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000894266 037__ $$aFZJ-2021-03141
000894266 1001_ $$0P:(DE-Juel1)180365$$aKöhler, Cristiano$$b0$$eCorresponding author$$ufzj
000894266 1112_ $$aINCF Neuroinformatics Assembly 2021$$conline$$wonline
000894266 245__ $$aFAIRification of electrophysiology data analysis: provenance capture in the Elephant toolbox
000894266 260__ $$c2021
000894266 3367_ $$033$$2EndNote$$aConference Paper
000894266 3367_ $$2DataCite$$aOther
000894266 3367_ $$2BibTeX$$aINPROCEEDINGS
000894266 3367_ $$2ORCID$$aLECTURE_SPEECH
000894266 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1628514494_30837$$xOther
000894266 3367_ $$2DINI$$aOther
000894266 500__ $$aContribution to the Human Brain Project Booth at the INCF Neuroinformatics Assembly 2021
000894266 520__ $$aThe analysis of electrophysiology data typically comprises multiple steps. These often consist of several scripts executed in a specific temporal order, which take different parameter sets and use distinct data files. As the researcher adjusts the individual analysis steps to accommodate new hypotheses or additional data, the resulting workflows may become increasingly complex, and undergo frequent changes. Although it is possible to use workflow management systems to organize the execution of the scripts and capture provenance information at the level of the script (i.e., which script file was executed, and in which environment?) and data file (i.e., which input and output files were supplied to that script), the resulting provenance track does not automatically provide details about the actual analysis carried out inside each script. Therefore, the final analysis results can only be understood by source code inspection or reliance in any accompanying documentation. We focus on two open-source tools for the analysis of electrophysiology data developed in EBRAINS. The Neo (RRID:SCR_000634) framework provides an object model to standardize neural activity data acquired from distinct sources. Elephant (RRID:SCR_003833) is a Python toolbox that provides several functions for the analysis of electrophysiology data. We set to improve these tools by implementing a data model that captures detailed provenance information and by representing the analysis results in a systematic and formalized manner. Ultimately, these developments aim to improve reproducibility, interoperability, findability, and re-use of analysis results.
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000894266 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x5
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000894266 9130_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x1
000894266 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000894266 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
000894266 9141_ $$y2021
000894266 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000894266 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000894266 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
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