Earth System Science: Understanding

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Earth System ScienceUnderstanding Protecting
Our Home Planet

• Ghassem R. Asrar, Ph.D
• Associate Administrator for Earth Science
• January 5, 2004

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IT Tech infusion into which of 6 ESE science
foci? Some sciences? Infrastructure for
all? Enabling tech?
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Extending Science Technology to Society
Interactive Dissemination
Calibration, Transformation To Characterized
Geophysical Parameters
Interaction Between Modeling/Forecasting and
Observation Systems
Petabytes 1015
Predictions
Terabytes 1012
Multi-platform, multi-parameter, high spatial and
temporal resolution, remote in-situ sensing
Gigabytes 109
Megabytes 106
Advanced Sensors
Data Processing Analysis
Access to Knowledge
Information Synthesis
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Integrating Global Measurements
TOPEX/Poseidon
SORCE
SeaWiFS
ERBS
Jason
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Applications of National Priority
Carbon Management
Aviation Safety
Energy Forecasting
Public Health
Water Management
Disaster Management
Coastal Management
Homeland Security
Agricultural Efficiency
Ecological Forecasting
Air Quality
Invasive Species
From Tech Infusion point of view, are
applications equal? The Reason CAN projects only
cover a subset of apps Obviously, Air Quality is
by far the most important one ?
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Integrated System Solutions
Science Models
- Oceans - Ice - Land - Coupled - Atmosphere
Value benefits to citizens and society
Predictions
High Performance Computing, Communication,
Visualization
Policy Decisions Management Decisions
Data
Standards Interoperability
Observations
Inputs Outputs Outcomes Impacts
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Inspire the Next Generation of Earth Explorers
Where are the key infusion spots in the
education??
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Technology

• Information Synthesis Distributed,
  Reconfigurable, Autonomous
• Access to Knowledge On-orbit Processing,
  Immersive Environments

Challenges to Enable Future Science -
Laser/Lidar technology to enable Earth system
science measurements - Large Deployables to
enable future weather/climate/ natural hazards
measurements - Intelligent Distributed
Systems using optical communication, on-board
reprogrammable processors, autonomous network
control, data compression, high density
storage - Information Knowledge Capture
through 3-D Visualization, holographic memory and
seamlessly linked models.
This is us? Is this the territory that SEEDS can
make big mark?
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Data System Architecture
- EOSDIS
Flight Operations,
Science Data
Distribution,
Processing,
Data Capture,
Access,
Data
Info Mgmt, Data
Initial Processing,
Transport
Interoperability,
Backup Archive
Data Acquisition
to DAACs
Archive, Distribution
Reuse
Research
Users
Tracking
Spacecraft
Data
Relay Satellite
DAACs
(TDRS)
NASA
Integrated
Data Processing Mission Control
Services
Network (NISN) Mission Services
Education
Users
Ground Stations
WWW
Value-Added
Providers
Science Teams
Intl Partners
Interagency
Polar Ground Stations
Data
Centers
Data
And this is how we can actually do it? With
arrows (and bow ?)?
Centers
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Data System Architecture
- EOSDIS
with ESIPs REASoNs
Data Flow Through the Value Chain
Science Data Processing Info Management,
Archive, Distribution, Access, Reuse
Research Users
ESIPs
Education Users
Data Acquisition
DAACs
Value-added Providers
REASoNs
International Data Centers
Interagency Data Centers
SEEDS?
Slide 17 Slightly Simplified OK, DAACs are are
the primary sources of raw ESE data So, is SEEDS
the facilitator between the DAACs and the
immediate user layer?
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Turning Observations Into Knowledge Products
Were are we (REASoN) along the data-to-knowledge
dimension?? Seeds does not deal with advanced
sensors, right? Is infusion most vital in
processing/analysis or in synthesis? (I think
synthesis is most in need of software and
intellectual technologies
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Evolving EOSDIS Elements

• Evolve data systems to achieve stability with
  innovation.
• Current Data System Context
• EOSDIS operation volumes include
• 2,178 unique data products
• 4.5TB of daily ingest
• 2TB of daily distribution
• Over 2 million distinct users for 2003
• Approach to system evolution
• Work with the ESE advisory committee (ESSAAC) to
  develop a plan for the way forward (plan expected
  within a year).
• Identify which current systems and functions need
  to evolve, e.g., bandwidth and storage capacity
• Work with the community (e.g. REASoN) to
  implement changes

