Ticer summer school_24_aug06

Ticer Summer School
Thursday 24th
August 2006
Dave Berry & Malcolm Atkinson
National e-Science Centre, Edinburgh
www.nesc.ac.uk

Digital Libraries, Grids & E-ScienceDigital Libraries, Grids & E-Science
What is E-Science?
What is Grid Computing?
Data Grids
Requirements
Examples
Technologies
Data Virtualisation
The Open Grid Services Architecture
Challenges

What is e-Science?What is e-Science?
• Goal: to enable better research in all disciplines
• Method: Develop collaboration supported by
advanced distributed computation
– to generate, curate and analyse rich data resources
• From experiments, observations, simulations & publications
• Quality management, preservation and reliable evidence
– to develop and explore models and simulations
• Computation and data at all scales
• Trustworthy, economic, timely and relevant results
– to enable dynamic distributed collaboration
• Facilitating collaboration with information and resource sharing
• Security, trust, reliability, accountability, manageability and agility

climateprediction.net and GENIE
• Largest climate model
ensemble
• >45,000 users, >1,000,000
model years
10K2K
Response of Atlantic
circulation to freshwater
forcing

6
Courtesy of David Gavaghan &
IB Team
Integrative Biology
Tackling two Grand Challenge research
questions:
• What causes heart disease?
• How does a cancer form and grow?
Together these diseases cause 61% of all UK
deaths
Building a powerful, fault-tolerant Grid
infrastructure for biomedical science
Enabling biomedical researchers to use
distributed resources such as high-performance
computers, databases and visualisation tools to
develop coupled multi-scale models of how
these killer diseases develop.

BBiomedicaliomedical RResearchesearch IInformaticsnformatics DDelivered byelivered by GGridrid
EEnablednabled SServiceservices
Glasgow
Edinburgh
Leicester
Oxford
London
Netherlands
Publically Curated Data
Private
data
Private
data
Private
data
Private
data
Private
data
Private
data
CFG Virtual
Organisation
Ensembl
MGI
HUGO
OMIM
SWISS-PROT
…
DATA
HUB
RGD
Synteny
Grid
Service
blast
+
Portal
http://www.brc.dcs.gla.ac.uk/projects/bridges/

eDiaMoND: Screening for Breast CancereDiaMoND: Screening for Breast Cancer
1 Trust  Many Trusts
Collaborative Working
Audit capability
Epidemiology
Other Modalities
-MRI
-PET
-Ultrasound
Better access to
Case information
And digital tools
Supplement Mentoring
With access to digital
Training cases and sharing
Of information across
clinics
Letters
Radiology reporting
systems
eDiaMoND
Grid
2ndary Capture
Or FFD
Case Information
X-Rays and
Case Information
Digital
Reading
SMF
Case and
Reading Information
CAD Temporal Comparison
Screening
Electronic
Patient Records
Assessment/ Symptomatic
Biopsy
Case and
Reading Information
Symptomatic/Assessment
Information
Training
Manage Training Cases
Perform Training
SMF CAD 3D Images
Patients
Provided by eDiamond project: Prof. Sir Mike Brady et al.

E-Science Data ResourcesE-Science Data Resources
• Curated databases
– Public, institutional, group, personal
• Online journals and preprints
• Text mining and indexing services
• Raw storage (disk & tape)
• Replicated files
• Persistent archives
• Registries
• …

TICER Summer School, August 24th 2006
©
10
EBank
Slide
from
Jeremy
Frey

©
11
Biomedical data – making
connections
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ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag
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gcctgttttt ttttaattgg
Slide provided by Carole Goble: University of Manchester

Using Workflows to Link ServicesUsing Workflows to Link Services
• Describe the steps in a Scripting Language
• Steps performed by Workflow Enactment Engine
• Many languages in use
– Trade off: familiarity & availability
– Trade off: detailed control versus abstraction
• Incrementally develop correct process
– Sharable & Editable
– Basis for scientific communication & validation
– Valuable IPR asset
• Repetition is now easy
– Parameterised explicitly & implicitly

