
About
Open metadata markup language (odML) is a format for storing metadata in an organised human- and machine-readable way. It does not constrain the metadata content, while providing a common schema (with implementations in XML, JSON, YAML) to integrate metadata from various sources. In addition, odML facilitates and encourages standardization by providing terminologies for metadata.
Learn more: https://g-node.github.io/python-odml/
Links
Training Resources odML webpageSupporting software
APIs
- odml python library. Python library for reading and writing odml files https://github.com/g-node/python-odml
- Java-odml-lib Java implementation of the data model https://github.com/g-node/odml-java-lib
- matlab-odml Matlab interface for odml files https://github.com/g-node/matlab-odm
Neurophysiology terminologies
Templates
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odML documents that can be re-used when collecting the same kind of information over the course of multiple identical experiments templates github repository
Viewer/Editor
- odml-ui: Graphical editor https://github.com/g-node/odml-ui
- odmlTables Spreadsheet interface (by INM-6 FZ Jülich) for odml files https://github.com/INM-6/python-odmltables
Additional URL
Use odML if you would like:
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An open, XML-based format to collect, store, and share metadata that is both machine- and human-readable
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A format for neurophysiological data with defined terminologies
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Flexible format. The basic idea of the odML approach is to combine a rather general data model with domain specific terminologies. Independence of format and content offers a maximum of flexibility. The terminologies introduce the basis for standardization that, however, can be ignored or extended when necessary