make local decisions that are grounded in high-quality data.MethodsAHEAD displays data from public health data systems (eg, surveillance systems and census data), organized around the 6 EHE indicators (HIV incidence, knowledge of HIV status, HIV diagnoses, linkage to HIV medical care, viral HIV suppression, and preexposure prophylaxis coverage). Data are displayed for each of the EHE priority areas (48 counties in Washington, District of Columbia, and San Juan, Puerto Rico) which accounted for more than 50% of all US HIV diagnoses in 2016 and 2017 and 7 primarily southern states with high rates of HIV in rural communities. AHEAD also displays data for the 43 remaining states for which data are available. Data features prioritize interactive data visualization tools that allow users to compare indicator data stratified by sex at birth, race or ethnicity, age, and transmission category within a jurisdiction (when available) or compare data on EHE indicators between jurisdictions.ResultsAHEAD was launched on August 14, 2020. In the 11 months since its launch, the Dashboard has been visited 26,591 times by 17,600 unique users. About one-quarter of all users returned to the Dashboard at least once. On average, users engaged with 2.4 pages during their visit to the Dashboard, indicating that the average user goes beyond the informational landing page to engage with 1 or more pages of data and content. The most frequently visited content pages are the jurisdiction webpages.ConclusionsThe Ending the HIV Epidemic plan is described as a “whole of society” effort. Societal public health initiatives require objective indicators and require that all societal stakeholders have transparent access to indicator data at the level of the health jurisdictions responsible for meeting the goals of the plan. Data transparency empowers local stakeholders to track movement toward EHE goals, identify areas with needs for improvement, and make data-informed adjustments to deploy the expertise and resources required to locally tailor and implement strategies to end the HIV epidemic in their jurisdiction.

Playing the “Data”: Constructing Interactive and Enjoyable Experiences of Social Justice-Oriented Contemporary Art

Many art educators are committed to engaging students with social justice issues to guide them “to know themselves and their worlds, and to live and act as part of community and society as critical citizens” (Garber, 2004, p. 6). This paper details an action research that utilizes an interactive and playable Contemporary Artwork Data Visualization (CADV) tool informed by Critical Multicultural Art Education and Dewey’s “Art as Experience.” The author suggests that this tool supports undergraduate students’ engagement with contemporary artworks that address social justice. Using varying, diverse artists as points of departure, the CADV tool initiated students’ open dialogues about social justice issues. Furthermore, using the CADV tool as a class built a learning community for students to understand various perspectives concerning social justice issues in the context of different experiences and cultures.

Listening to your mass spectrometer: An open-source toolkit to visualize mass spectrometer data

IntroductionWe have developed a set of tools built with open-source software that includes both a database and a visualization component to collect LC-MS/MS data and monitor quality control parameters. Description of toolTo display LC-MS/MS data we built a parsing tool using Python and standard libraries to parse the XML files after each clinical run. The tool parses the necessary information to store a database comprised of three distinct tables. Another component to this toolkit is an interactive data visualization tool that uses the data from the database. There are 5 different visualizations that present the data based on interchangeable parameters. Evaluation of toolUsing histogram visualization, we assessed how quality control parameters that feed our quality control algorithm, SMACK, which assists to improve the efficiency of data review and results, performed against the collective data. Using the newly identified QC parameter values from the toolkit, we compared the output of the SMACK algorithm; the number of QC flags changed in that there was a 1.7% (31/1944 observations) increase in flags and a 7.1% (138/1944 observations) decrease in presumed false positive flags, increasing the overall performance of SMACK which helped staff focus their time on reviewing more concerning QC failures. DiscussionWe have developed a customizable web-based dashboard for instrument performance monitoring for our opiate confirmation LC-MS/MS assay using data collected with each batch. The web-based platform allows users to monitor instrument performance and can encompass other instruments throughout the laboratory. This information can help the laboratory take proactive measures to maintain instruments, ultimately reducing the amount down time needed for maintenance.

Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz.

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual's health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual’s health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

AMRmap: An Interactive Web Platform for Analysis of Antimicrobial Resistance Surveillance Data in Russia.

Surveillance of antimicrobial resistance (AMR) is crucial for identifying trends in resistance and developing strategies for prevention and treatment of infections. Globally, AMR surveillance systems differ in terms of organizational principles, comprehensiveness, accessibility, and usability of data presentation. Until recently, the data on AMR in Russia were scarcely available, especially to international community, despite the fact that the large prospective multicenter surveillance in Russia was conducted and data were accumulated for over 20 years. We describe the source of data, structure, and functionality of a new-generation web platform, called AMRmap (https://amrmap.net/), for analysis of AMR surveillance data in Russia. The developed platform currently comprises susceptibility data of >40,000 clinical isolates, and the data on abundance of key resistance determinants, including acquired carbapenemases in gram-negatives, are updated annually with information on >5,000 new isolates. The AMRmap allows smart data filtration by multiple parameters and provides interactive data analysis and visualization tools: MIC and S/I/R distribution plots, time-trends and regression plots, associated resistance plots, prevalence maps, statistical significance graphs, and tables.