Integrating Water Quality Data for LongTerm Research

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Integrating Water Quality Data for Long-Term
Research

• M. Pauline Baker
• Assoc. Professor, Computer Science and
  Informatics
• Director, Visualization and Interactive Spaces Lab

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Challenges

• Data collection tends to be specific to a
  research project, or agency
• Multiple stakeholders
• Archived at different locations, in different
  formats, with or without supporting metadata
• Collected on different spatial scales and at
  different time scales

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Challenges

• Data collection tends to be specific to a
  research project, or agency
• Multiple stakeholders
• Archived at different locations, in different
  formats, with or without supporting metadata
• Collected on different spatial scales and at
  different time scales

Social Issues
Technical Issues
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Science Communities

• 15 years ago astronomy leads in data sharing
• Well-defined data formats and metadata, data
  repositories, large community-based software
  packages, agreed-upon rules for data sharing
• Why? Dependent on shared telescopes, many funded
  by public money

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Science Communities (cont.)

• Current trend
• duplicate that level of sharing
• extend beyond the academic researchers to include
  analysts and policy makers
• Advantages
• Better use of data, better science
• Ability to address larger and more complex
  problems, from multiple points of view

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Science Communities (cont.)

• Example efforts
• NSFs Network for Earthquake Engineering Systems,
  80M program to build a collaboratory

Includes experimental equipment sites, a common
data repository, tools to integrate experimental
and computational work, tools to support
collaboration
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Science Communities (cont.)

• Example efforts
• Oceanography community tools, such as buoy and
  satellite data archives, Live Access Server (a
  software package for sharing data over the Web),
  and various Web-based tools for accessing and
  graphics data, funded mostly by NOAA and NSF

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Science Communities (cont.)

• Example efforts
• NSF, CLEANER
• Collaborative Large-Scale Engineering Analysis
  Network for Environmental Research
• Still in the early stages
• Model is similar to NEES for earthquake
  engineering

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NSF Plans for CLEANER

• A network of environmental field facilities
• Data repository with data analysis, modeling and
  visualization tools
• Support for multidisciplinary research
• Collaboration, among engineers, natural and
  social scientists, educators, industry, policy
  makers, the public.

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CEES Monitoring Network

• A network of environmental field facilities
• Data repository with data analysis, modeling and
  visualization tools
• Support for multidisciplinary research
• Collaboration, among engineers, natural and
  social scientists, educators, industry, policy
  makers, the public.

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CEES Monitoring Network
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Technical Challenges

• Visualization Tools for Data Integration
• For researchers, analysts
• Parallel coordinates
• For information consumers
• Watching Your Watershed
• Both tools will be demonstrated during the tour

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Parallel Coordinates

• Example data from the CEES Eagle Creek Buoy
• 3 sensor packs Surface, Middle, Bottom
• 15 variables per sensor pack, sampled every
  15-minutes

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Parallel Coordinates

• A technique for viewing and interacting with
  high-dimensional data
• Plot each observation record across a set of
  vertical axes, one for each data dimension

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Parallel Coordinates

• Visually integrate over multiple axes

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Parallel Coordinates Interaction

• Select out a range of dates

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Parallel Coordinates Interaction

• Select out range of data values

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Watching Your Watershed

• Museum exhibit at Indiana State Museum, developed
  by VIS and CEES and ISM

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Watching Your Watershed

• Integrates NOAA rainfall data and USGS stream
  gage data, animated over time
• Participants select from a set of available
  significant rain events, and choose to view White
  River or Wabash River system
• Playback combines chart view with spatial iconic
  view

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July 6, 2003
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July 7, 2003
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July 12, 2003
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July 16, 2003
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July 18, 2003
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Summary

• Challenges are both technical and social
• Solutions exist for the technical challenges
  (data modeling, analysis, visualization, archival
  and access, etc.)
• Good examples exist of building science
  communities that utilize shared infrastructure
  and involve all stakeholders

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Thanks!

• For more information, contact
• Polly Baker
• Email baker_at_iu.edu
• Web http//vis.iu.edu
• Questions?