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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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8,021 to 8,030 of 11,083 Results

00_readme.txt May 6, 2020 - Hydrography and vertical carbon flux in the Barents Sea Plain Text - 3.0 KB - MD5: 64ac3727695bf95fb67b0cf47fece27e Information on station locations, sampling procedure, and CTD data processing
ARCEx_POC_flux.txt May 6, 2020 - Hydrography and vertical carbon flux in the Barents Sea Plain Text - 1.1 KB - MD5: 6a4dc5977473e3bfba8c71d20fdf4b5a Location and date of the sediment trap deployment and the export of particulate organic carbon (POC)
StationArS_CTDstnr875_1db_dens.txt May 6, 2020 - Hydrography and vertical carbon flux in the Barents Sea Plain Text - 30.4 KB - MD5: 0e91a1c422f2182f4543f62d91b018c7 Hydrographical data (CTD file) from station ArS (Station number 875)
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 A...
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

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

Plain Text - 3.0 KB -

Information on station locations, sampling procedure, and CTD data processing

ARCEx_POC_flux.txt

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

Plain Text - 1.1 KB -

Location and date of the sediment trap deployment and the export of particulate organic carbon (POC)

StationArS_CTDstnr875_1db_dens.txt

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

Plain Text - 30.4 KB -

Hydrographical data (CTD file) from station ArS (Station number 875)

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 A...

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.

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