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MF Norwegian School of Theology, Religion and Society

Nofima

Nord University

Norwegian Institute of Bioeconomy Research (NIBIO)

Norwegian University of Life Sciences (NMBU)

NTNU – Norwegian University of Science and Technology

OsloMet – Oslo Metropolitan University

UiT The Arctic University of Norway

University of Agder

University of Bergen

University of Inland Norway

University of Oslo

University of South-Eastern Norway

University of Stavanger

VID Specialized University

Western Norway University of Applied Sciences

Østfold University College

TROLLing

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131,581 to 131,590 of 138,218 Results

Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables Apr 20, 2020 - NTNU – Norwegian University of Science and Technology Riddervold, Hans Ole, 2020, "Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables", https://doi.org/10.18710/WNKSVX, DataverseNO, V1, UNF:6:gXehgeAeqs6FWVHODiWuAQ== [fileUNF] Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day. This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from tw...
00_ReadMe.txt Apr 20, 2020 - Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables Plain Text - 1.5 KB - MD5: 08bd9bc8bb6ff0eef16ace084c8df545
simple model original variables.tab Apr 20, 2020 - Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables Tabular Data - 223.4 KB - 15 Variables, 1045 Observations - UNF:6:gXehgeAeqs6FWVHODiWuAQ==
03dat1.tab Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi Tabular Data - 14.9 KB - 4 Variables, 737 Observations - UNF:6:dsqq3osqmstFH57/4I/b6A== This file contains the data for 04Rplot1.pdf.
04Rplot1.pdf Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi Adobe PDF - 830.3 KB - MD5: c7583d149343866f55f927b56417259e This is the graph that appears as Figure 2 in the article associated with this dataset.
05dat2.tab Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi Tabular Data - 3.1 KB - 1 Variables, 150 Observations - UNF:6:9W7CkLYwriV8kaVPKXC0pA== This file contains the data for 06Rplot2.pdf.
06Rplot2.pdf Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi Adobe PDF - 203.1 KB - MD5: 92b83b18ed4bb623fa23899d7620f4b5 This is the graph that appears as Figure 3 in the article associated with this dataset.
Replication Data for: Auroral Image Classification with Deep Neural Networks Apr 14, 2020 - UiT The Arctic University of Norway 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.

Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables

Apr 20, 2020 - NTNU – Norwegian University of Science and Technology

Riddervold, Hans Ole, 2020, "Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables", https://doi.org/10.18710/WNKSVX, DataverseNO, V1, UNF:6:gXehgeAeqs6FWVHODiWuAQ== [fileUNF]

Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day. This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from tw...

00_ReadMe.txt

Apr 20, 2020 - Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables

Plain Text - 1.5 KB -

simple model original variables.tab

Apr 20, 2020 - Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables

Tabular Data - 223.4 KB - 15 Variables, 1045 Observations -

03dat1.tab

Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi

Tabular Data - 14.9 KB - 4 Variables, 737 Observations -

This file contains the data for 04Rplot1.pdf.

04Rplot1.pdf

Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi

Adobe PDF - 830.3 KB -

This is the graph that appears as Figure 2 in the article associated with this dataset.

05dat2.tab

Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi

Tabular Data - 3.1 KB - 1 Variables, 150 Observations -

This file contains the data for 06Rplot2.pdf.

06Rplot2.pdf

Apr 15, 2020 - Replication Data for: Davvisámi earutkeahtes oamasteapmi

Adobe PDF - 203.1 KB -

This is the graph that appears as Figure 3 in the article associated with this dataset.

Replication Data for: Auroral Image Classification with Deep Neural Networks

Apr 14, 2020 - UiT The Arctic University of Norway

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.

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