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121 to 130 of 12,885 Results
Turbulator_Average.csv
Dec 2, 2024 - Background Data for: UAV Icing: Aerodynamic Degradation Caused by Intercycle and Runback Ice Shapes on an RG-15 Airfoil
Comma Separated Values - 1.6 KB - MD5: 70fb0b637a6097ca06625818e77f6a66
Turbulator_StdDev.csv
Dec 2, 2024 - Background Data for: UAV Icing: Aerodynamic Degradation Caused by Intercycle and Runback Ice Shapes on an RG-15 Airfoil
Comma Separated Values - 1.0 KB - MD5: 081548ddc0df54eaba887ef6e7d5baa1
Xfoil.csv
Dec 2, 2024 - Background Data for: UAV Icing: Aerodynamic Degradation Caused by Intercycle and Runback Ice Shapes on an RG-15 Airfoil
Comma Separated Values - 331 B - MD5: 796ed28ac72db6c170545b975aa3f2dd
Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts
Dec 2, 2024 - UiT The Arctic University of Norway
Martinsen, Iver; Sørensen, Steffen Aagaard; Ortega, Samuel; Godtliebsen, Fred; Tejedor, Miguel; Myrvoll-Nilsen, Eirik, 2024, "Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts", https://doi.org/10.18710/QGBZZK, DataverseNO, V1
This dataset contains PNG images of individual foraminifera and sediment grains. The dataset contains a set of training images, and a set of test images. Each of the sets contains four subfolders for each of the four classes in the dataset. The four classes are: A (Benthic Agglut...
00_README.txt
Dec 2, 2024 - Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts
Plain Text - 4.7 KB - MD5: 9192c827b76e670047c63b1239ef640e
test_images.zip
Dec 2, 2024 - Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts
ZIP Archive - 26.1 MB - MD5: ec1d0be919dc567eb61e31742d366f58
train_images.zip
Dec 2, 2024 - Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts
ZIP Archive - 102.3 MB - MD5: f7f426770785272364dde3c6e03a801f
Investigating rhoticity in Scottish Standard English with sociolinguistic interviews and corpus data: Auditory ratings
Nov 26, 2024 - TROLLing
Schützler, Ole, 2024, "Investigating rhoticity in Scottish Standard English with sociolinguistic interviews and corpus data: Auditory ratings", https://doi.org/10.18710/XKCCV1, DataverseNO, V1
The dataset is used in an article published in World Englishes. In the article, I test and discuss methodological and theoretical implications of combining classic sociolinguistic interview data with data from a spoken corpus. Thus, the data combined in this set stem from two sou...
00_ReadMe_codaR_AuditoryRatings.txt
Nov 26, 2024 - Investigating rhoticity in Scottish Standard English with sociolinguistic interviews and corpus data: Auditory ratings
Plain Text - 6.3 KB - MD5: 2ab1394325c76b6adce5bc6fc394044f
Documentation
codaR.csv
Nov 26, 2024 - Investigating rhoticity in Scottish Standard English with sociolinguistic interviews and corpus data: Auditory ratings
Comma Separated Values - 165.8 KB - MD5: ad007718d9fc47a43ed5ae9cab44b317
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