datalad-metalad 0.2.1

# DataLad extension for semantic metadata handling

[](https://travis-ci.org/datalad/datalad-metalad) [](https://ci.appveyor.com/project/mih/datalad-metalad) [](https://codecov.io/github/datalad/datalad-metalad?branch=master) [](https://GitHub.com/datalad/datalad-metalad/releases/) [](https://pypi-hypernode.com/pypi/datalad-metalad/) [](http://docs.datalad.org/projects/metalad/en/latest)

This software is a [DataLad](http://datalad.org) extension that equips DataLad with an alternative command suite for metadata handling (extraction, aggregation, reporting). It is backward-compatible with the metadata storage format in DataLad proper, while being substantially more performant (especially on large dataset hierarchies). Additionally, it provides new metadata extractors and improved variants of DataLad’s own ones that are tuned for better performance and richer, JSON-LD compliant metadata reports.

Command(s) currently provided by this extension

• meta-extract – new and improved dedicated command to run any and all of DataLad’s metadata extractors.

• meta-aggregate – complete reimplementation of metadata aggregation, with stellar performance benefits, in particular on large dataset hierarchies.

• meta-dump – new command to specifically access the aggregated metadata present in a dataset, much faster and more predictable behavior than the metadata command in datalad-core.

Additional metadata extractor implementations

• metalad_core – enriched variant of the datalad_core extractor that yields valid JSON-LD

• metalad_annex – refurbished variant of the annex extractor using the metalad extractor API

• metalad_custom – read pre-crafted metadata from shadow/side-care files for a dataset and/or any file in a dataset.

• metalad_runprov – report provenance metadata for datalad run records following the [W3C PROV](https://www.w3.org/TR/prov-overview) model

## Installation

Before you install this package, please make sure that you [install a recent version of git-annex](https://git-annex.branchable.com/install). Afterwards, install the latest version of datalad-metalad from [PyPi](https://pypi-hypernode.com/project/datalad-metalad). It is recommended to use a dedicated [virtualenv](https://virtualenv.pypa.io):

# create and enter a new virtual environment (optional) virtualenv –system-site-packages –python=python3 ~/env/datalad . ~/env/datalad/bin/activate

# install from PyPi pip install datalad_metalad

## Support

For general information on how to use or contribute to DataLad (and this extension), please see the [DataLad website](http://datalad.org) or the [main GitHub project page](http://datalad.org). The documentation is found here: http://docs.datalad.org/projects/metalad

All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/datalad/datalad-metalad/issues

If you have a problem or would like to ask a question about how to use DataLad, please [submit a question to NeuroStars.org](https://neurostars.org/tags/datalad) with a datalad tag. NeuroStars.org is a platform similar to StackOverflow but dedicated to neuroinformatics.

All previous DataLad questions are available here: http://neurostars.org/tags/datalad/

## Acknowledgements

DataLad development is supported by a US-German collaboration in computational neuroscience (CRCNS) project “DataGit: converging catalogues, warehouses, and deployment logistics into a federated ‘data distribution’” (Halchenko/Hanke), co-funded by the US National Science Foundation (NSF 1429999) and the German Federal Ministry of Education and Research (BMBF 01GQ1411). Additional support is provided by the German federal state of Saxony-Anhalt and the European Regional Development Fund (ERDF), Project: Center for Behavioral Brain Sciences, Imaging Platform. This work is further facilitated by the ReproNim project (NIH 1P41EB019936-01A1).