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The Stein Rokkan Research Group for Quantitative Social and Political Science

Tromsø Geophysical Observatory

Arctic Language Technology

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2,261 to 2,270 of 11,140 Results

Question-metadata.csv Mar 31, 2023 - Replication Data for: Dataset of motivational factors for using mobile health applications and systems Comma Separated Values - 26.1 KB - MD5: d2413d54d36192d98442c49e22bdd9f0 Describes which answer options are available for each question
Questionnaire.pdf Mar 31, 2023 - Replication Data for: Dataset of motivational factors for using mobile health applications and systems Adobe PDF - 142.7 KB - MD5: bdc00c7afcaca72a27f9e4be30b87114
Responses.csv Mar 31, 2023 - Replication Data for: Dataset of motivational factors for using mobile health applications and systems Comma Separated Values - 281.0 KB - MD5: c3b1ace55b8d7348abe8c22d7b8b6039 Questionnaire responses
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images Mar 29, 2023 Gupta, Deepak K.; Bhamba, Udbhav; Thakur, Abhishek; Gupta, Akash; Sharan, Suraj; Demir, Ertugrul; Prasad, Dilip K., 2023, "Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images", https://doi.org/10.18710/4F4KJS, DataverseNO, V1 Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, challenges related to GPU memory, smaller receptive field than needed for semantic correspondence and th...
00_ReadMe.txt Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images Plain Text - 6.2 KB - MD5: 66b4fc3733f5c54175c57f7c40d24869
test.zip Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images ZIP Archive - 8.7 GB - MD5: 776f8bccbb1285032864afee9cfa991b
train.csv Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images Comma Separated Values - 373.1 KB - MD5: acd730388d00a1102f17a8139106ac42 For testing purposes you may contact Dilip K. Prasad at dilip.prasad@uit.no
train.zip Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images ZIP Archive - 8.8 GB - MD5: d6b3f3ca33e41e006e258f93704417e2 The training files containing all the images of the training set.
221122_0081.flac Mar 23, 2023 - Replication Data for Microphone recording of flexural waves for estimation of lake ice thickness audio/x-flac - 18.1 MB - MD5: 03086fa6856699c944ef3987984dfb5f
221122_0082.flac Mar 23, 2023 - Replication Data for Microphone recording of flexural waves for estimation of lake ice thickness audio/x-flac - 15.0 MB - MD5: c69ad6009879ee8619b5cafb9a2a4a4e

Question-metadata.csv

Mar 31, 2023 - Replication Data for: Dataset of motivational factors for using mobile health applications and systems

Comma Separated Values - 26.1 KB -

Describes which answer options are available for each question

Questionnaire.pdf

Mar 31, 2023 - Replication Data for: Dataset of motivational factors for using mobile health applications and systems

Adobe PDF - 142.7 KB -

Responses.csv

Mar 31, 2023 - Replication Data for: Dataset of motivational factors for using mobile health applications and systems

Comma Separated Values - 281.0 KB -

Questionnaire responses

Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Mar 29, 2023

Gupta, Deepak K.; Bhamba, Udbhav; Thakur, Abhishek; Gupta, Akash; Sharan, Suraj; Demir, Ertugrul; Prasad, Dilip K., 2023, "Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images", https://doi.org/10.18710/4F4KJS, DataverseNO, V1

Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, challenges related to GPU memory, smaller receptive field than needed for semantic correspondence and th...

00_ReadMe.txt

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Plain Text - 6.2 KB -

test.zip

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

ZIP Archive - 8.7 GB -

train.csv

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Comma Separated Values - 373.1 KB -

For testing purposes you may contact Dilip K. Prasad at dilip.prasad@uit.no

train.zip

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

ZIP Archive - 8.8 GB -

The training files containing all the images of the training set.

221122_0081.flac

Mar 23, 2023 - Replication Data for Microphone recording of flexural waves for estimation of lake ice thickness

audio/x-flac - 18.1 MB -

221122_0082.flac

Mar 23, 2023 - Replication Data for Microphone recording of flexural waves for estimation of lake ice thickness

audio/x-flac - 15.0 MB -

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