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    "info": {
        "author": "Derek Ruths",
        "author_email": "druths@networkdynamics.org",
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            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Utilities"
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        "description": "*Muscle for data science*\r\n\r\nData science projects get disorganized quickly. Every test involves a new\r\nscript, each script requires a panoply of arguments and produces one or more\r\ndata files.  Keeping track of all this implied structure is a pain - what is\r\nthis script for?  What does it depend on?  What created this data file? Which\r\nparameters updated this table in the database?\r\n\r\nEnter flex - a utility that allows you to express and run all the computational\r\ntasks in a project. Crucially, it captures the specific parameters used for\r\neach task, the data files produced, and any dependencies that task has on other\r\ntasks. All this is captured in files called *pipelines* (which can even be\r\nconnected to one another).  Toss in some helpful comments, and you have\r\nexecutable documentation for your project.\r\n\r\nIt aims to achieve all this without introducing any overhead.  You\r\nwon't have to write more code than you currently are doing, write or maintain\r\nany extra documentation, or use fancy data management solutions. Whatever you\r\nare already doing, flex is compatible with it.",
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        "license": "Apache",
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        "summary": "Flex is a framework for building and executing computing pipelines",
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