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Background Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. Objective We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. Methods We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher’s query. Results PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. Conclusions The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.

BACKGROUND Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. OBJECTIVE We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. METHODS We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher’s query. RESULTS PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. CONCLUSIONS The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.

TexTonic is a visual analytic system for interactive exploration of very large unstructured text collections. TexTonic visualizes hierarchical clusters of representative terms, snippets, and documents in a single, multi-scale spatial layout. Exploration is supported by interacting with the visualization and directly manipulating the terms in the visualization using semantic interactions. These semantic interactions steer the underlying analytic model by translating user interactions within the visualization to contextual updates to the supporting data model. The combination of semantic interactions and information visualization at multiple levels of the data hierarchy helps users manage information overload so that they can more effectively explore very large text collections. In this article, we describe TexTonic’s data processing and analytic pipeline, user interface and interaction design principles, and results of a user study conducted mid-development with experienced data analysts. We also discuss the implications TexTonic could have on visual exploration and discovery tasks.

A new generation of tools is needed to allow researchers and practicing engineers the ability to leverage investments in new methodologies and software infrastructure while enabling customization to local conditions. MAEviz represents such a next generation of seismic risk assessment software, based on the Mid-America Earthquake (MAE) Center research in Consequence-based Risk Management (CRM) and is designed to be extended, customized, and evolved to meet the needs of specific organizations and regions. MAEviz helps bridge the gap between researchers, practitioners, and policy-makers by integrating the latest research findings and most accurate data, using state-of-the-art methodologies, in an extensible software platform.

MAEviz is a broadly extensible, open source platform for earthquake hazard risk management. MAEviz is a model cyberenvironment that provides practical capabilities for researchers through decision-makers to model earthquake events, develop risk reduction strategies, and implement mitigation plans to minimize the impact of earthquake disasters while also providing a pathway for researchers to quickly add new algorithms and data to assure that decisions are based on state-of-the-art engineering understanding. While MAEviz is capable of interacting with remote data and computational sources, it is also fully capable of running analyses locally so research scientists and decision-makers can generate information when a catastrophic event occurs and provide first-responders result information. This article describes MAEviz's overall layered architecture, its foundation in the widely used Eclipse Rich Client Platform (RCP), and use of open-source middleware and geographic information system (GIS) components. MAE...

Sophisticated data-distribution schemes and recent developments in sensors and instruments that can monitor the lower kilometers of the atmosphere at high levels of resolution have rapidly expanded the quantity of information available to mesoscale meteorology. The myLEAD personalized information-management tool helps geoscience users make sense of this vastly expanded information space. MyLEAD extends the general globus metadata catalog service and leverages a well-known general and extensible schema. Its orientation makes it an active player in large-scale distributed computation environments characterized by interacting grid and Web services.

Faceted browsing is a common technique for exploring collections where the data can be grouped into a number of pre-defined categories, most often generated from textual metadata. Historically, faceted browsing has been applied to a single data type such as text or image data. However, typical collections contain multiple data types, such as information from web pages that contain text, images, and video. Additionally, when browsing a collection of images and video, facets are often created based on the metadata which may be incomplete, inaccurate, or missing altogether instead of the actual visual content contained within those images and video. In this work we address these limitations by presenting MultiFacet, a faceted browsing interface that supports multiple data types. MultiFacet constructs facets for images and video in a collection from the visual content using computer vision techniques. These visual facets can then be browsed in conjunction with text facets within a single interface to reveal relationships and phenomena within multimedia collections. Additionally, we present a use case based on real-world data, demonstrating the utility of this approach towards browsing a large multimedia data collection.

Visualizing large document collections using a spatial layout of terms can enable quick overviews of information. These visual metaphors (e.g., word clouds, tag clouds, etc.) traditionally show a series of terms organized by space-filling algorithms. However, often lacking in these views is the ability to interactively explore the information to gain more detail, and the location and rendering of the terms are often not based on mathematical models that maintain relative distances from other information based on similarity metrics. In this paper, we present Typograph, a multi-scale spatial exploration visualization for large document collections. Based on the term-based visualization methods, Typograh enables multiple levels of detail (terms, phrases, snippets, and full documents) within the single spatialization. Further, the information is placed based on their relative similarity to other information to create the “near = similar” geographic metaphor. This paper discusses the design principles and functionality of Typograph and presents a use case analyzing Wikipedia to demonstrate usage.

MAEviz is an open-source project that helps reduce the time from discovery gap that exists between researchers, practitioners, and decision makers by integrating the latest research findings, most accurate data, and new methodologies into a single software product. It was developed as a platform to perform seismic risk assessment based on the mid-america earthquake (MAE) center research in the consequence-based risk management (CRM) framework. MAEviz is built upon an open source, extensible software platform developed at NCSA using the eclipse rich client platform (RCP). The example shown in the poster is network-based seismic retrofit (NBSR) analysis. The analysis solves a typical problem faced by decision makers that, given a fixed budget, which combination of bridges and retrofit methods would minimize the societal cost of an earthquake. It clearly shows how new science can be put quickly into the hands of the decision makers, thus bridging the gap between research and practical application. MAEviz is shown to be a powerful tool that can currently be used to assist decision makers in preparing for and mitigating the consequences of seismic hazards. Moreover, the extensible architecture of MAEviz allows it to be easily adapted to integrate newly discovered science and data, both in the area of seismic risk assessment, as well as other future research areas.

