multielectrode_grasp_pd/code/python-odml/docs/index.md

odML

odML (open metadata Markup Language) is a file format for storing arbitrary metadata. The underlying data model offers a way to store metadata in a structured human- and machine-readable way. Well organized metadata management is a key component to guarantee reproducibility of experiments and to track provenance of performed analyses.

python-odml is the python library for reading and writing odml metadata files. It is a registered research resource with the RRID:SCR_001376 .

APIs

• odml python library. Python library for reading and writing odml files.
• java-odml-lib Java implementation of the data model.
• matlab-odml Matlab interface for odml files.

Viewer/Editor

• odml-ui: Graphical editor
• odmlTables Spreadsheet interface (by INM-6 FZ JÛlich) for odml files.

Terminologies

odML facilitates and encourages standardization by providing terminologies. An odml-file can be based on such a terminology. In that case one does not need to provide definitions since they are part of the linked terminology.

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Getting started

Installation

python-odml is most conveniently installed via pip.

pip install odml

Tutorial and examples

• We have assembled a set of tutorials.

Python convenience scripts

The Python installation features two convenience commandline scripts.

• odmlconversion: Converts odML files of previous file versions into the current one.
• odmltordf: Converts odML files to the supported RDF version of odML.

Both scripts provide detailed usage descriptions by adding the help flag to the command.

odmlconversion -h
odmltordf -h

Support

If you experience problems using odml feel free to join our IRC channel #gnode at FreeNode or write an email to dev@g-node.org. If you find a bug please report it using the project issue tracker.

License

This project is open source published under an MIT license-like see license file for details.

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Citing

If you use odml, it would be much appreciated if you would cite it in publications with its identifier RRID:SCR_001376 and/or the reference:

Grewe J., Wachtler T., and Benda J. (2011). A bottom-up approach to data annotation in neurophysiology. Front. Neuroinform. 5:16, doi:10.3389/fninf.2011.00016

Referenced By

• Dragly et al (2018) doi:10.3389/fninf.2018.000169
• Brochier et al (2018) doi:10.1038/sdata.2018.55
• Moucek et al (2017) doi:10.1038/sdata.2016.121
• Papez et al (2017) doi:10.3389/fninf.2017.00024
• Bigdely-Shamlo et al (2016) doi:10.3389/fninf.2016.00007
• Rübel et al (2016) doi:10.3389/fninf.2016.00048
• Wiener et al (2016) doi:10.1016/j.neuron.2016.10.037
• Zehl et al (2016) doi:10.3389/fninf.2016.00026
• Jayapandian et al (2015) doi:10.3389/fninf.2015.00004
• Jezek et al (2015) doi:10.3389/fninf.2015.00003
• Kocaturk et al (2015) doi:10.3389/fnbot.2015.00008
• Maccione et al (2015) doi:10.1016/j.brainresbull.2015.07.008
• Vanek et al (2015) doi:10.1109/Informatics.2015.7377849
• Garcia et al (2014) doi:10.3389/fninf.2014.00010
• Moucek et al (2014) doi:10.3389/fninf.2014.00020
• Sobolev et al (2014) doi:10.3389/fninf.2014.00015
• Cockfield et al (2013) doi:10.3389/fninf.2013.00020
• Papez et al (2013) doi:10.1109/BIBM.2013.6732554
• Bakker et al (2012) doi:10.3389/fninf.2012.00030