What is the EODIS and SEEDS (NEWDIS)
Relationship? Tight co-evolution?
REASoN-to-EODIS tech transfusion? Use EOSDIS as
a data server and build the SEEDS technologies on
top of EODIS?
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Drivers of Evolving Data Info Systems

• Missions to Measurements
• ESE is moving from mission-based data systems to
  those that focus on Earth science measurements.
• ESEs DIS will be a resource for science-focused
  communities enabling research, and will be
  flexible, scalable and suited for the particular
  community needs.
• Continue on the pathways for acquiring
  observations to understand processes and develop
  Earth system models.
• The Advance of Information Technologies
• NASA will remain at the forefront of IT
  development and will partner with other agencies
  to ensure the strategic use of IT resources to
  avoid obsolescence and enable enhanced
  performance.
• The lowering cost of IT infrastructure enables
  ESE data systems to take advantage of improving
  computation, storage and network capabilities.
• Facilitate the Transition from Research to
  Operations
• Work with Federal partners to transition
  operational elements of data systems to other
  agencies while maintaining core data system
  functions necessary for conducting NASA ESE
  mission and goals.

Refocus from Missions to Measurements ??? Why
not from missions to science questions?
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Federation Contributions to the Evolution of
EOSDIS

• The Federation has contributed to existing DIS
  capabilities through prototyping, partnering and
  implementation activities.
• Access and Interoperability
• OpenDAP (A data protocol that has allowed the
  science community to be active participants in a
  distributed data infrastructure - interconnecting
  DAACs, ESIPs and others)
• ESML (The Earth Science Markup Language provides
  a means for describing disparate data types to
  enhance search and service capabilities.)
• Data Analysis and Processing Tools
• GIS-friendly formats (ESIPs offering data
  converted into GIS formats enabling rapid use of
  ESE data.)
• Search, Discover and Order (Several new data
  portals where user communities can easily obtain
  the particular data needed - this has been very
  successful in the land research communities.)
• Prototypes for Exploring Emerging Capabilities
  (Subsetting, reprojection, and aggregation data
  mining and discovery tools).

Federation Technologies to be infused through
SEEDS? OpenDAP, ESML, GIS-Friendly, DataPortals,
Prototypes
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ESE Strategy 2003
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ESE Technology Strategy

• The ESE technology program adopts an end-to-end
  approach to facilitate technology infusion.

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On the Context for IT Infusion Processes

• Driving Forces for IT Transfer
• Environmental Settings (Landscape Dimensions,
  Views Perspectives)
• Nodes and Directionality (IT Providers,
  Transformers, Users)
• Connectivity (
• Contents of Transfer (Tools, Methods,
  Infrastructure)
• Notation

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Strategic Evolution of ESE Data Systems - SEEDS
and the ESIP Federation

• Briefing to the ESIP Federation
• July 29, 2003
• Karen L. MoeKaren.L.Moe_at_nasa.gov
• SEEDS Study TeamCatherine Corlan, Kathy
  Fontaine, Vanessa Griffin, Gail McConaughy, Ken
  McDonald, Karen Moe,H. Ramapriyan, Richard
  Ullman, Stephen Wharton
• SEEDS Web Page http//eos.nasa.gov/seeds 

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Information System Challenges

• Development exploitation of heterogeneous
  information systems
• Enable flexibility within data systems to adapt
  to new data stream(s) or to changes in current
  processing streams
• Create measurement oriented data systems within
  the SEEDS interoperable framework that will help
  guide the flow of information and services and
  improve performance and access.
• SEEDS as fabric a mesh bridging Earth science
  data sets to the information web
• What how does EOSDIS evolve into next
  generation distributed architecture
• Identify and create interfaces that facilitate
  the flow of data to modeling efforts (e.g. carbon
  assimilation) - one size does not fit all.
  Enable seamless hooks into data mining and high
  performance computing environments.
• Leverage internet, plug play

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The Strategic Evolution of ESE Data Systems -
SEEDS

• SEEDS Mission To establish an evolution
  strategy and coordinating activities to assure
  the continued effectiveness of ESE data
  management systems and services.
• SEEDS Objectives
• Ensure timely delivery of Earth Science
  information at an affordable cost.
• Maximize availability and utility of ESE
  products.
• Engage the community on data management issues,
  objectives, and solutions.
• Enable the development of flexible systems to
  readily accommodate evolving products and
  services.
• The recent Research, Application, and Education
  Solutions Network (REASoN) cooperative agreement
  is the first implementation of the SEEDS
  framework.