MAEviz is an open-source project that helps reduce the time from discovery gap that exists between researchers, practitioners, and decision makers by integrating the latest research findings, most accurate data, and new methodologies into a single software product. MAEviz was developed as a platform to perform seismic risk assessment based on the Mid-America Earthquake (MAE) Center research in the consequence-based risk management (CRM) framework. The CRM paradigm provides a philosophical and practical framework for the assessment of the dynamic inter-disciplinary relationship between causes, effects, and effect mitigation for major events and disaster management. MAEviz provides the medium where "what if models and risk reduction strategies can be evaluated by both researchers and decision makers in an effort to avoid a catastrophic impact on society in the event of an earthquake.

MAEviz is a state-of-the-art earthquake risk assessment environment built to quickly bring new research results into production analyses. It is built upon an extensible Open Services Gateway Initiative (OSGi) based GIS application platform and leverages distributed content management, workflow, and virtual-organization based design concepts. MAEviz has been developed as a collaboration between the National Center for Supercomputing Applications and the Mid-America Earthquake (MAE) Center community and it implements the MAE Consequence-based Risk Management (CRM) methodology. The open source MAEviz provide a modern GIS application interface with sophisticated visualization and reporting capabilities that also incorporates mechanisms to integrate distributed data sources, develop reusable analyses and view data provenance, coordinate work in distributed teams. As an Eclipse Rich Client Platform (RCP) application, MAEviz is composed of multiple plugins and extensions that wrap various open source libraries such as Geotools, the Visualization Toolkit (VTK), and Jasper reports as well as middleware components developed at NCSA. This architecture enables MAEviz to rapidly be extended with new scientific analyses and allows reuse of the base GIS environment capabilities. This demonstration paper provides an overview of MAEviz' capabilities and architecture and presents it as an exemplar of a new generation of "cyberenvironments" that enable multi-disciplinary collaboration and foster direct connections between researchers, practitioners and decision makers. MAEviz is designed to bridge the gap between scientific discovery and practical use by integrating the latest research findings, most accurate data and state-of-the-art methodologies into an extensible open source software system.

The challenges of building an effective grid-based problem solving environment that truly extends and embraces a computational scientist’s traditional tools are multifold. It is far too easy to build simple stovepipes that allow fixed use patterns, that don’t extend a scientist’s desktop, and fail to encompass the full range of patterns that a scientist needs to find such a problem-solving environment as a liberating and enabling tool. In the LEAD project, we have focused on the most challenging users of numerical weather prediction, namely, the atmospheric science researchers, who are prone to use their own tools, their own modified versions of community codes such as the Weather Research and Forecasting (WRF) model, and are typically comfortable with elaborate shell scripts to perform the work they find to be necessary to succeed, to drive our development efforts. Our response to these challenges includes a multi-level workflow engine, to handle both the challenges of ensemble description and execution, as well as the detailed patterns of workflow on each computational resource; services to support the peculiarities of each platform being used to do the modeling (such as on TeraGrid), and the use of an RDF triple store and message bus together as the backbone of our notification, logging, and metadata infrastructure. The design of our problem-solving environment elements attempts to come to grips with lack of control of elements surrounding and supporting the environment; we achieve this through multiple mechanisms including using the OSGI plug-in architecture, as well as the use of RDF triples as our finest-grain descriptive element. This combination, we believe, is an important stepping stone to building a cyber environment, which aims to provide flexibility and ease of use far beyond the current range of typical problem solving environments.

Process management is an extremely important concept in both business and scientific communities. Several workflow management tools have been proposed in recent years offering advanced functionality in various domains. In the business world, workflow vendors offer commercial and customized solutions targeting specific users. In the scientific world, several open-source workflow management tools are freely available. However they are directed toward service aggregation rather than distributed process management. Little consideration is given to the needs of the client in terms of mapping the process flow of the client. In the grid community it is essential that the grid users have such a tool available enabling them to orchestrate complex work-flows on the fly without substantial help from the service providers. At the same time it is important that the grid user not be burdened with the intricacies of the workflow system. With the perspective of the grid user in mind, an extensible client-side workflow management system, called GridAnt, has been developed. This paper discusses the design principles, functionality, and application of the proposed GridAnt workflow manager.

This paper presents an architecture to meet the needs for authentication and authorization in Grid based component systems. While Grid Security Infrastructure (GSI) [1] is accepted as the standard for authentication on the Grid, distributed authorization is still an open problem being investigated by various groups [2],[3],[4]. Our design provides authentication and fine-grained authorization at the interface, method and parameter levels. We discuss the ways in which internal and external authorization services can be used in a component framework. The design is flexible to allow the use of various existing policy languages and authorization systems. Our prototype is based on XCAT, an implementation of the Common Component Architecture (CCA) specification.