Frontiers in Neuroinformatics (Jul 2016)
Handling Metadata in a Neurophysiology Laboratory
Abstract
To date, non-reproducibility of neurophysiological research is a matterof intense discussion in the scientific community. A crucial componentto enhance reproducibility is to comprehensively collect and storemetadata, that is all information about the experiment, the data,and the applied preprocessing steps on the data, such that they canbe accessed and shared in a consistent and simple manner. However,the complexity of experiments, the highly specialized analysis workflowsand a lack of knowledge on how to make use of supporting softwaretools often overburden researchers to perform such a detailed documentation.For this reason, the collected metadata are often incomplete, incomprehensiblefor outsiders or ambiguous. Based on our research experience in dealingwith diverse datasets, we here provide conceptual and technical guidanceto overcome the challenges associated with the collection, organization,and storage of metadata in a neurophysiology laboratory. Through theconcrete example of managing the metadata of a complex experimentthat yields multi-channel recordings from monkeys performing a behavioralmotor task, we practically demonstrate the implementation of theseapproaches and solutions with the intention that they may be generalizedto a specific project at hand. Moreover, we detail five use casesthat demonstrate the resulting benefits of constructing a well-organizedmetadata collection when processing or analyzing the recorded data,in particular when these are shared between laboratories in a modernscientific collaboration. Finally, we suggest an adaptable workflowto accumulate, structure and store metadata from different sourcesusing, by way of example, the odML metadata framework.
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