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InterMine

InterMine is an open source data warehouse built specifically for the integration and analysis of complex biological data. Developed by the Micklem lab at the University of Cambridge, InterMine enables the creation of biological databases accessed by sophisticated web query tools. Parsers are provided for integrating data from many common biological data sources and formats, and there is a framework for adding your own data. InterMine includes an attractive, user-friendly web interface that works 'out of the box' and can be easily customised for your specific needs, as well as a powerful, scriptable web-service API to allow programmatic access to your data.

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Institution: Micklem Lab, University of Cambridge

ParaView

ParaView is an application framework and turnkey application. Users can quickly build visualizations to analyze their data using qualitative and quantitative techniques, with data exploration done interactively in 3D or programmatically using ParaView's batch processing capabilities. Furthermore, the ParaView code base is designed in such a way that all of its components can be reused to quickly develop vertical applications. This flexibility allows ParaView developers to quickly develop applications that have specific functionality for a specific problem domain.

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Institution: Kitware

UCSF Chimera

UCSF Chimera is a highly extensible program for interactive visualization and analysis of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking results, trajectories, and conformational ensembles. High-quality images and animations can be generated. Chimera includes complete documentation and several tutorials, and can be downloaded free of charge for academic, government, non-profit, and personal use.

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Institution: University of California, San Francisco

Chimera is developed by the Resource for Biocomputing, Visualization, and Informatics, funded by the National Institutes of Health (NIGMS P41-GM103311).

Voreen

Voreen is a ray-casting technique for rendering large volumetric multi-channel microscopy data streams on commodity hardware.

The volumetric data is managed at different levels of detail by an octree structure. In contrast to previous octree-based techniques, the octree is built incrementally and therefore supports streamed microscopy data as well as data set sizes exceeding the available main memory.Furthermore, this approach allows the user to interact with the partially rendered data set at all stages of the octree construction.

This rendering technique allows biologists to visualize their scanned specimen on their standard desktop computers without high-end hardware requirements.

The user can interact with the data set during the initial loading to explore the already loaded parts, change rendering parameters like color maps or adjust clipping planes. Streamed data can also be visualized to detect and stop flawed scans early during the scan process.

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Institution: University of Munster

Quick2Insight

Provides automatic visualization of features inside biological image volumes in 3D. The software provides a simple and interactive visualization for the exploration of biological datasets through dataset-specific transfer functions and direct volume rendering.

The method employs a K-Means++ clustering algorithm to classify a two-dimensional histogram created from the input volume. The classification process utilizes spatial and data properties from the volume. Then using properties derived from the classified clusters the software automatically generates color and opacity transfer functions and presents the user with a high quality initial rendering of the volume data. User input can be incorporated through the simple yet intuitive interface for transfer function manipulation included in our framework. Our new interface helps users focus on feature space exploration instead of the usual effort intensive, low-level widget manipulation.

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Institution: SAIC-Frederick Inc, KnowledgeVis LLC

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

Vesper

VESpeR is a suite of web-based visualisation components that enable biologists to investigate the taxonomic, temporal and geographic coverage of DWCA species-referenced data that can be placed directly into the workflow of biologists who use such data. VESpeR allows biologists to perform tasks such as sanity checking of data, view patterns in geographical, taxonomic or temporal aspects in an interrelated context, and accurately view data even when it spans conflicting taxonomic classifications. This can make a significant contribution to the efficiency and usability of online catalogues for both the providers and end-users of the data they hold. Co-ordinated components The components are co-ordinated such that selections and actions in one component will be reflected in the data shown in other components. VESpeR multiple views VESpeR includes

  • a novel cross-taxonomy viewer that allows users to crosswalk different classifications, allowing them to accurately match specimens between data from different sources
  • interactive map to investigate specimen geographic coverage
  • interactive timeline to investigate specimen temporal coverage
  • sanity checker to view data completeness and vocabulary size

Bio-visualisation Visualisation techniques have been recognised as one of the major directions in future research when handling and querying biological data, offering the ability to find patterns and outliers in data which traditional query interfaces cannot match. A case in point is the multitude of species-referenced databases covering data from genomic to biodiversity data linked by taxonomic classifications that hold geographic and temporal-faceted data alongside other data. Many online databases hold collections of such data, often in archive format, but visual querying tools are invariably limited to a map interface of spatial distribution, neglecting the fact that biologists may wish to query or explore other facets of the data such as the classification or temporal distribution. Add onto this the problem of many complementary databases using different taxonomic classifications to reference their specimens and we have a situation where much of the potential utility of this data remains unused. VESpeR is designed to help address this.

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Institution: IIDI

VESpeR is a project at the Institute for Informatics and Digital Innovation. The Institute is part of Edinburgh Napier University based in Scotland, United Kingdom. At the Institute for Informatics & Digital Innovation we are dedicated to helping you and your organisation deal with new digital challenges and opportunities as they arise. We can help you to look ahead and shape our digital future. We work across every field of computer technology from sensors and mobile networks to data intensive applications requiring intelligent processing and filtering. We can help you explore not just the technology but how people interact with the technology and how it impacts on society. www.iidi.napier.ac.uk Tel: +44 (0)131 455 2651 Email: iidi@napier.ac.uk