323,692 research outputs found

    SEAD Virtual Archive: Thin Layer for Scientific Discovery and Long-Term Preservation

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    Major research universities are grappling with their response to the deluge of scientific data in its big data and long tail data forms. The latter consist of many diverse and heterogeneous sets, the data are collected via diverse and specialized methods, and are stored in a variety of formats and places. University libraries and their institutional repositories have traditionally been able to handle scientific output. But long-tail scientific data introduce substantial challenges to a traditional document-based repository through its vast heterogeneity, size, and its demands for meaningful discovery and in the case of large data sets, place-based use. In this presentation we will provide a brief overview of the NSF-funded project "Sustainable Environment - Actionable Data" (SEAD), which addresses the challenges of long-tail scientific data with the focus on sustainability science. We will provide an overview of this project and of its discovery and preservation component, called SEAD Virtual Archive. This component is being developed by the Data to Insight Center team at Indiana University in collaboration with IU and UIUC libraries. We will describe main features and our ongoing work on SEAD Virtual Archive and discuss the value and importance of partnerships between data research centers, such as D2I, and the libraries

    Snap2Diverse: Coordinating Information Visualizations and Virtual Environments

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    The field of Information Visualization is concerned with improving with how users perceive, understand, and interact with visual representations of data sets. Immersive Virtual Environments (VEs) excel at providing researchers and designers a greater comprehension of the spatial features and relations of their data, models, and scenes. This project addresses the intersection of these two fields where information is visualized in a virtual environment. Specifically we are interested in visualizing abstract information in relation to spatial information in the context of a virtual environment. We describe a set of design issues for this type of integrated visualization and demonstrate a coordinated, multiple-views system supporting 2D and 3D visualization tasks such as overview, navigation, details-on-demand, and brushing-and-linking selection. Software architecture issues are discussed with details of our implementation applied to the domain of chemical information and visualization. Lastly, we subject our system to an informal usability evaluation and identify usability issues with interaction and navigation that may guide future work in these situations

    Implementación de una lente mágica en un entorno virtual tridimensional con una tablet

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    When dealing with large data sets in Virtual Environments it is important to be able to manage the viewing of the data to obtain different levels of detail and to explore it from different perspectives. For this the appropriate interaction techniques have to be developed. This work consists on the investigation of different approaches to implement a flat Magic Lens application which interacts in a 3D Virtual Environment. The Magic Lens represents an interaction metaphor for the 2D selection and manipulation of 3D graphical information. The user by changing the position and the rotation of a hand held device, controls the Magic Lens motion in the Virtual Environment. Therefore two alternative views of the scene are offered, one where the user can appreciate an overview of the data set and the other which shows the focus view. Once some region of the VE is selected by the Magic Lens it is necessary to compute the view frustum, so that we can obtain a snapshot of the desired frustum in the hand held device and manipulate it there. This thesis focuses on the different interaction techniques which can be used in order to explore the virtual world with the Magic Lens.Ingeniería de Telecomunicació

    VAMDC as a Resource for Atomic and Molecular Data and the New Release of VALD

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    The Virtual Atomic and Molecular Data Centre (VAMDC) (M.L. Dubernet et al. 2010, JQSRT 111, 2151) is an EU-FP7 e-infrastructure project devoted to building a common electronic infrastructure for the exchange and distribution of atomic and molecular data. It involves two dozen teams from six EU member states (Austria, France, Germany, Italy, Sweden, United Kingdom) as well as Russia, Serbia, and Venezuela. Within VAMDC scientists from many different disciplines in atomic and molecular physics collaborate with users of their data and also with scientists and engineers from the information and communication technology community. In this presentation an overview of the current status of VAMDC and its capabilities will be provided. In the second part of the presentation I will focus on one of the databases which have become part of the VAMDC platform, the Vienna Atomic Line Data Base (VALD). VALD has developed into a well-known resource of atomic data for spectroscopy particularly in astrophysics. A new release, VALD-3, will provide numerous improvements over its predecessor. This particularly relates to the data contents where new sets of atomic data for both precision spectroscopy (i.e., with data for observed energy levels) as well as opacity calculations (i.e., with data involving predicted energy levels) have been included. Data for selected diatomic molecules have been added and a new system for data distribution and data referencing provides for more convenience in using the upcoming third release of VALD.Comment: 8 pages, 1 tabl

