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11 to 20 of 89 Results
Feb 16, 2024 -
Triggered contraction of self-assembled micron-scale DNA nanotube rings [Research Data]
TIFF Image - 33.0 MB -
MD5: 3cbb52face4cfe5b5c467e440baf0aec
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Feb 16, 2024 -
Triggered contraction of self-assembled micron-scale DNA nanotube rings [Research Data]
JPEG Image - 486.7 KB -
MD5: 6012ae2eec6942b532c26df8b95bb3c1
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Feb 16, 2024 -
Triggered contraction of self-assembled micron-scale DNA nanotube rings [Research Data]
TIFF Image - 33.0 MB -
MD5: 41276168cf582087ea79ed58501f50de
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Feb 16, 2024 -
Triggered contraction of self-assembled micron-scale DNA nanotube rings [Research Data]
TIFF Image - 33.0 MB -
MD5: 41276168cf582087ea79ed58501f50de
|
Feb 16, 2024 -
Triggered contraction of self-assembled micron-scale DNA nanotube rings [Research Data]
PNG Image - 27.2 KB -
MD5: 8369d19a3d16117c0f68de8dd2547a18
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Feb 16, 2024 -
Triggered contraction of self-assembled micron-scale DNA nanotube rings [Research Data]
PNG Image - 25.0 KB -
MD5: 18b0d6cc3185cc2151cfa3038628bb9b
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Jan 18, 2024 - 3D Spatial Data Processing
Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", https://doi.org/10.11588/data/UUMEDI, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]
This dataset contains 11 terrestrial laser scanning (TLS) tree point clouds (in .LAZ format v1.4) of 7 different species, which have been manually labeled into leaf and wood points. The labels are contained in the Classification field (0 = wood, 1 = leaf). The point clouds have a... |
Jan 16, 2024 - 3D Spatial Data Processing
Vallejo Orti, Miguel; Castillo, Carlos; Zahs, Vivien; Bubenzer, Olaf; Höfle, Bernhard, 2023, "Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]", https://doi.org/10.11588/data/NSMM6P, heiDATA, V2, UNF:6:KVUhApCn+Ker99oncknXzA== [fileUNF]
This code implements the TimeSeriesForest algorithm to classify different types of changes in gully environments. i)gully topographical change, ii)no change outside gully, iii) no change inside gully, and iv) non-topographical change. The algorithm is specifically designed for ti... |
Jan 2, 2024 - Data Analysis and Modeling in Medicine (MIISM) - Group Hesser
Jerg, Katharina, 2024, "Real-time definition of single seed placement sensitivity in low-dose-rate prostate brachytherapy [code and patient data]", https://doi.org/10.11588/data/Y3KIPM, heiDATA, V1
PURPOSE: In low-dose-rate brachytherapy iodine seeds are implanted based on a treatment plan, generated with respect to different dose constraints. The quality of the dose distribution depends on a precise seed placement, however, it is not clear which seed misplacements have a l... |
Dec 13, 2023 - 3D Spatial Data Processing
Vallejo Orti, Miguel; Negussie, Kaleb; Corral, Eva; Höfle, Bernhard; Bubenzer, Olaf, 2023, "Gully detection with Inverse Morphological Reconstruction Algorithm [data]", https://doi.org/10.11588/data/PXDR4M, heiDATA, V1
Characterization of micro-terrain features has been explored to detect gully objects in the terrain. An adaptation to the morphological reconstruction operator is implemented to detect gullies instead of buildings or other man-made structures. This operator can be configured to d... |