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5,241 to 5,250 of 11,633 Results
ts8168_decimated.obj
Apr 7, 2022 - High resolution 3D-scan of the runestone from Ervik (Ts. 8168), Harstad municipality
TGIF File - 292.9 MB - MD5: 6902dc38add292611eed1fe1a6a9ced8
Decimated 3D-model in Wavefront *.obj format.
ts8168_main.obj
Apr 7, 2022 - High resolution 3D-scan of the runestone from Ervik (Ts. 8168), Harstad municipality
TGIF File - 1.5 GB - MD5: fffdaf7c6ea02b0463976690d79aa029
Maximum quality 3D-model in Wavefront *.obj format.
Ts8168_runestone_ambientOC.jpg
Ts8168_runestone_ambientOC.jpg
Apr 7, 2022 - High resolution 3D-scan of the runestone from Ervik (Ts. 8168), Harstad municipality
JPEG Image - 9.7 MB - MD5: 2e7faabfb17f4619536965598951e917
Composite figure of ambient occlusion renders from different angles.
TGO Ramfjordmoen Ionosonde Data March 2022
Apr 4, 2022 - Tromsø Geophysical Observatory
Tromsø Geophysical Observatory, 2022, "TGO Ramfjordmoen Ionosonde Data March 2022", https://doi.org/10.18710/QKHNHB, DataverseNO, V1
About this dataset: This dataset contains ionosonde data in SAO format and ionograms in PNG format, and covers data from March 01-31, 2022. About the Tromsø Ionosonde (1993-present): Since 1980, the ionosonde was situated at 69° 35' N, 19° 13' E at Ramfjordmoen near Tromsø, Norway and operated by the Auroral Observatory of the University of Tromsø...
01_ReadMeFile.txt
Apr 4, 2022 - TGO Ramfjordmoen Ionosonde Data March 2022
Plain Text - 2.1 KB - MD5: d3160e771ec5d0bc672bdbe49d51d6ef
TGO_Ramfjordmoen_ionosonde_data_2022-03.PNG.zip
Apr 4, 2022 - TGO Ramfjordmoen Ionosonde Data March 2022
ZIP Archive - 43.1 MB - MD5: 3ca4688d39c4e08d514da9e9b51adc9b
TGO_Ramfjordmoen_ionosonde_data_2022-03.SAO.zip
Apr 4, 2022 - TGO Ramfjordmoen Ionosonde Data March 2022
ZIP Archive - 10.4 MB - MD5: d951d27ac90589e858b5bd6d7186ec18
Benchmark dataset for graph classification
Mar 30, 2022
Bianchi, Filippo Maria, 2022, "Benchmark dataset for graph classification", https://doi.org/10.18710/TIZ9II, DataverseNO, V1
This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks. The purpose of this dataset is to make the features on the nodes and the adjacency matrix to be completely uninformative if considered alone. Therefore, an algorithm that relies only on the node features or on the grap...
00README.txt
Mar 30, 2022 - Benchmark dataset for graph classification
Plain Text - 10.7 KB - MD5: 38648d2160df71c52bdb28a70c9d193a
Dataset description
easy.npz
Mar 30, 2022 - Benchmark dataset for graph classification
Unknown - 21.6 MB - MD5: 5746717f598a39097f7069863aa03846
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