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000155650 037__ $$aFZJ-2014-04707
000155650 041__ $$aEnglish
000155650 082__ $$a610
000155650 1001_ $$0P:(DE-Juel1)145394$$aZehl, Lyuba$$b0$$eCorresponding Author
000155650 1112_ $$aINM Retreat 2014$$cJuelich$$d2014-07-01 - 2014-07-02$$wGermany
000155650 245__ $$aOrganizing Metadata of Complex Neurophysiological Experiments
000155650 260__ $$c2014
000155650 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1409721280_30763
000155650 3367_ $$033$$2EndNote$$aConference Paper
000155650 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000155650 3367_ $$2ORCID$$aOTHER
000155650 3367_ $$2DRIVER$$aconferenceObject
000155650 3367_ $$2BibTeX$$aINPROCEEDINGS
000155650 500__ $$aAcknowledgements: Supported by the Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB), Human Brain Project (HBP, EU grant 604102), G-Node (BMBF Grant 01GQ1302), BrainScaleS (EU Grant 269912), ANR-GRASP. References: [1] Grewe J, Wachtler T, and Benda J (2011) A bottom-up approach to data annotation in neurophysiology. Front. Neuroinform. 5:16, [2] Riehle A, Wirtssohn S, Grün S, and Brochier T (2013) Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements. Front. Neural Circuits 7:48
000155650 520__ $$aTechnological progress in neuroscience allows recording from tens to hundreds of neurons simultaneously, both in vitro and in vivo, using various recording techniques (e.g., multi-electrode recordings) and stimulation methods (e.g., optogenetics). In addition, recordings can be performed in parallel from multiple brain areas, under more or less natural conditions in (almost) freely behaving animals. Consequently, electrophysiological experiments become increasingly complex. Moreover, to disentangle the relationship to behavior, it is necessary to document animal training, experimental procedures, and details of the setup along with recorded neuronal and behavioral data. Given these various sources of complexity within an experiment, the availability of such information about the experiment, commonly referred to as metadata, is of extreme relevance for reproducible data analysis and correct interpretation of results. Typically, experimenters have developed their own personal procedure to document their experiment, 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. In particular if collaborating groups have different scientific backgrounds, implicit knowledge is often not communicated. In order to perform interpretable analysis of the data, each data set should therefore clearly link to metadata annotations about experimental conditions such as the performed 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-readable way, an XML based file format, odML (open metadata Markup Language), was proposed [1]. Here, we will demonstrate the usefulness of standardized metadata collections for handling the 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 of an odML metadata structure and provide a practical introduction on how to generate an odML file. In addition, we offer odML templates to facilitate the usage of odML across different laboratories and experimental contexts. We demonstrate hands-on the advantages of using odML to screen large numbers of data sets according to selection criteria (e.g., behavioral performance) relevant for subsequent analyses (see companion posters by Denker et al.). Well organized metadata management is a key component to guarantee reproducibility of experiments and to track provenance of performed analyses.
000155650 536__ $$0G:(DE-HGF)POF2-331$$a331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331)$$cPOF2-331$$fPOF II$$x0
000155650 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x1
000155650 536__ $$0G:(EU-Grant)604102$$aHBP - The Human Brain Project (604102)$$c604102$$fFP7-ICT-2013-FET-F$$x2
000155650 536__ $$0G:(EU-Grant)269921$$aBRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921)$$c269921$$fFP7-ICT-2009-6$$x3
000155650 536__ $$0G:(DE-HGF)POF2-89571$$a89571 - Connectivity and Activity (POF2-89571)$$cPOF2-89571$$fPOF II T$$x4
000155650 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b1
000155650 7001_ $$0P:(DE-HGF)0$$aStoewer, Adrian$$b2
000155650 7001_ $$0P:(DE-HGF)0$$aJaillet, Florent$$b3
000155650 7001_ $$0P:(DE-HGF)0$$aBrochier, Thomas$$b4
000155650 7001_ $$0P:(DE-HGF)0$$aRiehle, Alexa$$b5
000155650 7001_ $$0P:(DE-HGF)0$$aWachtler, Thomas$$b6
000155650 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b7
000155650 773__ $$0PERI:(DE-600)2452979-5$$x1662-5196$$y2014
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000155650 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145394$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000155650 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144807$$aForschungszentrum Jülich GmbH$$b1$$kFZJ
000155650 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich GmbH$$b7$$kFZJ
000155650 9132_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x0
000155650 9131_ $$0G:(DE-HGF)POF2-331$$1G:(DE-HGF)POF2-330$$2G:(DE-HGF)POF2-300$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lFunktion und Dysfunktion des Nervensystems$$vSignalling Pathways and Mechanisms in the Nervous System$$x0
000155650 9131_ $$0G:(DE-HGF)POF2-89571$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vConnectivity and Activity$$x1
000155650 9141_ $$y2014
000155650 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000155650 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000155650 980__ $$aabstract
000155650 980__ $$aVDB
000155650 980__ $$aI:(DE-Juel1)INM-6-20090406
000155650 980__ $$aI:(DE-Juel1)IAS-6-20130828
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000155650 981__ $$aI:(DE-Juel1)IAS-6-20130828
000155650 981__ $$aI:(DE-Juel1)IAS-6-20130828