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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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8,131 to 8,140 of 11,186 Results

StationAS_CTDstnr929_1db_dens.txt May 6, 2020 - Hydrography and vertical carbon flux in the Barents Sea Plain Text - 31.6 KB - MD5: b5c9bf40b200cb53b4210d1eec1f41c5 Hydrographical data (CTD file) from station AS (Station number 929)
StationHS_CTDstnr818_1db_dens.txt May 6, 2020 - Hydrography and vertical carbon flux in the Barents Sea Plain Text - 13.6 KB - MD5: 268eec54dfcada160e3665658875d431 Hydrographical data (CTD file) from station HS (Station number 818)
Replication Data for: Auroral Image Classification with Deep Neural Networks Apr 14, 2020 Kvammen, Andreas; Wickstrøm, Kristoffer; McKay, Derek; Partamies, Noora, 2020, "Replication Data for: Auroral Image Classification with Deep Neural Networks", https://doi.org/10.18710/SSA38J, DataverseNO, V3 Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statis...
00_ReadMe.txt Apr 14, 2020 - Replication Data for: Auroral Image Classification with Deep Neural Networks Plain Text - 4.6 KB - MD5: 6c25a3fdae5cc8db0938a055991b217e ReadMe file where dataset and the files in repository is described.
TestImages.zip Apr 14, 2020 - Replication Data for: Auroral Image Classification with Deep Neural Networks ZIP Archive - 10.2 MB - MD5: b1ff172b00f06c86dd82c021683d451e Zipped archive containing the test images of the dataset in .PNG format. The class of the image is included in the image title.
TrainingImages.zip Apr 14, 2020 - Replication Data for: Auroral Image Classification with Deep Neural Networks ZIP Archive - 35.8 MB - MD5: b27e63cd55a51e756d0c387a1b7be893 Zipped archive containing the training images of the dataset in .PNG format. The class of the image is included in the image title.
Replication data for: User expectations and willingsness to share self-collected health Apr 3, 2020 Henriksen, André; Woldaregay, Ashenafi Zebene; Issom, David-Zacharie; Pfuhl, Gerit; Richard, Aude; Årsand, Eirik; Sato, Keiichi; Hartvigsen, Gunnar; Rochat, Jessica, 2019, "Replication data for: User expectations and willingsness to share self-collected health", https://doi.org/10.18710/28SRMJ, DataverseNO, V2, UNF:6:n0cjZA3X6VyQdVUJnYXoDg== [fileUNF] This is a questionnaire used in a project where the aim was to understand what motivates people to share self-collected health data, collected by mobile wearables and sensors.
00_ReadMe.txt Apr 3, 2020 - Replication data for: User expectations and willingsness to share self-collected health Plain Text - 496 B - MD5: 75cb4e51b257981de7ed49f9f729b842
questionnaire-1.pdf Apr 3, 2020 - Replication data for: User expectations and willingsness to share self-collected health Adobe PDF - 179.6 KB - MD5: ae5a78534f43f29ac76409df596544a4 Questionnaire
Responses-1.tab Apr 3, 2020 - Replication data for: User expectations and willingsness to share self-collected health Tabular Data - 51.3 KB - 31 Variables, 446 Observations - UNF:6:ChVIuTGMhUp1+QwFhBGo2w== SPSS

StationAS_CTDstnr929_1db_dens.txt

May 6, 2020 - Hydrography and vertical carbon flux in the Barents Sea

Plain Text - 31.6 KB -

Hydrographical data (CTD file) from station AS (Station number 929)

StationHS_CTDstnr818_1db_dens.txt

May 6, 2020 - Hydrography and vertical carbon flux in the Barents Sea

Plain Text - 13.6 KB -

Hydrographical data (CTD file) from station HS (Station number 818)

Replication Data for: Auroral Image Classification with Deep Neural Networks

Apr 14, 2020

Kvammen, Andreas; Wickstrøm, Kristoffer; McKay, Derek; Partamies, Noora, 2020, "Replication Data for: Auroral Image Classification with Deep Neural Networks", https://doi.org/10.18710/SSA38J, DataverseNO, V3

Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statis...

00_ReadMe.txt

Apr 14, 2020 - Replication Data for: Auroral Image Classification with Deep Neural Networks

Plain Text - 4.6 KB -

ReadMe file where dataset and the files in repository is described.

TestImages.zip

Apr 14, 2020 - Replication Data for: Auroral Image Classification with Deep Neural Networks

ZIP Archive - 10.2 MB -

Zipped archive containing the test images of the dataset in .PNG format. The class of the image is included in the image title.

TrainingImages.zip

Apr 14, 2020 - Replication Data for: Auroral Image Classification with Deep Neural Networks

ZIP Archive - 35.8 MB -

Zipped archive containing the training images of the dataset in .PNG format. The class of the image is included in the image title.

Replication data for: User expectations and willingsness to share self-collected health

Apr 3, 2020

Henriksen, André; Woldaregay, Ashenafi Zebene; Issom, David-Zacharie; Pfuhl, Gerit; Richard, Aude; Årsand, Eirik; Sato, Keiichi; Hartvigsen, Gunnar; Rochat, Jessica, 2019, "Replication data for: User expectations and willingsness to share self-collected health", https://doi.org/10.18710/28SRMJ, DataverseNO, V2, UNF:6:n0cjZA3X6VyQdVUJnYXoDg== [fileUNF]

This is a questionnaire used in a project where the aim was to understand what motivates people to share self-collected health data, collected by mobile wearables and sensors.

00_ReadMe.txt

Apr 3, 2020 - Replication data for: User expectations and willingsness to share self-collected health

Plain Text - 496 B -

questionnaire-1.pdf

Apr 3, 2020 - Replication data for: User expectations and willingsness to share self-collected health

Adobe PDF - 179.6 KB -

Questionnaire

Responses-1.tab

Apr 3, 2020 - Replication data for: User expectations and willingsness to share self-collected health

Tabular Data - 51.3 KB - 31 Variables, 446 Observations -

SPSS

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