Workflow SystemsWorkflow Systems
Language WF Enact. Comments
Shell
scripts
Shell + OS Common but not often thought of as WF. Depend
on context, e.g. NFS across all sites
Perl Perl
runtime
Popular in bioinformatics. Similar context
dependence – distribution has to be coded
Java JVM Popular target because JVM ubiquity – similar
dependence – distribution has to be coded
BPEL BPEL
Enactment
OASIS standard for industry – coordinating use of
multiple Web Services – low level detail - tools
Taverna Scufl EBI, OMII-UK & MyGrid
http://taverna.sourceforge.net/index.php
VDT /
Pegasus
Chimera &
DAGman
High-level abstract formulation of workflows,
automated mapping towards executable forms,
cached result re-use
Kepler Kepler BIRN, GEON & SEEK
http://kepler-project.org/

©
14
Workflow example
 Taverna in MyGrid http://www.mygrid.org.uk/
 “allows the e-Scientist to describe and enact their
experimental processes in a structured, repeatable
and verifiable way”
 GUI
 Workflow
language
 Enactment
engine

©
15
Pub/Sub for Laboratory data
using a broker and ultimately
delivered over GPRS
Notification
Comb-e-chem: Jeremy Frey

Relevance to Digital LibrariesRelevance to Digital Libraries
• Similar concerns
– Data curation & management
– Metadata, discovery
– Secure access (AAA +)
– Provenance & data quality
– Local autonomy
– Availability, resilience
• Common technology
– Grid as an implementation technology

TICER Summer School, August 24th 2006 18
What is a Grid?
LicenseLicense
PrinterPrinter
A grid is a system consisting of
− Distributed but connected resources and
− Software and/or hardware that provides and manages logically
seamless access to those resources to meet desired objectives
A grid is a system consisting of
− Distributed but connected resources and
− Software and/or hardware that provides and manages logically
seamless access to those resources to meet desired objectives
R2AD
DatabaseDatabase
Web
server
Web
server
Data CenterCluster
Handheld Supercomputer
Workstation
Server
Source: Hiro Kishimoto GGF17 Keynote May 2006

Virtualizing Resources
Resources
Web
services
AccessAccess
StorageStorage SensorsSensors ApplicationsApplications InformationInformationComputersComputers
Resource-specific InterfacesResource-specific Interfaces
Common Interfaces
Type-specific interfaces
Hiro Kishimoto: Keynote GGF17

Ideas and FormsIdeas and Forms
• Key ideas
– Virtualised resources
– Secure access
– Local autonomy
• Many forms
– Cycle stealing
– Linked supercomputers
– Distributed file systems
– Federated databases
– Commercial data centres
– Utility computing

Grid Middleware
Virtualized
resources
Grid
middleware
services
Brokering
Service
Brokering
Service
Registry
Service
Registry
Service
Data
Service
Data
Service
CPU
Resource
CPU
Resource Printer
Service
Printer
Service
Job-Submit
Service
Job-Submit
Service
Compute
Service
Compute
Service
Notify
Advertise
Application
Service
Application
Service

Key Drivers for GridsKey Drivers for Grids
• Collaboration
– Expertise is distributed
– Resources (data, software licences) are location-specific
– Necessary to achieve critical mass of effort
– Necessary to raise sufficient resources
• Computational Power
– Rapid growth in number of processors
– Powered by Moore’s law + device roadmap
– Challenge to transform models to exploit this
• Deluge of Data
– Growth in scale: Number and Size of resources
– Growth in complexity
– Policy drives greater data availability

Minimum Grid FunctionalitiesMinimum Grid Functionalities
• Supports distributed computation
– Data and computation
– Over a variety of
• hardware components (servers, data stores, …)
• Software components (services: resource managers,
computation and data services)
– With regularity that can be exploited
• By applications
• By other middleware & tools
• By providers and operations
– It will normally have security mechanisms
• To develop and sustain trust regimes

Grid & Related Paradigms
Utility Computing
• Computing “services”
• No knowledge of provider
• Enabled by grid technology
Utility Computing
• Computing “services”
• No knowledge of provider
• Enabled by grid technology
Distributed Computing
• Loosely coupled
• Heterogeneous
• Single Administration
Distributed Computing
• Loosely coupled
• Heterogeneous
• Single Administration
Cluster
• Tightly coupled
• Homogeneous
• Cooperative working
Cluster
• Tightly coupled
• Homogeneous
• Cooperative working
Grid Computing
• Large scale
• Cross-organizational
• Geographical distribution
• Distributed Management
Grid Computing
• Large scale
• Cross-organizational
• Geographical distribution
• Distributed Management

