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Introduction to Kepler. Deana Pennington, PhD University of New Mexico LTER Network Office, Sevilleta LTER PI CI-Team: Advancing CI-Based Science through Education, Training, and Mentoring of Science Communities CoPI Science Environment for Ecological Knowledge (SEEK) project July 10, 2007.
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Introduction to Kepler Deana Pennington, PhD University of New Mexico LTER Network Office, Sevilleta LTER PI CI-Team: Advancing CI-Based Science through Education, Training, and Mentoring of Science Communities CoPI Science Environment for Ecological Knowledge (SEEK) project July 10, 2007
Science Environment for Ecological Knowledge (SEEK) project Partnership for Biodiversity Informatics (PBI) National Science Foundation-funded large ITR
Which version? • Beta is stable, but with more limited functionality • Beta does not work with Java 1.6 – use 1.5 or install kepler-1.0.beta3-jre, which includes a compatible version • Nightly build has most current functionality – but some is undocumented • Nightly build is sometimes broken – but builds from the prior 3 nights are also available
Opening Kepler Installed version: • Start • My Programs • Kepler Nightly: • Kepler.bat
Director/Actor Metaphor Actor Actors know HOW to act..know their part Directors know WHEN they should act Actor Actor • Directors define the model of computation to be used in the workflow • Every workflow must specify a director
Actors & ports 1 input port 2 output ports Atomic actor Reads a dataset Initializing parameters actor name function data Input data Output data parameters ports
Open Actor input output Composite Actors Composite actor
Graph Editor Tool bar Search Model building area (Canvas) Library of components Navigation area
Discovery • Data • Actors • Directors
EarthGrid registered data show up in KEPLER Grid get Grid query
ENM in Kepler: Conceptual Workflow Occurrence Points Gridded layers: Climate Topography Legend EcoGrid query through Kepler C R Modeling Prediction Kepler Native Filter out If n < X, where n = count of occurrences X is user defined IPCC future climate scenarios (S = 21) MaNIS Species Locations (L) Append datasets Store points as ASCII 1 IPCC present climate layers (C) n = 7 Restructure For each S Convex Hull Mask Convert layers to binary Input Parameters Rescale Projection Extent Grain Restructure For each C Append layers For each C, S & T Rescale values Hydro1k topographic layers (T) n = 4 Restructure For each T GIS: GDAL/Java Testing data sample set For i = 1 to n n = # of models GARP model training & prediction of present distribution (P) Select best models (m) Calculate model error Sample Data 2 sets 1 2 Combine prediction results => probability map For each S integrated with T For each P & F prediction from models (m) = 22 Predict future distribution (F) from model For each model in m Dispersal analysis 2 GIS/R
ENM in Kepler: Executable Workflow Top level