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1. VITALSE: visualizing eye tracking and biometric data

   https://doi.org/10.1145/3377812.3382154  

   Roy, Devjeet; Fakhoury, Sarah; Arnaoudova, Venera (June 2020, ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings)

   null (Ed.)

   Recent research in empirical software engineering is applying techniques from neurocognitive science and breaking new grounds in the ways that researchers can model and analyze the cognitive processes of developers as they interact with software artifacts. However, given the novelty of this line of research, only one tool exists to help researchers represent and analyze this kind of multi-modal biometric data. While this tool does help with visualizing temporal eyetracking and physiological data, it does not allow for the mapping of physiological data to source code elements, instead projecting information over images of code. One drawback of this is that researchers are still unable to meaningfully combine and map physiological and eye tracking data to source code artifacts. The use of images also bars the support of long or multiple code files, which prevents researchers from analyzing data from experiments conducted in realistic settings. To address these drawbacks, we propose VITALSE, a tool for the interactive visualization of combined multi-modal biometric data for software engineering tasks. VITALSE provides interactive and customizable temporal heatmaps created with synchronized eyetracking and biometric data. The tool supports analysis on multiple files, user defined annotations for points of interest over source code elements, and high level customizable metric summaries for the provided dataset. VITALSE, a video demonstration, and sample data to demonstrate its capabilities can be found at http://www.vitalse.app. 

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2. Real-Time Data Analytics of COVID Pandemic Using Virtual Reality

   https://doi.org/10.1007/978-3-030-77599-5_9  

   Sharma, S; Bodempudi, S.T; Reehl, A (July 2021, Lecture notes in computer science)

   Chen, J.Y.C.; Fragomeni, G (Ed.)

   Visualizing data effectively is critical for the discovery process in the age of big data. We are exploring the use of immersive virtual reality platforms for scientific data visualization for COVID-19 pandemic. We are interested in finding ways to better understand, perceive and interact with multidimensional data in the field of cognition technology and human-computer interaction. Immersive visualization leads to a better understanding and perception of relationships in the data. This paper presents a data visualization tool for immersive data visualizations based on the Unity development platform. The data visualization tool is capable of visualizing the real-time COVID pandemic data for the fifty states in the USA. Immersion provides a better understanding of the data than traditional desktop visualization tools and leads to more human-centric situational awareness insights. This research effort aims to identify how graphical objects like charts and bar graphs depicted in Virtual Reality tools, developed in accordance with an analyst’s mental model can enhance an analyst’s situation awareness. Our results also suggest that users feel more satisfied when using immersive virtual reality data visualization tools and thus demonstrate the potential of immersive data analytics. 

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3. freeCount: A Coding Free Framework for Guided Count Data Visualization and Analysis

   https://doi.org/10.1145/3626203.3670605  

   Brooks, Elizabeth Mae; Sanders, Sheri A; Pfrender, Michael E (July 2024, ACM)

   The analysis and interpretation of high-dimensional biological data sets is a challenging task. Exploratory data analysis of count data produced by next-generation sequencing technologies presents a common hurdle to researchers. Biologists often find it difficult to get started with the analysis process, which can be time consuming and researchers are guided through the iterative steps of data assessment, processing, and analysis in a visual environment. The freeCount analysis framework takes advantage of the reactive features of R Shiny to deliver a set of modular and interactive tools and tutorials for the structured analysis and visualization of count data. 

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4. Human-Centric Situational Awareness and Big Data Visualization

   Bodempudi, Sri Teja; Sharma, Sharad; Sahu, Atma; Agrawal, Rajeev (October 2019, EPiC series in computing)

   Human-centric situational awareness and visualization are needed for analyzing the big data in an efficient way. One of the challenges is to create an algorithm to analyze the given data without any help of other data analyzing tools. This research effort aims to identify how graphical objects (such as data-shapes) developed in accordance with an analyst's mental model can enhance analyst's situation awareness. Our approach for improved big data visualization is two-fold, focusing on both visualization and interaction. This paper presents the developed data and graph technique based on forcedirected model graph in 3D. It is developed using Unity 3D gaming engine. Pilot testing was done with different data sets for checking the efficiency of the system in immersive environment and non-immersive environment. The application is able to handle the data successfully for the given data sets in data visualization. The currently graph can render around 200 to 300 linked nodes in real-time. 

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5. Interactive Data Visualization for Decision-making in Government-funded Advanced Research Computing Service

   https://doi.org/10.59490/dgo.2025.946  

   Cheng, Xiaoyue; Chen, Yu-Che; Knepper, Rich; Burk, Andrew (May 2025, Conference on Digital Government Research)

   Data-driven decision-making has become more prevalent in the field of digital government. Data visualization as a key tool for data exploration, can help decision makers quickly understand data and grasp insights behind pages of spreadsheets. To better support scientific discovery through advanced research computing, the U.S. National Science Foundation has funded the cyberinfrastructure programs including TeraGrid, XSEDE, and ACCESS for over twenty years. These programs have generated extensive data about the awarded projects along with their resource allocation and usage. However, existing visualization tools for these data were not designed for in-depth analysis or direct decision-making support. This research investigated the needs of resource providers and users, analyzed the data collected from TeraGrid and XSEDE projects between 2003 and 2022, and developed a publicly accessible interactive visualization dashboard. The goal of this visual platform is to enable resource providers and users to independently explore data through graphs and tables and make informed comparisons and decisions. Resource providers can use the platform to study and compare trends in resource allocation and usage across projects funded at various institutions and within different fields, identify more efficient resource users and research directions, and provide personalized services to institutions and principal investigators in need. Resource users can search for potential collaborators and helpers from successful cyberinfrastructure project awardees in similar fields, nearby locations, or related research topics. Additionally, this paper demonstrates the practical application of the visualization dashboard through three examples: a research-intensive institution, an under-represented minority-serving institution, and a junior researcher from a specific field of science, showcasing how each can leverage the platform for decision-making. 

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