Year: 2022
Type: article
Abstract: Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data.
Source: Information
DOI: 10.3390/info13110547
Authors: Alexandr A. Kalinin, Selvam Palanimalai, Junqi Zhu, Wenyi Wu, Nikhil Devraj, Chunchun Ye, Nellie Ponarul, Syed S. Husain, Ivo D. Dinov
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,466
Funders: National Science Foundation, University of Michigan, National Institutes of Health, University of California, Los Angeles, National Institute of Mental Health, National Cancer Institute, National Institute of Nursing Research, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences
Awards: G1923858223, G2049072053, G2096905578, G2842266435, G2878942984, G3020965894, BD Spokes: SPOKE: MIDWEST: Collaborative: Advanced Computational Neuroscience Network (ACNN), G3696626821, G3776160423, Statistics Online Computational Resource for Education, G39581344, Optimal Decision Making in Radiotherapy Using Panomics Analytics, Computational examination of RDoC threat and reward constructs in a representative, predominantly low-income, longitudinal sample at increased risk for internalizing disorders, Center for Complexity and Self-management of Chronic Disease (CSCD), G5544006853, G5546168233, G5633052571, G593441938, G6144821058, G6246816588, Collaborative Research: Distributome--An Interactive Web-based Resource for Probability Distributions, Collaborative Research: Distributome--An Interactive Web-based Resource for Probability Distributions, Michigan Institute for Clinical and Health Research (MICHR), G7863310615, G7968915297, BD Hubs: Collaborative Proposal: Midwest: Midwest Big Data Hub: Building Communities to Harness the Data Revolution, G8056118400, NCS-FO: Connectome mapping algorithms with application to community services for big data neuroscience, Michigan Nutrition Obesity Research Center, Digital behavioral phenotyping and multi-region electrophysiology to determine behavioral and neural network changes underlying the stress response in mice, Biomedical Informatics and Data Science Training Program (BIDS-TP)
OpenAlex ID: W4309736742