A linear, binary classifier to predict bacterial biofilm formation on polyacrylates
dc.contributor.author | Williams, Phil | |
dc.contributor.other | Alexander, Morgan R | |
dc.contributor.other | Laughton, Charles | |
dc.contributor.other | Hook, Andrew L | |
dc.contributor.other | Figueredo, Grazziela P. | |
dc.contributor.other | Williams, Paul | |
dc.date.accessioned | 2022-12-21T13:40:12Z | |
dc.date.available | 2022-12-21T13:40:12Z | |
dc.date.issued | 2022-12-21 | |
dc.identifier.uri | https://rdmc.nottingham.ac.uk/handle/internal/10008 | |
dc.description.abstract | Research data and Python code to undertake machine learning to predict bacterial attachment to polyacrylates. | en_UK |
dc.language.iso | en | en_UK |
dc.publisher | The University of Nottingham | en_UK |
dc.rights | CC-BY | * |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject.lcsh | Biofilms | en_UK |
dc.subject.lcsh | Attachment mechanisms (Biology) | en_UK |
dc.subject.lcsh | Polymers | en_UK |
dc.title | A linear, binary classifier to predict bacterial biofilm formation on polyacrylates | en_UK |
dc.identifier.doi | http://doi.org/10.17639/nott.7256 | |
dc.subject.free | ToF-SIMS; Python code; machine learning; bacterial attachment | en_UK |
dc.subject.jacs | Biological Sciences::Microbiology::Applied microbiology | en_UK |
dc.subject.lc | Q Science::QR Microbiology::QR100 Microbial ecology | en_UK |
uon.division | University of Nottingham, UK Campus::Faculty of Science::School of Pharmacy | en_UK |
uon.funder.controlled | Engineering & Physical Sciences Research Council | en_UK |
uon.datatype | Python source code, Microsoft Excel datafile | en_UK |
uon.grant | EP/N006615/1 | en_UK |
uon.collectionmethod | ToF-SIMs | en_UK |
Files in this item
This item appears in the following Collection(s)
-
Public Research Data
A collection of research data, held in this repository, that is publicly available, except where individual embargoes apply