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dc.contributor.authorWilliams, Phil
dc.contributor.otherAlexander, Morgan R
dc.contributor.otherLaughton, Charles
dc.contributor.otherHook, Andrew L
dc.contributor.otherFigueredo, Grazziela P.
dc.contributor.otherWilliams, Paul
dc.date.accessioned2022-12-21T13:40:12Z
dc.date.available2022-12-21T13:40:12Z
dc.date.issued2022-12-21
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/10008
dc.description.abstractResearch data and Python code to undertake machine learning to predict bacterial attachment to polyacrylates.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.lcshBiofilmsen_UK
dc.subject.lcshAttachment mechanisms (Biology)en_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.7256
dc.subject.freeToF-SIMS; Python code; machine learning; bacterial attachmenten_UK
dc.subject.jacsBiological Sciences::Microbiology::Applied microbiologyen_UK
dc.subject.lcQ Science::QR Microbiology::QR100 Microbial ecologyen_UK
uon.divisionUniversity of Nottingham, UK Campus::Faculty of Science::School of Pharmacyen_UK
uon.funder.controlledEngineering & Physical Sciences Research Councilen_UK
uon.datatypePython source code, Microsoft Excel datafileen_UK
uon.grantEP/N006615/1en_UK
uon.collectionmethodToF-SIMsen_UK


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