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21 to 30 of 62 Results
Jun 16, 2014 - Statistical Natural Language Processing Group
Wäschle, Katharina; Riezler, Stefan, 2014, "PatTR: Patent Translation Resource", https://doi.org/10.11588/data/10002, heiDATA, V3
PatTR is a sentence-parallel corpus extracted from the MAREC patent collection. The current version contains more than 22 million German-English and 18 million French-English parallel sentences collected from all patent text sections as well as 5 million German-French sentence pa... |
Sep 28, 2023 - Atmosphäre
Vardag, Sanam Noreen; Maiwald, Robert, 2023, "Optimising Urban Measurement Networks for CO2 Flux Estimation: A High-Resolution Observing System Simulation Experiment using GRAMM/GRAL [data]", https://doi.org/10.11588/data/NHIVDO, heiDATA, V1
The data set contains CO2 concentration fields at 2m height above ground in Heidelberg, Germany as simulated by the model GRAMM (v19.1)/GRAL(v.19.1). It can be used as input for Observing System Simulation Experiments (OSSE). The model GRAMM and a link to its documentation can be... |
Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1
System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text |
Apr 26, 2024 - GIScience / Geoinformatics Research Group
Schultz, Michael; Li, Hao; Wu, Zhaoyhan; Wiell, Daniel; Auer, Michael; Zipf, Alexander, 2024, "OpenStreetMap land use for Europe "Research Data"", https://doi.org/10.11588/data/IUTCDN, heiDATA, V1
OSMLanduse data is a scientific dataset generated within the scope of the Horizon 2020 - LandSense project. It is a classification of Sentinel-2 imagery using a deep learning model trained on OSM landuse and landcover features. The data might contain errorneous classifications. T... |
Aug 18, 2021 - 3D Spatial Data Processing
Weiser, Hannah; Winiwarter, Lukas; Anders, Katharina; Fassnacht, Fabian Ewald; Höfle, Bernhard, 2021, "Opaque Voxel-based Tree Models for Virtual Laser Scanning in Forestry Applications [Research Data and Source Code]", https://doi.org/10.11588/data/MZBO7T, heiDATA, V1
Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is as a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key... |
Diekert, Florian; Schaap, Robbert-Jan; Eymess, Tillmann, 2022, "NATCOOP dataset", https://doi.org/10.11588/data/GV8NBL, heiDATA, V1, UNF:6:3YvGZs4Tn05DRKwdxpNhwA== [fileUNF]
The NATCOOP project set out to study how nature shapes the preferences and incentives of economic agents and how this in turn affects common-pool resource management. Imagine a group of fishermen targeting a species that requires a lot of teamwork to harvest. Do these fishers bec... |
Jul 11, 2023 - 3D Spatial Data Processing
Vallejo Orti, Miguel; Negussie, Kaleb; Corral, Eva; Höfle, Bernhard; Bubenzer, Olaf, 2023, "Multi Profile Curvature Analysis (MPCA) algorithm for gully detection using TanDEM X Digital elevation model.", https://doi.org/10.11588/data/A4KGYJ, heiDATA, V1
Characterization of micro-terrain features has been explored to detect convex and concave features in the terrain. The analysis of first and second derivatives of a function fitted to the terrain is a frequently used resource to describe terrain characteristics and to undertake G... |
Dec 4, 2023 - HITS MBM
Roessner Rita; Michelarakis, Nicholas; Gräter, Frauke; Aponte-Santamaría, Camilo, 2023, "Mechanical forces control the valency of the malaria adhesin VAR2CSA by exposing cryptic glycan binding sites [data]", https://doi.org/10.11588/data/EZHMDU, heiDATA, V1
Plasmodium falciparum (Pf) is responsible for the most lethal form of malaria. VAR2CSA is an adhesin protein expressed by this parasite at the membrane of infected erythrocytes for attachment to the placenta, leading to pregnancy-associated malaria. VAR2CSA is a large 355 kDa mul... |
Ludwig, Christina; Hecht, Robert; Lautenbach, Sven; Schorcht, Martin; Zipf, Alexander, 2020, "Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code", https://doi.org/10.11588/data/UYSAA5, heiDATA, V1, UNF:6:+pceldpLQoaQQqPk4/t1VQ== [fileUNF]
Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data... |
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... |
