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dc.rights.licenseopenen_US
dc.contributor.authorBAUERMEISTER, Sarah
dc.contributor.authorPHATAK, Mukta
dc.contributor.authorSPARKS, Kelly
dc.contributor.authorSARGENT, Lana
dc.contributor.authorGRISWOLD, Michael
dc.contributor.authorMCHUGH, Caitlin
dc.contributor.authorNALLS, Mike
dc.contributor.authorYOUNG, Simon
dc.contributor.authorBAUERMEISTER, Joshua
dc.contributor.authorELLIOTT, Paul
dc.contributor.authorSTEPTOE, Andrew
dc.contributor.authorPORTEOUS, David
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDUFOUIL, Carole
dc.contributor.authorGALLACHER, John
dc.date.accessioned2023-06-26T07:55:01Z
dc.date.available2023-06-26T07:55:01Z
dc.date.issued2023-04-26
dc.identifier.issn1573-7284 (Electronic) 0393-2990 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/182812
dc.description.abstractEnData discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enData harmonisation
dc.subject.enCohort
dc.subject.enData visualisation
dc.subject.enC-surv data model
dc.subject.enData discovery
dc.subject.enDatasets
dc.title.enEvaluating the harmonisation potential of diverse cohort datasets
dc.title.alternativeEur J Epidemiolen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s10654-023-00997-3en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed37099244en_US
bordeaux.journalEuropean Journal of Epidemiologyen_US
bordeaux.page605-615en_US
bordeaux.volume38en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamPHARES_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-04140812
hal.version1
hal.date.transferred2023-06-26T07:55:27Z
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
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