{ "info": { "author": "The DataLad Team and Contributors", "author_email": "team@datalad.org", "bugtrack_url": null, "classifiers": [], "description": "DataLad extension for semantic metadata handling\n================================================\n\n`Travis tests status `__\n`Build status `__\n`codecov.io `__\n`GitHub\nrelease `__ `PyPI\nversion fury.io `__\n`Documentation `__\n\nThis software is a `DataLad `__ extension that\nequips DataLad with an alternative command suite for metadata handling\n(extraction, aggregation, reporting). It is backward-compatible with the\nmetadata storage format in DataLad proper, while being substantially\nmore performant (especially on large dataset hierarchies). Additionally,\nit provides new metadata extractors and improved variants of DataLad\u2019s\nown ones that are tuned for better performance and richer, JSON-LD\ncompliant metadata reports.\n\nCommand(s) currently provided by this extension\n\n- ``meta-extract`` \u2013 new and improved dedicated command to run any and\n all of DataLad\u2019s metadata extractors.\n- ``meta-aggregate`` \u2013 complete reimplementation of metadata\n aggregation, with stellar performance benefits, in particular on\n large dataset hierarchies.\n- ``meta-dump`` \u2013 new command to specifically access the aggregated\n metadata present in a dataset, much faster and more predictable\n behavior than the ``metadata`` command in datalad-core.\n\nAdditional metadata extractor implementations\n\n- ``metalad_core`` \u2013 enriched variant of the ``datalad_core`` extractor\n that yields valid JSON-LD\n- ``metalad_annex`` \u2013 refurbished variant of the ``annex`` extractor\n using the metalad extractor API\n- ``metalad_custom`` \u2013 read pre-crafted metadata from shadow/side-care\n files for a dataset and/or any file in a dataset.\n- ``metalad_runprov`` \u2013 report provenance metadata for ``datalad run``\n records following the `W3C\n PROV `__ model\n\nInstallation\n------------\n\nBefore you install this package, please make sure that you `install a\nrecent version of\ngit-annex `__. Afterwards,\ninstall the latest version of ``datalad-metalad`` from\n`PyPi `__. It is recommended\nto use a dedicated `virtualenv `__:\n\n::\n\n # create and enter a new virtual environment (optional)\n virtualenv --system-site-packages --python=python3 ~/env/datalad\n . ~/env/datalad/bin/activate\n\n # install from PyPi\n pip install datalad_metalad\n\nSupport\n-------\n\nFor general information on how to use or contribute to DataLad (and this\nextension), please see the `DataLad website `__ or\nthe `main GitHub project page `__. The documentation\nis found here: http://docs.datalad.org/projects/metalad\n\nAll bugs, concerns and enhancement requests for this software can be\nsubmitted here: https://github.com/datalad/datalad-metalad/issues\n\nIf you have a problem or would like to ask a question about how to use\nDataLad, please `submit a question to\nNeuroStars.org `__ with a\n``datalad`` tag. NeuroStars.org is a platform similar to StackOverflow\nbut dedicated to neuroinformatics.\n\nAll previous DataLad questions are available here:\nhttp://neurostars.org/tags/datalad/\n\nAcknowledgements\n----------------\n\nDataLad development is supported by a US-German collaboration in\ncomputational neuroscience (CRCNS) project \u201cDataGit: converging\ncatalogues, warehouses, and deployment logistics into a federated \u2018data\ndistribution\u2019\u201d (Halchenko/Hanke), co-funded by the US National Science\nFoundation (NSF 1429999) and the German Federal Ministry of Education\nand Research (BMBF 01GQ1411). Additional support is provided by the\nGerman federal state of Saxony-Anhalt and the European Regional\nDevelopment Fund (ERDF), Project: Center for Behavioral Brain Sciences,\nImaging Platform. 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