001     859032
005     20210130000147.0
024 7 _ |a 10.1093/database/bay124
|2 doi
024 7 _ |a 2128/22389
|2 Handle
024 7 _ |a pmid:30576483
|2 pmid
024 7 _ |a WOS:000453806800003
|2 WOS
024 7 _ |a altmetric:59783030
|2 altmetric
037 _ _ |a FZJ-2019-00002
082 _ _ |a 570
100 1 _ |a Pallast, N.
|0 P:(DE-HGF)0
|b 0
245 _ _ |a Cloud-based relational database for multimodal animal data
260 _ _ |a Oxford
|c 2018
|b Oxford University Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1561624214_25932
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Pre-clinical research builds on a large variety of in vivo and ex vivo tools such as non-invasive imaging, microscopy, and analysis of gene expression. To work efficiently with multimodal data and correlate results across scales, it is of particular importance to have easy access to all data points from different specimen, e.g. the magnetic resonance imaging (MRI) data from different time points, and the post-mortem histology. That requires an efficient data management, which is customizable and designed to relate all applied methods, raw data and analyses to one specific animal. Despite increasing demands to handle such complex data, most pre-clinical labs have not yet established such an electronic database. Here, we present a novel cloud-based relational database for multimodal animal data, which operates on commercial software. We have implemented data fields for various pre-clinical features such as MRI, histology and behaviour. Automated procedures replace manual and recurrent calculations. Pre-set plotting and printing features provide efficient analysis and documentation. The database template is useful for all labs working with laboratory animals and the adaption to specific research projects requires no prior scripting expertise. The database works operating-system independent through the web browser and allows multiple users to work simultaneously. The data entry is monitored and restricted for particular tests according to the user management in order to keep for example users during the experiment blinded for the experimental group. The database improves data accessibility, standardization of data recording and data handling efficiency in pre-clinical research.
536 _ _ |a 572 - (Dys-)function and Plasticity (POF3-572)
|0 G:(DE-HGF)POF3-572
|c POF3-572
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Wieters, F.
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Nill, M.
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Fink, G. R.
|0 P:(DE-Juel1)131720
|b 3
700 1 _ |a Aswendt, M.
|0 P:(DE-HGF)0
|b 4
|e Corresponding author
773 _ _ |a 10.1093/database/bay124
|0 PERI:(DE-600)2496706-3
|p bay124
|t Database
|v 2018
|y 2018
|x 1758-0463
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/859032/files/bay124.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/859032/files/bay124.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:859032
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)131720
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-572
|2 G:(DE-HGF)POF3-500
|v (Dys-)function and Plasticity
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2019
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b DATABASE-OXFORD : 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-3-20090406
|k INM-3
|l Kognitive Neurowissenschaften
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-3-20090406
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21