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SEEDS Overview

• Support ESE Data SystemsEvolution Planning
• Address key ESE data systems goals
• Coordinate data system evolutionwith
  implementing projects
• Lead coordination/planning withnational
  international partners
• Support Transition toMeasurement-Focused
  Paradigm
• Sustain and Apply Cost Estimation Tool

SEEDS Mission Establish evolution strategy and
coordinating activities to assure continued
effectiveness of ESE data management systems
services.

• Sustain Community Involvement
• Conduct Community Workshops
• Support Four Working Groups
• Brief Science Committees Organizations

SEEDS Objectives 1. Ensure timely delivery of
Earth Science information at an affordable
cost. 2. Maximize availability and utility of ESE
products. 3. Engage community on data management
issues, objectives, and solutions. 4. Enable the
development of flexible systems to readily
accommodate evolving products services.
SEEDSCoordinatingResponsibilities

• Foster Technology Evolution
• Provide Cost Estimation Tool
• Carry Out IT Prototyping Infusion
• Carry Out Software Reuse Initiatives

• Sustain Apply Unifying Frameworkof Core
  Standards Guidelines
• Format Interface Standards Processes
• Levels of Service Guidelines
• Data Lifecycle Planning Procedures
• Metrics Planning Reporting Guidelines

• Support HQ Initiatives toFund Distributed
  Providersof Products Services
• Utilize SEEDS Paradigm
• Address Thematic Science Questions for Research,
  Education and Applications
• Support REASoN CAN Management

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SEEDS Status

• Completed Study Recommendations
• Discussed draft recommendations at March 2003
  workshop in Annapolis.
• We have community buy-in for the recommendations.
• Presented overview of recommendations to AA in
  March.
• Received action from AA to develop plan for
  evolution of ESE data systems.
• Working with ESDIS Project Manager to address
  this action (see slide next page).
• Incorporated feedback and published final
  recommendations July 3, 2003.
• Supported REASoN CAN
• Contributed guidelines and selection criteria.
• Supported evaluation, selection, awards process
  and milestone negotiation.
• SEEDS planning activities to continue in FY2004
• Determination of the appropriate scope,
  responsibilities, and resourcesfor SEEDS will be
  made as part of the ESE data systems planning
  action.
• Support working group participation by REASoNCAN
  awardees - standards, metrics, technology, reuse.
• Develop options and recommend approachand budget
  for SEEDS coordinating functions.

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ESE Data Systems Planning for the Future

• Work in partnership with ESDIS to apply SEEDS
  principlesand guidelines to plan the evolution
  of ESE data systems
• SEEDS - focused on setting context for evolution
  of current data systems
• ESDIS - focused on development, management and
  operation of Enterprise science data systems
• Address ESE key data systems goals
• Increase resources for higher level product
  generation - more science value
• Increase community participation
• Move to measurement based systems and away from
  mission-based systems
• Increase utilization of smaller, distributed
  systems and reduce reliance on large,
  centralized systems
• Enable the development of Climate Data Records
• Closer collaboration between scientists and those
  planning and developing data systems
• Define evolutionary path and project plan that
• Meets ESE science and budgetary goals
• Addresses data systems goals
• Takes advantage of advances in technology
• Utilizes existing assets to maximum ability
• Is flexible enough to allow continued evolution

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SEEDS Working Groups

• SEEDS working groups specified in the REASoN CAN
  are not solely populated by REASoN winners, but
  rather will be augmented by the REASoN winners.
• ESIP Federation members who have participated on
  the study teams are more than welcome to
  continue. 
• The SEEDS management team is working with the
  REASoN study managers to refine the work plan of
  working groups.
• REASoN negotiations are still in progress.
• SEEDS is trying to figure out how best to
  accommodate the REASoN winners first or second
  choice of working group(s). It is necessary to
  balance the work load.
• We anticipate starting this Fall, 2003.
• SEEDS working groups and Federation standing
  committees have similarities and differences in
  their mission, scope, approach.
• SEEDS and the Federation will spend more time
  determining where the greatest leverage lies and
  work in collaboration.

