Integrating automated metadata handling into the laboratory workflow.
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1
Ludwig-Maximilians-Universität München), Germany
Metadata handling belongs to our scientific life as much as handling the data itself. We take notes about experimental conditions, the experimental subject, look up what the stimulus parameters were etc. Proper data annotation is crucial for data analysis, data management, reproducibility of scientific results. If the data is to be shared with other scientists appropriate annotation becomes even more important. Many metadata are known to the recording and analysis tools. Our aim is to incorporate annotation capability into these tools and thus to automatize the annotation process as far as possible.
Here we show how metadata handling can be included into the researcher's workflow using odML (open metadata markup language, www.g-node.org/odml). The odML is a rather simple and flexible hierarchical format of extended key-value pairs, enabling to enter any metadata necessary. Interoperability can be achieved by using specified terminologies
(see www.g-node.org/odml/terminologies). We present the odML API and tool suite for data annotation supporting the odML format and terminologies. Furthermore we exemplify how data annotation can be incorporated into the neurophysiologists tool chain. Beginning at the time of recording the tool for data recording (relacs; www.relacs.net) writes the metadata it knows to a file along with the recorded data. This comprises for example information about stimulus settings, hardware configurations etc. During data analysis (e.g. using Matlab) further information about the analyses performed, or characteristics of the recorded cell that is extracted from the responses is added. Finally, the data is stored and needs to be kept available. Our data management/data sharing tools (LabLog, http://lablog.sourceforge.net ; G-Node portal, www.portal.g-node.org) help to keep track of the recorded data.
This way the whole workflow can be traced from the final results back to the raw data. All information of both the original data and the analysis tools are kept together with the results thus increasing reproducibility. Eventually this will allow us to share data with other scientists in a very convenient way.
Supported by the German Federal Ministry of Education and Research (BMBF grants 01GQ0801 and 01GQ0802)
Keywords:
Electrophysiology,
integrating metadata,
analysis tools,
data sharing,
computational neuroscience
Conference:
5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012.
Presentation Type:
Poster
Topic:
Neuroinformatics
Citation:
Grewe
J,
Stoewer
A,
Sobolev
A,
Fritsch
H,
Wachtler
T and
Benda
J
(2014). Integrating automated metadata handling into the laboratory workflow..
Front. Neuroinform.
Conference Abstract:
5th INCF Congress of Neuroinformatics.
doi: 10.3389/conf.fninf.2014.08.00080
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Received:
21 Mar 2013;
Published Online:
27 Feb 2014.
*
Correspondence:
Dr. Jan Grewe, Ludwig-Maximilians-Universität München), Planegg-Martinsried, Germany, jan.grewe@uni-tuebingen.de