    DocuDrama

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    This paper presents an approach combining concepts of virtual storytelling with cooperative processes. We will describe why storytelling is relevant in cooperation support applications. We will outline how storytelling concepts provide a new quality for groupware applications. Different prototypes illustrate a combination of a groupware application with various storytelling components in a Theatre of Work

    ViBe (Virtual Berlin) - Immersive Interactive 3D Urban Data Visualization - Immersive interactive 3D urban data visualization

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    The project investigates the possibility of visualizing open source data in a 3D interactive virtual environment. We propose a new tool, 'ViBe'. We programmed 'ViBe' using Unity for its compatibility with HTC VIVE glasses for virtual reality (VR). ViBe offers an abstract visualization of open source data in a 3D interactive environment. The ViBe environment entails three main topics a) inhabitants, b) environmental factors, and c) land-use; acting as representatives of parameters for cities and urban design. Berlin serves as a case study. The data sets used are divided according to Berlin's twelve administrative districts. The user immerses into the virtual environment where they can choose, using the HTC Vive controllers, which district (or Berlin as a whole) they want information for and which topics they want to be visualized, and they can also teleport back and forth between the different districts. The goal of this project is to represent different urban parameters an abstract simulation where we correlate the corresponding data sets. By experiencing the city through visualized data, ViBe aims to provide the user with a clearer perspective onto the city and the relationship between its urban parameters. ViBe is designed for adults and kids, urban planners, politicians and real estate developers alike

    Symbolic Activities in Virtual Spaces

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    This paper presents an approach to combine concepts ofsymbolic acting and virtual storytelling with the support ofcooperative processes. We will motivate why symboliclanguages are relevant in the social context of awarenessapplications. We will describe different symbolicpresentations and illustrate their application in three differentprototypes

    Schema architecture and their relationships to transaction processing in distributed database systems

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    We discuss the different types of schema architectures which could be supported by distributed database systems, making a clear distinction between logical, physical, and federated distribution. We elaborate on the additional mapping information required in architecture based on logical distribution in order to support retrieval as well as update operations. We illustrate the problems in schema integration and data integration in multidatabase systems and discuss their impact on query processing. Finally, we discuss different issues relevant to the cooperation (or noncooperation) of local database systems in a heterogeneous multidatabase system and their relationship to the schema architecture and transaction processing

    SEED: efficient clustering of next-generation sequences.

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    MotivationSimilarity clustering of next-generation sequences (NGS) is an important computational problem to study the population sizes of DNA/RNA molecules and to reduce the redundancies in NGS data. Currently, most sequence clustering algorithms are limited by their speed and scalability, and thus cannot handle data with tens of millions of reads.ResultsHere, we introduce SEED-an efficient algorithm for clustering very large NGS sets. It joins sequences into clusters that can differ by up to three mismatches and three overhanging residues from their virtual center. It is based on a modified spaced seed method, called block spaced seeds. Its clustering component operates on the hash tables by first identifying virtual center sequences and then finding all their neighboring sequences that meet the similarity parameters. SEED can cluster 100 million short read sequences in <4 h with a linear time and memory performance. When using SEED as a preprocessing tool on genome/transcriptome assembly data, it was able to reduce the time and memory requirements of the Velvet/Oasis assembler for the datasets used in this study by 60-85% and 21-41%, respectively. In addition, the assemblies contained longer contigs than non-preprocessed data as indicated by 12-27% larger N50 values. Compared with other clustering tools, SEED showed the best performance in generating clusters of NGS data similar to true cluster results with a 2- to 10-fold better time performance. While most of SEED's utilities fall into the preprocessing area of NGS data, our tests also demonstrate its efficiency as stand-alone tool for discovering clusters of small RNA sequences in NGS data from unsequenced organisms.AvailabilityThe SEED software can be downloaded for free from this site: http://manuals.bioinformatics.ucr.edu/home/[email protected] informationSupplementary data are available at Bioinformatics online
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