Why use / build Grids?Why use / build Grids?
• Research Arguments
– Enables new ways of working
– New distributed & collaborative research
– Unprecedented scale and resources
• Economic Arguments
– Reduced system management costs
– Shared resources ⇒ better utilisation
– Pooled resources ⇒ increased capacity
– Load sharing & utility computing
– Cheaper disaster recovery

Why use / build Grids?Why use / build Grids?
• Operational Arguments
– Enable autonomous organisations to
• Write complementary software components
• Set up run & use complementary services
• Share operational responsibility
• General & consistent environment for
Abstraction, Automation, Optimisation & Tools
• Political & Management Arguments
– Stimulate innovation
– Promote intra-organisation collaboration
– Promote inter-enterprise collaboration

Grids In Use: E-Science Examples
• Data sharing and integration
− Life sciences, sharing standard data-sets,
combining collaborative data-sets
− Medical informatics, integrating hospital information
systems for better care and better science
− Sciences, high-energy physics
• Data sharing and integration
− Life sciences, sharing standard data-sets,
combining collaborative data-sets
− Medical informatics, integrating hospital information
systems for better care and better science
− Sciences, high-energy physics
• Capability computing
− Life sciences, molecular modeling, tomography
− Engineering, materials science
− Sciences, astronomy, physics
• Capability computing
− Life sciences, molecular modeling, tomography
− Engineering, materials science
− Sciences, astronomy, physics
• High-throughput, capacity computing for
− Life sciences: BLAST, CHARMM, drug screening
− Engineering: aircraft design, materials, biomedical
− Sciences: high-energy physics, economic modeling
• High-throughput, capacity computing for
− Life sciences: BLAST, CHARMM, drug screening
− Engineering: aircraft design, materials, biomedical
− Sciences: high-energy physics, economic modeling
• Simulation-based science and engineering
− Earthquake simulation
• Simulation-based science and engineering
− Earthquake simulation

PDB 33,367 Protein structuresEMBL DB 111,416,302,701 nucleotides
Database GrowthDatabase Growth
Slide provided by Richard Baldock: MRC HGU Edinburgh

Requirements: User’s viewpointRequirements: User’s viewpoint
• Find Data
– Registries & Human communication
• Understand data
– Metadata description, Standard / familiar formats &
representations, Standard value systems & ontologies
• Data Access
– Find how to interact with data resource
– Obtain permission (authority)
– Make connection
– Make selection
• Move Data
– In bulk or streamed (in increments)

Requirements: User’s viewpoint 2Requirements: User’s viewpoint 2
• Transform Data
– To format, organisation & representation
required for computation or integration
• Combine data
– Standard database operations + operations relevant to
the application model
• Present results
– To humans: data movement + transform for viewing
– To application code: data movement + transform to the
required format
– To standard analysis tools, e.g. R
– To standard visualisation tools, e.g. Spitfire

Requirements: Owner’s viewpointRequirements: Owner’s viewpoint
• Create Data
– Automated generation, Accession Policies, Metadata
generation
– Storage Resources
• Preserve Data
– Archiving
– Replication
– Metadata
– Protection
• Provide Services with available resources
– Definition & implementation: costs & stability
– Resources: storage, compute & bandwidth

Requirements: Owner’s viewpoint 2Requirements: Owner’s viewpoint 2
• Protect Services
– Authentication, Authorisation, Accounting, Audit
– Reputation
• Protect data
– Comply with owner requirements – encryption for privacy,
…
• Monitor and Control use
– Detect and handle failures, attacks, misbehaving users
– Plan for future loads and services
• Establish case for Continuation
– Usage statistics
– Discoveries enabled

Large Hadron ColliderLarge Hadron Collider
• The most powerful
instrument ever built to
investigate elementary
particle physics
• Data Challenge:
– 10 Petabytes/year of data
– 20 million CDs each year!
• Simulation, reconstruction,
analysis:
– LHC data handling requires
computing power equivalent
to ~100,000 of today's fastest
PC processors

Composing Observations in AstronomyComposing Observations in Astronomy
Data and images courtesy Alex Szalay, John Hopkins
No. & sizes of data sets as of mid-2002,
grouped by wavelength
• 12 waveband coverage of large
areas of the sky
• Total about 200 TB data
• Doubling every 12 months
• Largest catalogues near 1B objects

