GEOSCIENCE NEEDS

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GEOSCIENCE NEEDS CHALLENGES

• Dogan Seber
• San Diego Supercomputer Center
• University of California, San Diego, USA

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• Earth science research is moving towards a
  systems approach. To understand the Earth we
  need to look at it as a whole. Scientists have
  expertise in specific areas in their
  sub-disciplines and knowledge about sister
  disciplines is limited.

Can cyberinfrastructure help?
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Some common IT problems in the Geosciences

• Exponential increase in data volumes
• Diversity and complexity of data sets
• Data storage, access and preservation
• Data integration (semantic and syntactic)
• Computational challenges and access to HPC
• Advance visualization (3D/4D)
• Archiving publications with reusable components

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A Scientific Effort Vector

Background Research
Data Collection and Compilation/
Software issues
Science
Back- ground Research
Data Collection and Compilation/ Software
Issues

Science
Science - Analysis, Modeling, Interpretation,
Discovery
Source R. Keller
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Enabling Scientific DiscoveriesPathway to
Discovery
Data
Knowledge
Access
Process
Analyze
Interpret
Discovery
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Large/Complex Data Volumes

• National/International Observatories/projects
• EarthScope
• ES is a US project to collect data across the
  entire US over the next 10 years. Includes
  seismic, GPS and drill hole data
• LiDAR data
• Airborne and ground based data collection (large
  volumes of data sets)
• Global Observations
• A variety of satellites gathering data at
  different resolutions
• Hydrology, Environmental, Natural resource
  development projects, etc.
• Small projects
• Individual researchers maintain a lot of data
  sets, such as geology maps, geochemistry
  databases, earthquake catalogues, etc.
  Collectively
• reusable data reach large volumes and complex
  dimensions

Challenge How to manage these data so that vast
amounts of data can be used by all scientists in
an easy-to-use environment
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Data Storage, Access and Preservation

• Preservation of digital and legacy data sets
• Since research needs and styles of each scientist
  vary, each researcher has his/her own data with
  their own flavors
• Access to other scientists data is limited
• When scientists do not continue to maintain their
  data, it is lost forever!

Challenge How to build a framework to exchange
data and help preserving collected data sets
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Data Integration Issues

• Integration requires both syntactic and semantic
  level integration. e.g., How can a geologist
  merge multiple geology maps to make a seamless
  (integrated) map that overlaps with national
  and international boundaries.

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Integrate Geologic Data From Multiple Sources
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Computational Challenges in Geosciences

• Developing/Accessing community codes
• Parallelizing software for efficient runs
• Accessing small to very large clusters
• Technical expertise to use high-end
  systems/clusters

Challenges How to build a system that helps
scientists run advance software without having
access to significant resources (computers and
technical) How to build a system that helps
scientists to focus on science rather than
technological challenges/problems
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Example Can we build a system that not only a
few privileged, but also the entire community
could use to run 3D seismic modeling?
(Goldstein 2001)
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Geosciences are Visualization Oriented

• Once large volume data sets are accessed, how can
  we visualize them to get a better understanding
  of each data set?
• To build an effective visualization environment
  powerful software and hardware needed.

Challenge How to build a visualization system
that helps scientists analyze large and complex
data sets dynamically.
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Archiving results and publications with reusable
components

• Science progresses incrementally. New knowledge
  is built on top of existing knowledge.
• Scientific validity is shown by repeatability.

Challenges How to preserve scientific results
and help others to repeat the analysis as
efficiently as possible? How to share algorithms
and processing flows with others?
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Efforts underway

• Numerous projects are funded to address these
  questions
• E.g., GEON, SCEC ITR, CUAHSI, EarthChem
• NSF funding opportunities in GEO and CISE
  directorates
• Professional societies getting involved in CI
• GSA Geoinformatics Division
• AGU Earth and Space informatics focus group
• Extensive level of outreach and learning
  activities taking place

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Lessons Learned 1/2

• Building cyberinfrastructure resources is a
  social experimentation
• Equal partnerships between domain and IT is a
  must
• Understand the needs of the domain sciences
• Community outreach is critical (workshops,
  seminars, scientific meetings, etc)
• Get it right the first time!
• Define the goals clearly, and publicize them
• Learn to differentiate a system that works and
  a system that is usable

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Lessons Learned 2/2

• Work with those who are willing and interested
• Identify killer apps, use them to attract more
  interest
• Teach! Help building a community of users and
  resource builders
• Problems are similar. Work with other
  communities, solutions may be out there