001     892671
005     20240313103132.0
024 7 _ |2 doi
|a 10.1515/nf-2020-0041
024 7 _ |2 ISSN
|a 0947-0875
024 7 _ |2 ISSN
|a 1868-856X
024 7 _ |2 ISSN
|a 2363-7013
024 7 _ |2 Handle
|a 2128/28428
037 _ _ |a FZJ-2021-02257
041 _ _ |a English
082 _ _ |a 610
100 1 _ |0 P:(DE-Juel1)144807
|a Denker, Michael
|b 0
245 _ _ |a Reproducibility and efficiency in handling complex neurophysiological data
260 _ _ |a Berlin
|b De Gruyter
|c 2021
336 7 _ |2 DRIVER
|a article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
|b journal
|m journal
|s 1628173422_19631
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
336 7 _ |0 0
|2 EndNote
|a Journal Article
520 _ _ |a Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.
536 _ _ |0 G:(DE-HGF)POF4-5235
|a 5235 - Digitization of Neuroscience and User-Community Building (POF4-523)
|c POF4-523
|f POF IV
|x 0
536 _ _ |0 G:(DE-HGF)POF4-5231
|a 5231 - Neuroscientific Foundations (POF4-523)
|c POF4-523
|f POF IV
|x 1
536 _ _ |0 G:(DE-Juel1)HDS-LEE-20190612
|a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)
|c HDS-LEE-20190612
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |0 P:(DE-Juel1)144168
|a Grün, Sonja
|b 1
700 1 _ |0 P:(DE-Juel1)171573
|a Wachtler, Thomas
|b 2
700 1 _ |0 0000-0001-6593-2800
|a Scherberger, Hansjörg
|b 3
|e Corresponding author
773 _ _ |0 PERI:(DE-600)2855056-0
|a 10.1515/nf-2020-0041
|g Vol. 0, no. 0, p. 000010151520200041
|n 1
|p 27- 34
|t Neuroforum
|v 27
|x 2363-7013
|y 2021
856 4 _ |u https://juser.fz-juelich.de/record/892671/files/10.1515_nf-2020-0041.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:892671
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)144807
|a Forschungszentrum Jülich
|b 0
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)144168
|a Forschungszentrum Jülich
|b 1
|k FZJ
913 1 _ |0 G:(DE-HGF)POF4-523
|1 G:(DE-HGF)POF4-520
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5235
|a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|v Neuromorphic Computing and Network Dynamics
|x 0
913 1 _ |0 G:(DE-HGF)POF4-523
|1 G:(DE-HGF)POF4-520
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5231
|a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|v Neuromorphic Computing and Network Dynamics
|x 1
913 0 _ |0 G:(DE-HGF)POF3-574
|1 G:(DE-HGF)POF3-570
|2 G:(DE-HGF)POF3-500
|3 G:(DE-HGF)POF3
|4 G:(DE-HGF)POF
|a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|v Theory, modelling and simulation
|x 0
913 0 _ |0 G:(DE-HGF)POF3-571
|1 G:(DE-HGF)POF3-570
|2 G:(DE-HGF)POF3-500
|3 G:(DE-HGF)POF3
|4 G:(DE-HGF)POF
|a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|v Connectivity and Activity
|x 1
914 1 _ |y 2021
915 _ _ |0 StatID:(DE-HGF)0310
|2 StatID
|a DBCoverage
|b NCBI Molecular Biology Database
|d 2020-09-02
915 _ _ |0 StatID:(DE-HGF)0200
|2 StatID
|a DBCoverage
|b SCOPUS
|d 2020-09-02
915 _ _ |0 StatID:(DE-HGF)0510
|2 StatID
|a OpenAccess
915 _ _ |0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
|a Creative Commons Attribution CC BY 4.0
920 _ _ |l no
920 1 _ |0 I:(DE-Juel1)INM-6-20090406
|k INM-6
|l Computational and Systems Neuroscience
|x 0
920 1 _ |0 I:(DE-Juel1)INM-10-20170113
|k INM-10
|l Jara-Institut Brain structure-function relationships
|x 1
920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
|k IAS-6
|l Theoretical Neuroscience
|x 2
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-6-20090406
980 _ _ |a I:(DE-Juel1)INM-10-20170113
980 _ _ |a I:(DE-Juel1)IAS-6-20130828
980 _ _ |a OPENSCIENCE
981 _ _ |a I:(DE-Juel1)IAS-6-20130828


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21