GODIVA Data Portal
• Grid for Ocean Diagnostics, Interactive
Visualisation and Analysis
• Daily Met Office Marine Forecasts and
gridded research datasets
• National Centre for Ocean Forecasting
• ~3Tb climate model datastore via Web
Services
• Interactive Visualisations inc. Movies
• ~ 30 accesses a day worldwide
• Other GODIVA software produces
3D/4D Visualisations reading data
remotely via Web Services
Online Movies
www.nerc-essc.ac.uk/godiva

GODIVA Visualisations
• Unstructured Meshes
• Grid Rotation/Interpolation
• GeoSpatial Databases v. Files
(Postgres, IBM, Oracle)
• Perspective 3D Visualisation
• Google maps viewer

NERC Data Grid
• The DataGrid focuses on federation of
NERC Data Centres
• Grid for data discovery, delivery and use
across sites
• Data can be stored in many different
ways (flat files, databases…)
• Strong focus on Metadata and
Ontologies
• Clear separation between discovery and
use of data.
• Prototype focussing on Atmospheric and
Oceanographic data
www.ndg.nerc.ac.uk

Global In-flight Engine DiagnosticsGlobal In-flight Engine Diagnostics
in-flight data
airline
maintenance centre
ground
station
global network
eg SITA
internet, e-mail, pager
DS&S Engine Health Center
data centre
Distributed Aircraft Maintenance Environment: Leeds, Oxford, Sheffield &York, Jim Austin
100,000 aircraft
0.5 GB/flight
4 flights/day
200 TB/day
Now BROADEN
Significant in
getting Boeing
787 engine
contract

Storage Resource Manager (SRM)Storage Resource Manager (SRM)
• http://sdm.lbl.gov/srm-wg/
• de facto & written standard in physics, …
• Collaborative effort
– CERN, FNAL, JLAB, LBNL and RAL
• Essential bulk file storage
– (pre) allocation of storage
• abstraction over storage systems
– File delivery / registration / access
– Data movement interfaces
• E.g. gridFTP
• Rich function set
– Space management, permissions, directory, data transfer
& discovery

Storage Resource Broker (SRB)Storage Resource Broker (SRB)
• http://www.sdsc.edu/srb/index.php/Main_Page
• SDSC developed
• Widely used
– Archival document storage
– Scientific data: bio-sciences, medicine, geo-sciences, …
• Manages
– Storage resource allocation
• abstraction over storage systems
– File storage
– Collections of files
– Metadata describing files, collections, etc.
– Data transfer services

Condor Data ManagementCondor Data Management
• Stork
– Manages File Transfers
– May manage reservations
• Nest
– Manages Data Storage
– C.f. GridFTP with reservations
• Over multiple protocols

Globus Tools and Services
for Data Management
GridFTP
A secure, robust, efficient data transfer protocol
The Reliable File Transfer Service (RFT)
Web services-based, stores state about transfers
The Data Access and Integration Service (OGSA-DAI)
Service to access to data resources, particularly relational and
XML databases
The Replica Location Service (RLS)
Distributed registry that records locations of data copies
The Data Replication Service
Web services-based, combines data replication and
registration functionality
Slides from Ann Chervenak

RLS in Production Use: LIGO
Laser Interferometer Gravitational Wave Observatory
Currently use RLS servers at 10 sites
Contain mappings from 6 million logical files to over 40
million physical replicas
Used in customized data management system: the
LIGO Lightweight Data Replicator System (LDR)
Includes RLS, GridFTP, custom metadata catalog, tools for
storage management and data validation

RLS in Production Use: ESG
Earth System Grid: Climate
modeling data (CCSM, PCM,
IPCC)
RLS at 4 sites
Data management
coordinated by ESG portal
Datasets stored at NCAR
64.41 TB in 397253 total files
1230 portal users
IPCC Data at LLNL
26.50 TB in 59,300 files
400 registered users
Data downloaded: 56.80 TB
in 263,800 files
Avg. 300GB downloaded/day
200+ research papers being
written

TICER Summer School, August 24th 20062nd
EGEE Review, CERN - 49
Enabling Grids for E-sciencE
INFSO-RI-508833
gLite Data Management
• FTS
– File Transfer Service
• LFC
– Logical file catalogue
• Replication Service
– Accessed through LFC
• AMGA
– Metadata services

