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odML (open metadata Markup Language) is an XML based file format, proposed by [Grewe et al. (2011) Front Neuroinform 5:16], in order to provide metadata in an organized, 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.

What are metadata and why are they needed?

Metadata are data about data. They describe the conditions under which the actual raw-data of an experimental study were acquired. The organization of such metadata and their accessibility may sound like a trivial task, and most laboratories developed their home-made solutions to keep track of their metadata. Most of these solutions, however, break down if data and metadata need to be shared within a collaboration, since implicit knowledge of what is important and how it is organized is often underestimated.

While maintaining the relation to the actual raw-data, odML can help to collect all metadata which are usually distributed over several files and formats, and to store them combined which facilitates sharing data and metadata.

Key features of odML

• open, XML based language, to collect, store and share metadata
• Machine- and human-readable
• Python-odML library
• An interactive odML-Editor which can be found at this github repository.

Documentation and tutorials for the python-odml implementation can be found here. In this tutorial we will illustrate the conceptual design of odML and show hands-on how you can generate your own odML metadata collection. In addition, we demonstrate the advantages of using odML to screen large numbers of data sets according to selection criteria relevant for subsequent analyses.

python-odml also features an export to the semantic web format RDF.