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2,281 to 2,290 of 11,162 Results

Replication Data for: Dataset of motivational factors for using mobile health applications and systems Mar 31, 2023 Henriksen, André; Woldaregay, Ashenafi Zebene; Issom, David-Zacharie; Pfuhl, Gerit; Sato, Keiichi; Årsand, Eirik; Hartvigsen, Gunnar, 2023, "Replication Data for: Dataset of motivational factors for using mobile health applications and systems", https://doi.org/10.18710/AOQF05, DataverseNO, V1 This dataset contains responses from a questionnaire about what motivates people to collect and share their health data for research and public health benefits. The online questionnaire was open for data collection between November 2018 and March 2020. The questionnaire was published in a Norwegian, English and French version, and published online....
00_ReadMe.txt Mar 31, 2023 - Replication Data for: Dataset of motivational factors for using mobile health applications and systems Plain Text - 23.1 KB - MD5: d29a0fa833e1d0878531dd44fccd154a
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 the need to incorporate multi-scale features arise. The resolution of in...
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.

Replication Data for: Dataset of motivational factors for using mobile health applications and systems

Mar 31, 2023

Henriksen, André; Woldaregay, Ashenafi Zebene; Issom, David-Zacharie; Pfuhl, Gerit; Sato, Keiichi; Årsand, Eirik; Hartvigsen, Gunnar, 2023, "Replication Data for: Dataset of motivational factors for using mobile health applications and systems", https://doi.org/10.18710/AOQF05, DataverseNO, V1

This dataset contains responses from a questionnaire about what motivates people to collect and share their health data for research and public health benefits. The online questionnaire was open for data collection between November 2018 and March 2020. The questionnaire was published in a Norwegian, English and French version, and published online....

00_ReadMe.txt

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

Plain Text - 23.1 KB -

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 the need to incorporate multi-scale features arise. The resolution of in...

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.

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