This title appears in the Scientific Report :
2016
An illustrative approach to metadata management for electrophysiological experiments
An illustrative approach to metadata management for electrophysiological experiments
The generation and accumulation of data is a common feature in all scientific endeavors. These primary data, and subsequent data resulting from post-processing steps, should always be accompanied by information about the origin of the data. This information is typically recorded as metadata and is e...
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Personal Name(s): | Sprenger, Julia (Corresponding author) |
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Canova, Carlos / Pick, Jana / Zehl, Lyuba / Denker, Michael / Grün, Sonja | |
Contributing Institute: |
JARA-BRAIN; JARA-BRAIN Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Imprint: |
2016
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Conference: | Neurobiology Doctoral Students Workshop 2016, Bielefeld (Germany), 2016-08-03 - 2016-08-05 |
Document Type: |
Conference Presentation |
Research Program: |
Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination Optogenetische Analyse der für kognitive Fähigkeiten zuständigen präfrontal-hippokampalen Netzwerke in der Entwicklung The Human Brain Project Supercomputing and Modelling for the Human Brain Connectivity and Activity |
Publikationsportal JuSER |
The generation and accumulation of data is a common feature in all scientific endeavors. These primary data, and subsequent data resulting from post-processing steps, should always be accompanied by information about the origin of the data. This information is typically recorded as metadata and is essential for reproducible data analysis, for facilitation of the communication between members of a project, and ultimately to support later accessibility of recorded data. In neuroscience, and in particular experimental neurophysiology, the development of a common approach to metadata management is still an ongoing effort [1].We present here an easy-to-use approach for metadata generation for neuroscientific data in the context of two electrophysiological use cases and illustrate how we can apply a common metadata framework to store metadata records. Our approach is based on odML (open metadata Markup Language [2]), i.e. a hierarchical XML-based metadata format which was designed to represent complex metadata collections. However, both use cases revealed that manually generating and filling an odML metadata document is not sufficiently solved without extensive programming knowledge, with the consequence of effectively preventing the use of the odML framework. To overcome this challenge, we developed odML-tables, a software solution that emerged from this observation and is designed to bridge the gap between classical hierarchical representations of odML and a flat, tabular counterpart, which is well-suited for manual entry using common graphical software tools (Microsoft Excel, LibreOffice Calc). We show how odML-tables complements a sustainable workflow for metadata management in the two use cases.References:[1] Zehl, L., Jaillet, F., Stoewer, A., Grewe, J., Sobolev, A., Wachtler, T., Brochier, T.,Riehle, A., Denker, M., Grün, S.: Handling Metadata in a Neurophysiology LaboratoryFrontier in Neuroinformatics (under revision)[2] Grewe, J., Wachtler, T., & Benda, J. (2011). A Bottom-up Approach to DataAnnotation in Neurophysiology. Frontiers in Neuroinformatics, 5, 16. |