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ESE Data Systems Planning for the Future

• Work in partnership with ESDIS to apply SEEDS
  principlesand guidelines to plan the evolution
  of ESE data systems
• SEEDS - focused on setting context for evolution
  of current data systems
• ESDIS - focused on development, management and
  operation of Enterprise science data systems
• Address ESE key data systems goals
• Increase resources for higher level product
  generation - more science value
• Increase community participation
• Move to measurement based systems and away from
  mission-based systems
• Increase utilization of smaller, distributed
  systems and reduce reliance on large,
  centralized systems
• Enable the development of Climate Data Records
• Closer collaboration between scientists and those
  planning and developing data systems
• Define evolutionary path and project plan that
• Meets ESE science and budgetary goals
• Addresses data systems goals
• Takes advantage of advances in technology
• Utilizes existing assets to maximum ability
• Is flexible enough to allow continued evolution

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Community Engagement
SEEDS Workshop Recommendation It should be the
highest priority for the current Formulation
Team of the SEEDS project to develop and
implement organizational structures facilitating
much deeper engagement of key stakeholders. This
action itself must involve some of these
stakeholders and should start immediately. The
success of SEEDS will strongly depend on the
degree to which we engage all the communities
supplying, analyzing, adding value and using
NASAs ESE products

• John Townshend
• University of Maryland

Benefits of CE
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Levels of participation
Community Engagement
Community Involvement
Low
High
Deep Involvement
Participation
Awareness
Ownership
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Community Engagement

• Community engagement is a process, not a program.
  It is the participation of members of a community
  in assessing, planning, implementing, and
  evaluating solutions to problems that affect
  them.
• As such, community engagement involves
  interpersonal trust, communication, and
  collaboration.
• Such engagement, or participation, should focus
  on, and result from, the needs, expectations, and
  desires of a community's members.

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Principles of Community Engagement(derived from
with some additions from www.cdc.gov/phppo/)

• 1.Be clear about the purposes or goals of the
  engagement effort, and the populations and/or
  communities you want to engage. The implementers
  of the engagement process need to be able to
  communicate to the community why participation is
  worthwhile.
• 2.Become knowledgeable about the community in
  terms of its economic conditions, political
  structures, norms and values, demographic trends,
  history, and experience with engagement efforts.
  Learn about the community's perceptions of those
  initiating the engagement activities. It is
  important to learn as much about the community as
  possible, through both qualitative and
  quantitative methods from as many sources as
  feasible.
• 3.Go into the community, establish relationships,
  build trust, work with the formal and informal
  leadership, and seek commitment from community
  organizations and leaders to create processes for
  mobilizing the community. Engagement is based on
  community support for whatever the process is
  trying to achieve.
• 4. Remember and accept that community
  self-determination is the responsibility and
  right of all people who comprise a community. No
  external entity should assume it can bestow on a
  community the power to act in its own
  self-interest.
• 5. Partnering with the community is necessary to
  create change and improve information systems..
• 6. All aspects of community engagement must
  recognize and respect community diversity.
  Awareness of the various cultures of a community
  and other factors of diversity must be paramount
  in designing and implementing community
  engagement approaches.
• 7. Community engagement can only be sustained by
  identifying and mobilizing community assets, and
  by developing capacities and resources for
  community decisions and action.
• 8. An engaging organization or individual change
  agent must be prepared to release control of
  actions or interventions to the community, and be
  flexible enough to meet the changing needs of the
  community.
• 9. Community collaboration requires long-term
  commitment by the engaging organization and its
  partners.

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NewDISS Petri Dish with Generic Federation
Mapping
ESIP-2 or Pathfinder PI
ESIP-1
ESIP-1, ESIP-2, SIPS or SCF
ESIP-1 or ESIP-2
ESIP-1 with LTA in-place
LTA
ESIP-2, ESIP-3, RESAC or RAC
Backbone Data Centers
Science Data Centers
Long Term Archive
Multi-Mission Data Centers
Application Centers
Mission Data Centers
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Mapping Federation Components Into NewDISS

• Goodman showed the now familiar "petri dish"
  diagram, which is a theoretical mapping of how
  NewDISS components might fit together. 
• Martha Maiden likes this idea.  Certainly, the
  Science Plan is now the primary driving force
  behind all that goes on in the Earth Science
  Enterprise.  The secondary objective is to make
  data available for all.  She suggests a thematic
  look at the science questions addressed by
  Federation activities would certainly be useful. 
  
• Victor Zlotnicki returned discussion to the
  "petri dish" that attempts to show how Federation
  partners would actually fit into a NewDISS
  mapping.  His point was that to an outside
  observer, this would in fact look like total
  chaos.  Might we not be better off tracing a
  single activity through the value chain?  That
  is, it is probably more useful to focus on a
  single application and see how it expands outward
  and brings in various partners.  The idea of a
  single chart showing all the interrelations might
  be unrealistic and also, in the end, not all that
  helpful.
• Dave Jones points out that this is the approach
  taken by most weather forecasts.  Start out by
  showing local conditions and expand out to the
  national view to explain what factors have
  contributed to make the current conditions.  A
  similar analogy would seem to apply for an Earth
  Science data product or service.  Show the end
  product and trace back to show all the partners
  who contributed to the design.  It could be a
  good way to introduce new people to what it is
  that the Federation does.   