INFSO-RI-508833
Data Management Services
• FiReMan catalog
– Resolves logical filenames (LFN) to physical location of files and storage elements
– Oracle and MySQL versions available
– Secure services
– Attribute support
– Symbolic link support
– Deployed on the Pre-Production Service and DILIGENT testbed
• gLite I/O
– Posix-like access to Grid files
– Castor, dCache and DPM support
– Has been used for the BioMedical Demo
– Deployed on the Pre-Production Service and the DILIGENT testbed
• AMGA MetaData Catalog
– Used by the LHCb experiment
– Has been used for the BioMedical Demo
Medical Data Management 3
ClientClient
Medical Data Management
Application
MDM Client LibraryMDM Client Library
Grid CatalogsGrid Catalogs
MetadataMetadata
Catalog (AMGA)Catalog (AMGA)
Medical
Imager
EncryptionEncryption
KeystoreKeystore (Hydra)(Hydra)
File CatalogFile Catalog
(Fireman)(Fireman)
SRM DICOMSRM DICOM
MDM TriggerMDM Trigger
GridFTPGridFTP
gLitegLite I/OI/O
Trigger:
• Retrieve DICOM files
from imager.
• Register file in
Fireman
• gLite EDS client:
Generate encryption
keys and store them
in Hydra
• Register Metadata in
AMGA
Client Library:
• Lookup file through Metadata
(AMGA)
• Use gLite EDS client:
• Retrieve file through gLite I/O
• Retrieve encryption Key from
Hydra
• Decrypt data
• Serve it up to the application

INFSO-RI-508833
File Transfer Service
• Reliable file transfer
• Full scalable implementation
– Java Web Service front-end, C++ Agents, Oracle or MySQL database support
– Support for Channel, Site and VO management
– Interfaces for management and statistics monitoring
• Gsiftp, SRM and SRM-copy support
• Support for MySQL and Oracle
• Multi-VO support
• GridFTP and SRM copy support

Commercial SolutionsCommercial Solutions
• Vendors include:
– Avaki
– Data Synapse
• Benefits & costs
– Well packaged and documented
– Support
– Can be expensive
• But look for academic rates

Data Integration StrategiesData Integration Strategies
• Use a Service provided by a Data Owner
• Use a scripted workflow
• Use data virtualisation services
– Arrange that multiple data services have common
properties
– Arrange federations of these
– Arrange access presenting the common
properties
– Expose the important differences
– Support integration accommodating those
differences

Data Virtualisation ServicesData Virtualisation Services
• Form a federation
– Set of data resources – incremental addition
– Registration & description of collected resources
– Warehouse data or access dynamically to obtain updated data
– Virtual data warehouses – automating division between collection and
dynamic access
• Describe relevant relationships between data sources
– Incremental description + refinement / correction
• Run jobs, queries & workflows against combined set of data
resources
– Automated distribution & transformation
• Example systems
– IBM’s Information Integrator
– GEON, BIRN & SEEK
– OGSA-DAI is an extensible framework for building such systems

Virtualisation variationsVirtualisation variations
• Extent to which homogeneity obtained
– Regular representation choices – e.g. units
– Consistent ontologies
– Consistent data model
– Consistent schema – integrated super-schema
– DB operations supported across federation
– Ease of adding federation elements
– Ease of accommodating change as federation
members change their schema and policies
– Drill through to primary forms supported

OGSA-DAIOGSA-DAI
• http://www.ogsadai.org.uk
• A framework for data virtualisation
• Wide use in e-Science
– BRIDGES, GEON, CaBiG, GeneGrid, MyGrid,
BioSimGrid, e-Diamond, IU RGRBench, …
• Collaborative effort
– NeSC, EPCC, IBM, Oracle, Manchester, Newcastle
• Querying of data resources
– Relational databases
– XML databases
– Structured flat files
• Extensible activity documents
– Customisation for particular applications

The Open Grid Services Architecture
• An open, service-oriented architecture (SOA)
− Resources as first-class entities
− Dynamic service/resource creation and destruction
• Built on a Web services infrastructure
• Resource virtualization at the core
• Build grids from small number of standards-based
components
− Replaceable, coarse-grained
− e.g. brokers
• Customizable
− Support for dynamic, domain-specific content…
− …within the same standardized framework

