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heiDATA is an institutional repository for Open Research Data from Heidelberg University. It is managed by Heidelberg University's Research Data Unit, a joint institution of the University Library and the Computing Centre. If you are interested in publishing your data here, please have a look at our user guide and get in touch with us. Detailed information on heiDATA can be found in our service description.
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711 to 715 of 715 Results
Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]
Apr 24, 2024 - AIPHES
Mihaylov, Todor, 2024, "Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]", https://doi.org/10.11588/data/HU3ARF, heiDATA, V1
Machine Reading Comprehension is a language understanding task where a system is expected to read a given passage of text and typically answer questions about it. When humans assess the task of reading comprehension, in addition to the presented text, they usually use the knowled...
Codices Palatini latini – Juristische Handschriften
Apr 26, 2024 - Handschriftenbeschreibungen der Universitätsbibliothek Heidelberg zu den Codices Palatini
Huthwelker, Thorsten; Metzger, Wolfgang; Winterer, Christoph, 2024, "Codices Palatini latini – Juristische Handschriften", https://doi.org/10.11588/data/XIQNX0, heiDATA, V1
Here you can find the descriptions of the Latin Palatine manuscripts as published in the manuscripts catalog "Die juristischen Handschriften der Codices Palatini Latini in der Vatikanischen Bibliothek (Cod. Pal. lat. 621-813)", edited by Thorsten Huthwelker, Wolfgang Metzger und...
OpenStreetMap land use for Europe "Research Data"
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...
Developmental origins and evolution of pallial cell types and structures in birds - Data
Apr 30, 2024 - Kaessmann Lab
Zaremba, Bastienne; Fallahshahroudi, Amir; Schneider, Céline; Schmidt, Julia; Sarropoulos, Ioannis; Leushkin, Evgeny; Berki, Bianka; Van Poucke, Enya; Jensen, Per; Senovilla-Ganzo, Rodrigo; Hervas-Sotomayor, Francisca; Trost, Nils; Lamanna, Francesco; Sepp, Mari; García-Moreno, Fernando; Kaessmann, Henrik, 2024, "Developmental origins and evolution of pallial cell types and structures in birds - Data", https://doi.org/10.11588/data/BX6REK, heiDATA, V1, UNF:6:4DNo7hOUtj/vCfSrVp37wA== [fileUNF]
The advanced cognitive abilities of birds rival those of mammals and have been attributed to evolutionary innovations in the pallium. However, a comprehensive cellular characterization of this brain region in birds has been lacking. We scrutinized the structures, cell types and e...
A Minimalistic, Synthetic Cell-Inspired Metamaterial for Enabling Reversible Strain-Stiffening [data]
May 1, 2024 - 3D Matter Made to Order (3DMM2O)
Taale, Mohammadreza, 2024, "A Minimalistic, Synthetic Cell-Inspired Metamaterial for Enabling Reversible Strain-Stiffening [data]", https://doi.org/10.11588/data/MNMLVX, heiDATA, V2
Strain-stiffening, i.e. the nonlinear stiffening of a material in response to a strain, is an intrinsic feature of many biological systems, including skin, blood vessels, and single cells. To avoid a mismatch in mechanical properties, synthetic materials in contact with such biol...
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