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heiDATA is an institutional repository for research data of Heidelberg University. It is managed by the Competence Centre for Research Data, a joint institution of the University Library and the Computing Centre. If you are interested in publishing your data here, please see our author instructions and get in touch with us.
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41 to 50 of 233 Results
Feb 6, 2019 - AIPHES
Heinzerling, Benjamin, 2019, "Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"", https://doi.org/10.11588/data/9JKAVW, heiDATA, V1
This dataset contains source code and system output used in the PhD thesis "Aspects of Coherence for Entity Analysis". This dataset is split into three parts corresponding to the chapters describing the three main contributions of the thesis: chapter3.tar.gz: Java source code for...
Feb 6, 2019 - Computer Assisted Clinical Medicine
Zöllner, Frank; Lietzmann, Florian; Attenberger, Ulrike; Haneder, Stefan; Michaely, Henrik; Schad, Lothar, 2019, "DCE-MRI of the human kidney using BLADE: A feasibility study in healthy volunteers [Dataset]", https://doi.org/10.11588/data/5RSAM3, heiDATA, V1
To evaluate the degree of motion compensation in the kidney using two different sampling methods, each in their optimized settings: A BLADE k-space acquisition technique and a routinely used kidney perfusion acquisition scheme (TurboFLASH). Dynamic contrast enhanced magnetic reso...
Feb 5, 2019SFB 933: Materiale Textkulturen
Datenpublikationen des Teilprojekts C05 des SFB 933: Materiale Textkulturen.
Feb 5, 2019 - Computer Assisted Clinical Medicine
Davids, M.; Zöllner, F.; Ruttorf, M.; Nees, F.; Flor, H.; Schumann, G.; Schad, L.; the Imagen Consortium, 2019, "Fully-automated quality assurance in multi-center studies using MRI phantom measurements [Dataset]", https://doi.org/10.11588/data/RR5BMF, heiDATA, V1
43 measurements acquired in eight different sites within the IMAGEN-project, comprising the following 3 T scanner types: Siemens Verio and TimTrio; General Electric Signa Excite, and Signa HDx; Philips Achieva. Additionally one phantom data set was aquired on a 3 T Siemens Skyra...
Feb 4, 2019 - AIPHES
Marasovic, Ana, 2019, "Abstract Anaphora Resolution [Source Code]", https://doi.org/10.11588/data/UDMPY5, heiDATA, V1
Abstract Anaphora Resolution (AAR) aims to find the interpretation of nominal expressions (e.g., this result, those two actions) and pronominal expressions (e.g., this, that, it) that refer to abstract-object-antecedents such as facts, events, plans, actions, or situations. The f...
Feb 4, 2019 - AIPHES
Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1
This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar...
Feb 1, 2019 - SFB 933 Materiale Textkulturen - Teilprojekt B11
Sauer, Hein, 2019, "Datenpublikation zu materialen Formierungen musiktheoretischer Konzepte: Praxeologie eines Fachschrifttums im ausgehenden Mittelalter", https://doi.org/10.11588/data/MM6ZGU, heiDATA, V1
Das Projekt untersucht in der Überlieferung musiktheoretischer Texte an der Wende vom Spätmittelalter zur frühen Neuzeit die inhaltliche Konstitution, materiale Gestaltung und praktische Verwendung von Handschriften (und auch frühen Drucken): Wie wird theoretisches Wissen über Mu...
Feb 1, 2019SFB 933: Materiale Textkulturen
Datenpublikationen des Teilprojekts B11 des SFB 933: Materiale Textkulturen.
SFB 933: Materiale Textkulturen(Heidelberg University)
Feb 1, 2019
Datenpublikationen des SFB 933: Materiale Textkulturen.
Jan 31, 2019 - AIPHES
Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1
Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...
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