2,201 to 2,210 of 11,076 Results
Apr 4, 2023 -
Swedish observations of the Aurora Borealis in the period 1716-1732 in contemporaneous scholarly publications
Comma Separated Values - 39.7 KB -
MD5: 0df96f5eb3da3a5538d6b52320a4f2b1
Dataset file |
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 publi... |
Mar 31, 2023 -
Replication Data for: Dataset of motivational factors for using mobile health applications and systems
Plain Text - 23.1 KB -
MD5: d29a0fa833e1d0878531dd44fccd154a
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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 |
Mar 31, 2023 -
Replication Data for: Dataset of motivational factors for using mobile health applications and systems
Adobe PDF - 142.7 KB -
MD5: bdc00c7afcaca72a27f9e4be30b87114
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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 |
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... |
Mar 29, 2023 -
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Plain Text - 6.2 KB -
MD5: 66b4fc3733f5c54175c57f7c40d24869
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Mar 29, 2023 -
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
ZIP Archive - 8.7 GB -
MD5: 776f8bccbb1285032864afee9cfa991b
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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 |