OGSA Capabilities
Security
• Cross-organizational users
• Trust nobody
• Authorized access only
Security
• Cross-organizational users
• Trust nobody
• Authorized access only
Information Services
• Registry
• Notification
• Logging/auditing
Information Services
• Registry
• Notification
• Logging/auditing
Execution Management
• Job description & submission
• Scheduling
• Resource provisioning
Execution Management
• Job description & submission
• Scheduling
• Resource provisioning
Data Services
• Common access facilities
• Efficient & reliable transport
• Replication services
Data Services
• Common access facilities
• Efficient & reliable transport
• Replication services
Self-Management
• Self-configuration
• Self-optimization
• Self-healing
Self-Management
• Self-configuration
• Self-optimization
• Self-healing
Resource Management
• Discovery
• Monitoring
• Control
Resource Management
• Discovery
• Monitoring
• Control
OGSAOGSA
OGSA “profiles”OGSA “profiles”
Web services foundationWeb services foundation

Basic Data Interfaces
• Storage Management
− e.g. Storage Resource
Management (SRM)
• Storage Management
− e.g. Storage Resource
Management (SRM)
• Data Access
− ByteIO
− Data Access & Integration
(DAI)
• Data Access
− ByteIO
− Data Access & Integration
(DAI)
• Data Transfer
− Data Movement Interface
Specification (DMIS)
− Protocols (e.g. GridFTP)
• Data Transfer
− Data Movement Interface
Specification (DMIS)
− Protocols (e.g. GridFTP)
• Replica management
• Metadata catalog
• Cache management
• Replica management
• Metadata catalog
• Cache management

The State of the ArtThe State of the Art
• Many successful Grid & E-Science projects
– A few examples shown in this talk
• Many Grid systems
– All largely incompatible
– Interoperation talks under way
• Standardisation efforts
– Mainly via the Open Grid Forum
– A merger of the GGF & EGA
• Significant user investment required
– Few “out of the box” solutions

Technical ChallengesTechnical Challenges
• Issues you can’t avoid
– Lack of Complete Knowledge (LOCK)
– Latency
– Heterogeneity
– Autonomy
– Unreliability
– Scalability
– Change
• A Challenging goal
– balance technical feasibility
– against virtual homogeneity, stability and reliability
– while remaining affordable, manageable and maintainable

Areas “In Development”Areas “In Development”
• Data provenance
• Quality of Service
– Service Level Agreements
• Resource brokering
– Across all resources
• Workflow scheduling
– Co-sheduling
• Licence management
• Software provisioning
– Deployment and update
• Other areas too!

Operational ChallengesOperational Challenges
• Management of distributed systems
– With local autonomy
• Deployment, testing & monitoring
• User training
• User support
• Rollout of upgrades
• Security
– Distributed identity management
– Authorisation
– Revocation
– Incident response

Grids as a Foundation for SolutionsGrids as a Foundation for Solutions
• The grid per se doesn’t provide
– Supported e-Science methods
– Supported data & information resources
– Computations
– Convenient access
• Grids help providers of these, via
– International & national secure e-Infrastructure
– Standards for interoperation
– Standard APIs to promote re-use
• But Research Support must be built
– Application developers
– Resource providers

Collaboration ChallengesCollaboration Challenges
• Defining common goals
• Defining common formats
– E.g. schemas for data and metadata
• Defining a common vocabulary
– E.g. for metadata
• Finding common technology
– Standards should help, eventually
• Collecting metadata
– Automate where possible

Social ChallengesSocial Challenges
• Changing cultures
– Rewarding data & resource sharing
– Require publication of data
• Taking the first steps
– If everyone shares, everyone wins
– The first people to share must not lose out
• Sustainable funding
– Technology must persist
– Data must persist

SummarySummary
• E-Science exploits distributed computing
resource to enable new discoveries, new
collaborations and new ways of working
• Grid is an enabling technology for e-science.
• Many successful projects exist
• Many challenges remain

Globus Alliance
CeSC (Cambridge)
Digital
Curation
Centre
e-Science
Institute
UK e-ScienceUK e-Science
EGEE,
ChinaGri
Grid
Operations
Support
Centre
National
Centre for
e-Social
Science
National
Institute
for
Environmental
e-Science
Open
Middleware
Infrastructure
Institute

Ticer summer school_24_aug06

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