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The Virtual Estuary: Simulation meets Visualization. Erik Anderson Joel Daniels Juliana Freire Claudio Silva. Yvette Spitz Scott Durski. Antonio Baptista Joseph Zhang. CMOP: Making Sense of Data. Five-component ecosystem model. SELFE – Unstructured grid. Phytoplankton. Nitrate.

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The Virtual Estuary: Simulation meets Visualization

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The virtual estuary simulation meets visualization l.jpg

The Virtual Estuary: Simulation meets Visualization

Erik Anderson

Joel Daniels

Juliana Freire

Claudio Silva

Yvette Spitz

Scott Durski

Antonio Baptista

Joseph Zhang


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CMOP: Making Sense of Data

  • Five-component ecosystem model

SELFE – Unstructured grid

Phytoplankton

Nitrate

Spitz et al. (2005)

Ammonium

Nodes: 27624, Elements: 53369

Vertical levels: 54

50 km

Zooplankton

Detritus

  • Model of Myrionecta Rubra: 7 components

ROMS – Structured grid

  • Other possible model: 12 components

km

Grid pts: 384x128

Vert. levels: 44

km


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CMOP: Making Sense of Data

  • Observation and modeling of multiple systems at multiple scales

    • Scientists need to collaborate: biologists, chemists, oceanographers, computer scientists

  • Very large number of data products, sensor measurements, and results from numerical models

  • Cover more than 10 years of experiments: occupy over 30 TB of storage


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CMOP Cyberinfrastructure

  • Support key activities in the CMOP collaboratory: from data acquisition and visualization to result dissemination

  • VisTrails is a scientific workflow system

    • Integrate simulations, data pre-processing, and analysis

    • Focus on exploratory tasks such as visualization and data mining

    • Transparently tracks provenance of the discovery process---the trail followed as users generate and test hypotheses

    • Supports result dissemination through publishing and teaching

      Open-source: http://www.vistrails.org


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VisTrails: Provenance of Exploration

Reproducibility

and

Validation

Collaboration

Knowledge sharing: data + processes


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Benefits of Provenance

  • Compare data products

    • Assess data quality,

      understand process

  • Support for reflective

    reasoning

  • Improve training and teaching

“Reflective reasoning requires the ability to store temporary results, to make inferences from stored knowledge, and to follow chains of reasoning backward and forward, sometimes backtracking when a promising line of thought proves to be unfruitful. …the process is slow and laborious”

Donald A. Norman

[Freire et al., IPAW 2006]


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Benefits of Provenance

  • Compare data products

  • Support for reflective reasoning

  • Improve training and teaching

  • Knowledge re-use

    • Provide suggestions to users as they construct visualizations

[Koop et al., IEEE Vis 2008]


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Visualization at large and on the go

Rendering of the Columbia River

on an IPOD Touch

High- resolution rendering of the Columbia River

virtual estuary at a display wall


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VisTrails: Science Dissemination

  • Science mashups: simplify data exploration through visualization

    http://www.vistrails.org/vismashup/main.html

[Santos et al., IEEE Vis 2009]


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VisTrails: Science Dissemination

  • Teaching and training

    • Lecture provenance: student can reproduce results

    • Homework provenance: provides insights regarding ask complexity and nature, student confusion

    • VisTrails has been adopted as a teaching tool at Utah, UNC, UTEP, Linkoping (Sweden)


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VisTrails: Science Dissemination

  • Provenance-rich documents and publications

    http://www.vistrails.org/index.php/Viswiki


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The Importance of Visualization: Research

  • A picture is worth a thousand words!

  • Understand large volumes of simulation results---obtain insights

    • Identify interesting features

    • Correlate information from multiple models

    • Interactively explore data

  • Flow visualization


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The Importance of Visualization: Education

  • Intuitive representation, easier for non-experts to understand

  • Visually appealing

  • Teaching and outreach: encourage students and general public to learn about science


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Conclusions and Future Work

  • Visualization is essential for ocean observatories

  • VisTrails and Provenance

    • Support reflective reasoning

    • Intuitive interfaces for simplifying the construction and refinement of visualizations

  • Sharing provenance creates new opportunities

    • Workflow/provenance repositories; provenance-enabled publications

    • Expose scientists to different techniques and tools

    • Scientists can learn by example; expedite their scientific training; and potentially reduce their time to insight

Provenance + Workflows + Sharing have the potential to revolutionize science!


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Acknowledgments

  • Thanks to:

    • VisTrails group

    • CMOP cyber and modeling teams


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Backup


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VisTrails: Parameter Exploration

  • Conveniently explore parameter spaces and compare results

[Freire et al., IPAW 2006]


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Provenance Enabling Tools

[Callahan et al., IPAW 2008]


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VisComplete

[Koop et al., IEEE Vis 2008]


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