UT Realia: Recent submissions
Now showing items 91-95 of 138
-
UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth
(University of Tartu, Institute of Chemistry, Chair of Bioorganic chemistry, 2022-01-13)The "UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth" dataset contains the raw microscopy images, corresponding bright-field Z-stack based contrast-enhanced ... -
UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images
(University of Tartu, Institute of Chemistry, Chair of Bioorganic chemistry, 2022-01-13)The "UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images" dataset contains the machine learning model files for CHO-K1 cell segmentation from fluorescence ... -
UT-GPCR003 Fluorescence anisotropy and microscopy measurements experimental metadata of ligand binding to M4 muscarinic receptors
(University of Tartu, Institute of Chemistry, Chair of Bioorganic chemistry, 2022-01-13)The "UT-GPCR003 Fluorescence anisotropy and microscopy measurements experimental metadata of ligand binding to M4 muscarinic receptors" dataset contains the raw fluorescence anisotropy data for ligand binding to M4 muscarinic ... -
Antimicrobial activity of commercial photocatalytic SaniTise™ window glass
(University of Tartu, 2022-01-04)The dataset represents collection of raw data for different analyses used in the manuscript "Antimicrobial activity of commercial photocatalytic SaniTise™ window glass" submitted to MDPI journal Catalysts at 30.12.2021. -
HPC Cloud traces for better cloud service reliability
(Semantic Technology Institute (STI) Innsbruck, Department of Computer Science, University of Innsbruck, Austria; Institute of Computer Science, University of Tartu, Estonia; School of Computer and Information Sciences, University of Hyderabad, India, 2021)This data is in support of the research on "A combined system metrics approach to cloud service reliability using artificial intelligence" (doi: 10.20944/preprints202111.0548.v1)