7,151 to 7,160 of 7,427 Results
Apr 20, 2020
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... |
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
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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==
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Feb 3, 2020
ter Maat, Geertje W., 2020, "Replication Data for: Separating geometry- from stress-induced remanent magnetization in magnetite with ilmenite lamellae from the Stardalur basalts, Iceland", https://doi.org/10.18710/GHHUNU, DataverseNO, V1, UNF:6:8ErThoqOgotQt1AclommUA== [fileUNF]
Bulk rock magnetic measurements (hysteresis loops, backfield curves and FORC diagrams) were done on basalts from the Stardalur volcano. Realistic geometries of magnetite grains from those basalts were obtained by FIB-SEM nanotomography. Meshes of these geometries were used to create micromagnetic models. The magnetic properties of these grains were... |
Plain Text - 1.3 KB -
MD5: a6efd711c853f1b8c85a702c89c8fa69
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Tabular Data - 126 B - 3 Variables, 10 Observations - UNF:6:BfhbjjzwGiINHipo2YNr2A==
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Unknown - 156.1 MB -
MD5: 9735366bfe179ed2be70acaf3252de78
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Unknown - 140.0 MB -
MD5: 0d0e26e3722a038199a52885229f78ef
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Unknown - 181.6 MB -
MD5: 49812fb53701de38c1ca6210858cc741
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Unknown - 174.8 MB -
MD5: 97b0dd9ffc5ea22643ec5bba8a0a1e9d
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