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Ecogrid & Virtual Laboratory for e-Science PowerPoint PPT Presentation


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Ecogrid & Virtual Laboratory for e-Science. from field observation to spatial knowledge. Willem Bouten, project leader Floris Sluiter, design & implementation Guido van Reenen, data analysis Victor Mensing, Vlinderstichting Dirk Zoetebier, Sovon

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Ecogrid & Virtual Laboratory for e-Science

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Ecogrid

&

Virtual Laboratory for e-Science

from field observation to spatial knowledge

Willem Bouten, project leader

Floris Sluiter, design & implementation

Guido van Reenen, data analysis

Victor Mensing, Vlinderstichting

Dirk Zoetebier, Sovon

Aart Jan van der Linden, Talmon Comm.

CBPG


Virtual Laboratory for e-Science

Mission

To boost e-Science by creating an e-Science environment and carrying out research on methodologies.

Essential components

-e-Science development areas

-a Virtual Laboratory development area

-a Large-Scale Distributed computing development area, consisting of high performance networking and grid parts


Virtual Laboratory for e-Science

Aim

To develop and apply a Grid and Virtual Lab technology based information system and research environment.


Virtual Laboratory for e-Science

The VL-e project has four programme lines, most of them containing more than one subprogramme.

P1 – e-Science in Applications

P2 – generic Virtual Laboratory methodology

P3 – large-scale distributes systems

P4 – scaling up & validating in ‘real-time’ applications

VL- e


VL- e

e-Science in Applications

Virtual Laboratory for e-Science

Research of the P1 line is carried out within the following subprogrammes:

P1 – e-Science in Applications

SP1 – Data Intensive Science

SP2 – Food Informatics

SP3 – Medical Diagnosis & Imaging

SP4 – Biodiversity

SP5 – Bioinformatics ASP

SP6 – The Dutch Telescience Laboratory


VL- e

e-Science in Applications

biodiversity

Virtual Laboratory for e-Science

Biodiversity

Aim

To develop and apply a Grid and Virtual Lab technology based information system and research environment

for identification, distribution, integration and analyses of observations and model results of the dynamics of flora and fauna


VL- e

e-Science in Applications

biodiversity

Virtual Laboratory for e-Science

Biodiversity

Partners:

CBPG –Computational Bio- and Physical Geography(IBED – Institute for Biodiversity and Ecosystem Dynamics)

(UvA – Universiteit van Amsterdam)

RNLAF –Royal Netherlands Airforce

VOFF –Association for Research of Flora & Fauna

And co-operation with other programme lines, especially subprogrammes of P2 (generic Virtual Laboratory methodology)

CBPG


VL- e

e-Science in Applications

biodiversity

EcoGrid

Virtual Laboratory for e-Science

Biodiversity

Two main components:

a national database for biodiversity information

 this talk (Ecogrid)

2.a PSE for integrated analysis of observations and model results

 topic of Judy Shamoun-Baranes (BAMBAS)


Ecogrid

Objectives

  • Construct a virtualdatabase that is connected to geographically distributed databases which contain information on the distributions of species, on landscapecharacteristics and on weather.


Ecogrid

Objectives

  • Construct a virtualdatabase that is connected to geographically distributed databases which contain information on the distributions of species, on landscapecharacteristics and on weather.

  • Develop generic methodologies and tools for scale conversions, to be able to integrate and interpret data that are observed at different spatial scales.


Ecogrid

Objectives

  • Construct a virtualdatabase that is connected to geographically distributed databases which contain information on the distributions of species, on landscapecharacteristics and on weather.

  • Develop generic methodologies and tools for scale conversions, to be able to integrate and interpret data that are observed at different spatial scales.

  • Use this infrastructure across the boundaries of organisations to indentify:

  • - spatial wood web structures

  • - biodiversity hotspots

  • - effects of changed land-use

  • - ...


Example: insect diversity and Red-backed Shrikes


}

Nitrogen dep.


Ecogrid - data acquisition

Initial Situation

{

VlinderstichtingFLORONNMVRAVONBLWGVZZ ANNEMOON

EIS-NLTinea

SOVON

VOFF


Ecogrid - data acquisition

taxonomy

observation

locality

}

central

VOFF

database

SOVON

NCO databases


central databases

Data shared by all NCO’s

NCO databases

NCO-specific data

RAVON


central databases

WEB

webpages

GIS

maps

etc.

METADATA

PERSONS

portal login

TAXONOMY

NCO databases


spatial databases

  • Spatial functions to query (SQL) within and across layers:

    • Equals()

    • Disjoint()

    • Intersects()

    • Touches()

    • Crosses()

    • Within()

    • Contains()

    • Overlaps()

  • Special indexing techniques (GisT)

  • ‘Layers’ build with these data types, stored as tables in PostgreSQL/PostGIS.


    Ecogrid system - overview

    Spotfire

    geostatistics

    data mining

    modules

    scale conversion

    modules

    science

    portal

    data

    acquisition

    portal

    EcoGrid

    virtual

    meta

    database

    BAMBAS

    database

    central

    databases

    VOFF

    landscape

    model

    experiments

    weather

    processed

    data

    VL-eScience Research Environment


    Ecogrid system - prototype

    Spotfire

    geostatistics

    data mining

    modules

    scale conversion

    modules

    science

    portal

    data

    acquisition

    portal

    EcoGrid

    virtual

    meta

    database

    BAMBAS

    database

    SOVON

    landscape

    model

    experiments

    weather

    processed

    data

    VL-eScience Research Environment


    Ecogrid - portal prototype


    Ecogrid - portal prototype


    Ecogrid - portal prototype


    Ecogrid - portal prototype


    Ecogrid - portal prototype


    Concluding remarks

    • PostgreSQL (and Postgis) are especially suited for large scientific databases

    • By the end of this year all the databases will be populated and we will have online data acquisition.

    • Then we will shift our focus on using the data and concentrate on Analysis and Datamining


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