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A New Generation of Data Services for Earth System Science Education and
Research: Unidata’s Plans and Directions
AGU Fall MeetingSan Francisco, CA6 December 2005
Dr. Mohan RamamurthyDirector, Unidata Program Center
UCAR Office of ProgramsBoulder, CO
Science DriversScience Drivers Environmental problems like Environmental problems like
global change & water cycle global change & water cycle transcend disciplinary as well as transcend disciplinary as well as geographic boundaries, requiring geographic boundaries, requiring multidisciplinary approaches and multidisciplinary approaches and global teams for solving them;global teams for solving them;
Rapid advances in observational Rapid advances in observational technologies, especially in technologies, especially in remote sensing;remote sensing;
Increasing use of complex, Increasing use of complex, coupled modeling systems;coupled modeling systems;
Research studies on societal impact of hurricane-related flooding involve integrating data from atmospheric sciences, oceanography, hydrology, geology, geography, and social sciences.
Science Drivers: ExamplesScience Drivers: Examples
Emergency Response
Ensemble Predictions
Coastal Environments
GIS Integration
End to End Information Services
Need integrated services
Education DriversEducation Drivers
A “holistic” Earth-A “holistic” Earth-system science system science approach to educationapproach to education
Active, student-Active, student-centered learning. i.e., centered learning. i.e., learning science by learning science by doing sciencedoing science• Observations (data)Observations (data)• Tools (models, Tools (models,
visualization)visualization)• DiscoveryDiscovery
Technology Trends Enabling a Technology Trends Enabling a New Generation of Data ServicesNew Generation of Data Services
• Internet & the World Wide WebInternet & the World Wide Web• Commodity microprocessorsCommodity microprocessors• Object-oriented programmingObject-oriented programming• Open standardsOpen standards• Web services Web services • Extensible Markup Language Extensible Markup Language
(XML)(XML)• Global, high-bandwidth and Global, high-bandwidth and
wireless networkswireless networks• Digital librariesDigital libraries• CollaboratoriesCollaboratories• Grid Computing/e-ScienceGrid Computing/e-Science• Data Portals and Federated, Data Portals and Federated,
distributed Serversdistributed Servers• Geographic Information Systems Geographic Information Systems • Knowledge environmentsKnowledge environments• Ontologies and Semantic web Ontologies and Semantic web • Data mining and knowledge Data mining and knowledge
discoverydiscovery
Data Services: An Evolution
An evolution from proprietary data systems towards more open standards-based data services – i.e. web services.Data services should address the myriad applications and needs of the community: research, education, outreach, collaboration, etc.
This transition to web services poses many challenges.
Service-Oriented ScienceService-Oriented Science
Source: Ian Foster, Source: Ian Foster,
Science, 6 May 2005Science, 6 May 2005
Web Services are self-contained, self-describing, modular applications that can be published, located, and invoked across the Web.
XML based Web Services are emerging as tools for creating next generation distributed systems that facilitate program-to-program interaction without the user-to-program interaction.
Besides recognizing the heterogeneity as a fundamental ingredient, these web services, independent of platform and environment, can be packaged and published on the internet as they can communicate with other systems using the common protocols.
Emerging web services standards are enabling much easier system-to-system integration.
Google Maps and Personal Google Maps and Personal Weather DataWeather Data
How is this magic performed?
Answer: DHTML, JavaScript, CSS, XML, and XSLT
Google EarthGoogle Earth
A Partial List of Data ServicesA Partial List of Data Services
CollectionCollection TransportTransport NotificationNotification Cataloging and metadata generationCataloging and metadata generation Metadata submissionMetadata submission SubsettingSubsetting AggregationAggregation Decoders/format converterDecoders/format converter QueryingQuerying VisualizationVisualization CollaborationCollaboration OntologyOntology Data miningData mining Weblogging or blogging Weblogging or blogging ……
End–To–End Data Service End–To–End Data Service Development at UnidataDevelopment at Unidata
IDD Data LDM
THREDDSCatalog
Browse
Access
THREDDSData
Server(TDS)
THREDDSData
Repository(TDR)
Put Data
TDS Client
NotifyE-mail
Application(e.g. IDV)
Service
DataStorage
TDR Client
LocallyGenerated
Data
LocallyGenerated
Data
Internet Data DistributionInternet Data Distribution
Source
LDM
Source
Source
LDM LDM
LDMLDM
LDM LDM
LDM
LDM
Internet
RadarModel
Satellite
Fil
e F
orm
ats
Lo
cal/
Rem
ote
Ser
vice
sU
nd
erly
ing
In
terf
aces
Pri
mar
yIn
terf
aces
THREDDScatalog
TDS
(THREDDS Data Server Interface)
OGC WCS (Web Coverage Server)
NetCDF interfaceOPeNDAP
OpenDAP ADDE
GRID
Station
AREA
IOsp
GRIB
GININIDS
netCDF via HTTP
Jgoffs
DMSP
NetCDF NetCDF
TDS: A Collection of ServicesTDS: A Collection of Services
TDR: Another ViewTDR: Another View
PeoplePeople
DocumentsDocuments DataData
Catalog
Generation Tools
Analysis andVisualization Tools
Data Services
Discovery andPublication Tools
Discovery and Publication Services
Dat
a C
atal
ogS
ervi
ces
PeoplePeople
DocumentsDocuments DataData
Catalog
Generation Tools
Analysis andVisualization Tools
Data Services
Discovery andPublication Tools
Discovery and Publication Services
Dat
a C
atal
ogS
ervi
ces
Data Services for Education
THREDDSMiddleware
For effective incorporation of data into digital libraries, we need a range of data services tailored for education:
integrate data, models, viz. tools with learning objects and other curricular materials
NCSAOU
Unidata
Unidata
OU
User running local analysis and display tools
Data Service
Decoder Service
Assimilation Service Prediction
Service
User Orchestrates Web Services to Create Regional
Forecast
Product Generation & Mining Service
LEAD: Data Services for NWP
IU
Orchestration Service
UAH
Data Services for Field ProjectsData Services for Field Projects
GALEON IE: Data services for GALEON IE: Data services for GIS Integration GIS Integration
Concluding RemarksConcluding Remarks At Unidata, we are in the process of building many of At Unidata, we are in the process of building many of
these data-related services and technologies;these data-related services and technologies;
Despite the significant progress, much work still lies Despite the significant progress, much work still lies
ahead;ahead;
Other organizations and communities are engaged in Other organizations and communities are engaged in
similar exercises (e.g., NOAA, IOOS, and CUAHSI) ;similar exercises (e.g., NOAA, IOOS, and CUAHSI) ;
We will partner with those groups where necessary We will partner with those groups where necessary
and leverage each other’s work;and leverage each other’s work;
We are diligently building bridges with other We are diligently building bridges with other
communities for mutual benefit.communities for mutual benefit.
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