2,031 to 2,040 of 3,457 Results
Jan 28, 2022 - Psühholoogia andmed
Kask, Annika; Põldver, Nele; Kreegipuu, Kairi, 2022, "Eyebrow angle and gaze direction as modulators of the emotional value of schematic faces: a visual mismatch response (vMMR) study (data)", https://doi.org/10.23673/RE-308, DATADOI, V1
The aim of the present study was to examine if systematic manipulating with eyebrows and gaze directions expected to change the emotional value of schematic faces has an influence on the subjective and automatic discrimination of schematic faces and if this discrimination is influenced by the subjects’ emotional state. Participants (33 volunteers,... |
MS Excel Spreadsheet - 322.9 KB -
MD5: 1828b61f95849ba7487419a50b6447f3
The average amplitudes of the vMMN difference wave in ten ~20ms intervals for 6 posterior-occipital electrodes in response to angry and neutral schematic faces with eyebrow angle and gaze direction modifications.
Fail on embargo all kuni 2022-06-01. File is under embargo until 2022-06-01. |
MS Excel Spreadsheet - 23.9 KB -
MD5: a403d8980be13125d58799af0f4a7aa4
Subjective evaluations to angry and neutral deviant with eyebrow angle and gaze direction modifications, also two standard stimuli.
Fail on embargo all kuni 2022-06-01. File is under embargo until 2022-06-01. |
Jan 24, 2022 - Arvutiteaduse andmed
Ali, Mohammed A. S.; Hollo, Kaspar; Laasfeld, Tõnis; Torp, Jane; Tahk, Maris-Johanna; Rinken, Ago; Palo, Kaupo; Parts, Leopold; Fishman, Dmytro, 2022, "ArtSeg-CHO-M4R: artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells.", https://doi.org/10.23673/RE-307, DATADOI, V1
The "ArtSeg-CHO-M4R, artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells" dataset contains microscopy images along with the ground truth binary masks for artifact segmentation from brightfield images. The dataset consists of three main directories for train, validation, and test splits. The image... |
Jan 24, 2022 -
ArtSeg-CHO-M4R: artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells.
ZIP Archive - 353.6 MB -
MD5: 2bfba2ed31260663f37d738a2c433683
Test split of the ArtSeg-CHO-M4R dataset |
Jan 24, 2022 -
ArtSeg-CHO-M4R: artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells.
ZIP Archive - 868.6 MB -
MD5: 5dc5f1720ff4b3c12754aaf4946e2cf7
Training split of the ArtSeg-CHO-M4R dataset |
Jan 24, 2022 -
ArtSeg-CHO-M4R: artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells.
ZIP Archive - 246.4 MB -
MD5: f6101014b4754a6bfa8fa68cca21726d
Validation split of the ArtSeg-CHO-M4R dataset |
Jan 24, 2022 -
ArtSeg-CHO-M4R: artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells.
HTML - 700 B -
MD5: 79da7ba44461b593b4f6afc1f09853c4
2026. aasta migratsiooni käigus varasemast DataDOI süsteemist üle kantud kasutusstatistika kajastab tegevust eelmises DSpace-põhises süsteemis ega näita Dataverse’i uusi kasutusandmeid.
Usage statistics carried over from the previous DataDOI system as part of the 2026 migration reflect activity in the former DSpace-based system and do not represent... |
Jan 24, 2022 -
ArtSeg-CHO-M4R: artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells.
Plain Text - 3.8 KB -
MD5: f629ed3253c97c7b94ee700396f04527
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Jan 19, 2022 - Keemia instituudi andmed
Tahk, Maris-Johanna; Torp, Jane; Ali, Mohammed A.S.; Fishman, Dmytro; Parts, Leopold; Grätz, Lukas; Müller, Christoph; Keller, Max; Veikšina, Santa; Laasfeld, Tõnis; Rinken, Ago, 2022, "UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells", https://doi.org/10.23673/RE-306, DATADOI, V1
The "UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells" dataset contains the raw microscopy images of the experiments along with images processed using the random forest algorithm and U-Net3 based deep convolutional neural networks for cell segmentation from bright-field images. The dataset contains experime... |