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Earth Science Enterprise, 2005

• NASA-Centric -gt National Applications
• Expand the economical and societal benefits of ES
  information and technology
• REASoN Research, Education and Applications
  Solutions Network
• A distributed network of data and information
  providers for ES, applications and education
  projects
• 42 awards to government (21), university (16),
  commercial (3), and non-profit organizations (2)
• These projects unite previously disparate NASA
  Earth Science activities and programs
• Federation of Earth Science Information Partners
  ESIPFed
• Improve science-based end-to-end processes, rhe
  quality and value of ES products and services
• Composed of 50 agencies, universities,
  companies, non-profit orgs, REASoN projects
• Brings together scientists and organizations that
  have not worked together for the common good
• ESE Budget Summary Preserving a robust Earth
  Science program
• Completing EOS first series mission development
  budget ramps down accordingly
• EOSDIS becoming more efficient with EOSDIS
  Maintenance Development contract
• Research program growing commensurate with
  availability new data from new missions
• Applications program level funded beyond FY05
• Continuing commitments to Climate Change Science
  Program, international cooperative programs

35
Case for Loose Coupling A Network Science
rationale

• The distribution of web connections are is
  scale-free with power-law distribution of
  connections (Barabasi, 2000)
• The number of the number of links k originating
  from a given node exhibits a power law
  distribution. P(k) k-g
• The scale-free pattern of the Web is maintained
  by the continuous, (mostly) autonomous addition
  of new nodes and links among the nodes.
• Any fixed linking structure (i. e. strong
  coupling) among the nodes
• fail to incorporate the new arrivals
• retain broken or obsolete links
• and in general will not be an agile, adoptive
  system
• in other words, it can not satisfy the original
  goals of SEEDS

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Value Chain

• The mission of ESE is better science
• Science is turning obs into actionable knowledge
  by the transforming data into knowledge by
  processing synthesis)
• Two parts of the data life cycle Data
  Acquisition, Data usage focus on usage
• Data access -gt transformation -gt synthesis -gt
  explanatory or actionable knowledge
• This is a Value chain!
• An increasing fraction of the Earth Science data
  are and are web accessible through a variety of
  web services

37
ESIP Federation Insights on Technology

• Rob Raskin
• NASA/Jet Propulsion Laboratory
• California Institute of Technology

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DODS

• Server-side read/subset for most data formats
• Client-side integration with most visualization/
  analysis tools (IDL, MATLAB, VisAD, GrADS)
• About 300 datasets available
• Data Access Protocol (DAP) to be separately
  developed and distributed
• One of the few ESIPs with a specific mission to
  work with other ESIPs

39
DODS (cont.)

• Advantages
• Integration with science visualization software
• Disadvantages
• Catalog system remains weak
• Data must be converted to intermediate format for
  transfer
• User interacts with array row/column parameters
  rather than geographic parameters

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WMS/WCS

• Open standards developed by Open GIS Consortium
  (OGC)
• Web Mapping Server (WMS) for maps
• Web Coverage Server (WCS) for data
• NASA plays major role in standards development
  processes for WMS/WCS
• Eight WMS or WCS servers in place
• Advanced by Digital Earth Cluster (now GIS
  Services Cluster)

41
WMS/WCS (cont.)

• Advantages
• Part of larger suite of standards, e.g. Web
  Feature Server (WFS) for vector data
• Enables overlay of disparate datasets
• Standards developed in conjunction with broader
  communities
• Disadvantages
• WCS still in development
• Complex data types generally not supported

42
Peer-to-Peer (MODster)

• NAPSTER-like functionality for MODIS data
• Essentially a redirection service enabling users
  to find MODIS granules of interest
• Appropriate model for cases where multiple sites
  have similar data product

43
WSDL/UDDI

• WSDL and UDDI provide Web service
  interoperability
• Standard way to access Web services
• Explored by IBM ESIP
• UDDIs currently for business services

44
Technologies in Last Years Winning Proposals

• Universal Interchange Technology for Earth
  Science Data (UNITE) (UAH, JPL, ORNL)
• Plug play based on ESML descriptors
• ESML, WCS integration into FIND
• Standards Framework in Support of Dynamic
  Assembly of NewDISS Components (BASIC, IBM, JPL,
  ORNL, JHU)
• WSDL/UDDI, WMS/WCS, FIND integration
• MODster (UCSB, DODS)
• Peer-to-Peer