NCI Spectal data for PLOS One 'Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy' publication

515 11 1762 2 10553 document 1762 Raw_Spectal_Data.xlsx application/vnd.openxmlformats-officedocument.spreadsheetml.sheet cc377a8bd8aaf943f4046ccf138f8ac0 MD5 110808 2020-06-30 16:39:36 http://edata.bham.ac.uk/515/1/Raw_Spectal_Data.xlsx 515 1 1 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet spreadsheet public cc_by_4

Raw_Spectal_Data.xlsx

data archive 213 disk0/00/00/05/15 2020-07-09 09:59:51 2021-07-12 16:00:16 2020-07-09 09:59:51 data_collection show Mellors Ben BXM678@bham.ac.uk Dehghani Hamid h.dehghani@cs.bham.ac.uk NCI Spectal data for PLOS One 'Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy' publication 10col_ephy Spectral Data The raw spectral data for repositry submission as part of the PLOS One paper titled 'Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy'. The raw spectral reflectance data from the NCI measurements of both healthy and treated cell culture samples are shown, along with each wavelength of illumination 2020-07-09 published University of Birmingham https://doi.org/10.25500/edata.bham.00000515 Dataset research-data@contacts.bham.ac.uk School of Chemistry Physical Science for Health Doctoral Training Centre School of Computer Science epsrc EP/L016346/1 yes yes yes TRUE Supervisor Dehghani Hamid h.dehghani@cs.bham.ac.uk