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dc.contributor.authorWilliams, Phil
dc.contributor.authorAlexander, Morgan R.
dc.contributor.otherLaughton, Charles
dc.date.accessioned2022-09-08T07:55:19Z
dc.date.available2022-09-08T07:55:19Z
dc.date.issued2022-09-08
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/9556
dc.description.abstractDatafiles, in Microsoft Excel format, associated with the manuscript "A linear, binary classifier to predict bacterial biofilm formation on polyacrylates" These files contain ToF-SIMS ion peaks and the bacterial attachment data.en_UK
dc.language.isoenen_UK
dc.publisherThe University of Nottinghamen_UK
dc.rightsCC-BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.lcshTime-of-flight mass spectrometryen_UK
dc.subject.lcshBiofilmsen_UK
dc.subject.lcshBiomedical materialsen_UK
dc.subject.lcshPolymersen_UK
dc.titleA linear, binary classifier to predict bacterial biofilm formation on polyacrylatesen_UK
dc.identifier.doihttp://doi.org/10.17639/nott.7235
dc.subject.freeToF-SIMS, bacterial attachment, machine learning, molecular descriptorsen_UK
dc.subject.jacsSubjects Allied to Medicineen_UK
dc.subject.jacsComputer Sciences::Artificial intelligence::Machine learning, Automated reasoningen_UK
dc.subject.jacsBiological Sciences::Microbiology::Bacteriologyen_UK
dc.subject.lcQ Science::QR Microbiology::QR 75 Bacteria. Cyanobacteriaen_UK
uon.divisionUniversity of Nottingham, UK Campus::Faculty of Science::School of Pharmacyen_UK
uon.funder.controlledEngineering & Physical Sciences Research Councilen_UK
uon.datatypeToF-SIMS ion peaks and bacterial attachment dataen_UK
uon.grantEP/N006615/1en_UK
uon.collectionmethodToF-SIMS and fluorescence microscopyen_UK
uon.preservation.rarelyaccessedtrue


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