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Technological progress in neuroscience allows recording from tens to hundreds of neuronssimultaneously, both in vitro and in vivo, using various recording techniques (e.g., multi-electrode recordings) and stimulation methods (e.g., optogenetics). In addition, recordingscan be performed in parallel from multiple brain areas, under more or less naturalconditions in (almost) freely behaving animals. Consequently, electrophysiologicalexperiments become increasingly complex. Moreover, to disentangle the relationship tobehavior, it is necessary to document animal training, experimental procedures, and detailsof the setup along with recorded neuronal and behavioral data. Considering this, availabilityof the information about the experimental conditions, commonly referred to as metadata, isof extreme relevance for reproducible data analysis and correct interpretation of results.Typically, experimenters have developed their own personal procedure to document theirexperiment, allowing at best other members of the lab to share data and metadata.However, at the latest when it comes to data sharing across labs, details may be missed. Inparticular if collaborating groups have different scientific backgrounds, implicit knowledge isoften not communicated. In order to perform interpretable analysis, each data set shouldtherefore clearly link to metadata annotations about experimental conditions such as theperformed task, quality of the data, or relevant preprocessing (e.g., spike sorting).In order to provide metadata in an organized, easily accessible, but also machine-readableway, an XML based file format, odML (open metadata Markup Language), was proposed [1].Here we will demonstrate the usefulness of standardized metadata collections for handlingthe data and their analysis in the context of a complex behavioral (reach to grasp)experiment with neuronal recordings from a large number of electrodes (Utah array)delivering massively parallel spike and LFP data [2]. We illustrate the conceptual design ofan odML metadata structure and provide a practical introduction on how to generate anodML file. In addition, we offer odML templates to facilitate the usage of odML acrossdifferent laboratories and experimental contexts. We demonstrate hands-on the advantagesof using odML to screen large numbers of data sets according to selection criteria (e.g.,behavioral performance) relevant for subsequent analyses (see companion posters byDenker et al. and Riehle et al.). Well organized metadata management is a key componentto guarantee reproducibility of experiments and to track provenance of performed analyses. |
