Page History

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The Fedora-Kepler investigation was performed by Rick Moore for Fedora Commons and the Cornell University Library. Funding for this project was provided under a grant from the Mellon Foundation.

Project

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Technologies

Fedora Digital Repository

The project used Fedora Repository V3.2.1, released May 8, 2009. It was chosen because it contained the first available release of a streamlined REST API for Fedora. This version is available from Fedora Commons at http://www.fedora-commons.org/software/current. The Fedora REST API is documented at http://fedora-commons.org/confluence/display/FCR30/REST+API.

Kepler Scientific Workflow

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Much of the code developed for this project was written in Jython, an Java implementation of the Python programming language. It provides supplements Java with powerful dynamic language support and gives developers access to the strengths of both Python and Java. Jython is available from The Jython Project (http:www.jython.org) web site.

To learn more about Jython visit one of the following sites:

• Jython Users Guide - http://wiki.python.org/jython/UserGuide
• "Jython Essentials", by Samuele Pedroni & Noel Rappin - http://www.oreilly.com/catalog/jythoness/ - this is a bit outdated but still useful
• Online training course by Dave Kuhlman http://www.rexx.com/~dkuhlman/jython_course_03.html

JyFedoREST

JyFedoREST is a Jython package for creating and managing objects in a Fedora Repository. It is a minimal Jython wrapper around V3.2.1 of the Fedora REST API. It is described in detail on it's own project page.

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Several actors and workflows were developed to test Fedora's REST API using JyFedorREST from within Kepler. They are described in detail on the REST API Tests page. The most interesting of these is the Ingest With Datastream workflow.

Test Case - DataSTAR

DataSTAR is a Cornell Library initiative aimed at providing a staging archive for scientific data that is to be shared among several collaborators, both within the Cornell community and external to it.The actors and workflows developed for this part of the project are descibed in detail on the DataSTAR Use Case page.

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