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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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1 to 10 of 721 Results
Jun 20, 2024 - heiEDITIONS
Chinca, Mark; Hunter, Helen; Wolf, Jürgen; Young, Christopher, 2018, "Kaiserchronik - digital", https://doi.org/10.11588/data/PEFKJM, heiDATA, V4
The digital edition presents the entire manuscript transmission of the Kaiserchronik, both known and extant, in dual format: digital facsimiles of the manuscripts (where these are still extant) on the one hand, transcriptions of the manuscript text on the other. Manuscripts now l...
Jun 18, 2024 - Blasco Group
Vazquez-Martel, Clara; Florido Martins, Lilliana; Genthner, Elisa; Almeida, Carlos; Martel Quintana, Antera; Bastmeyer, Martin; Gómez Pinchetti, Juan Luis; Blasco, Eva, 2024, "Printing Green: Microalgae-based materials for 3D printing with light [data]", https://doi.org/10.11588/data/KSMIGY, heiDATA, V1
Microalgae have emerged as sustainable feedstocks due to their ability to fix CO2 during cultivation, rapid growth rates, and capability to produce a wide variety of metabolites. Several microalgae accumulate lipids in high concentrations, especially triglycerides, along with lip...
Jun 17, 2024 - Blasco Group
Kröger, Finn; Eichelmann, Robert; Sauter, Gabriel; Pollien, Audrey; Tegeder, Petra; Gade, Lutz H.; Blasco, Eva, 2024, "Two-Photon Laser Printing of 3D Multicolor Emissive Polymer Microstructures [data]", https://doi.org/10.11588/data/JTWFKX, heiDATA, V1
In this study, we aim for the fabrication of precise multi-color 3D microstructures utilizing organic emitters. We have carefully selected dyes with red, green, and blue (RGB) emission characteristics and incorporated them into printable formulations suitable for two-photon laser...
Jun 13, 2024 - AG Wittbrodt
Afting, Cassian; Mainik, Philipp; Vazquez-Martel, Clara; Abele, Tobias; Kaul, Verena; Kale, Girish; Göpfrich, Kerstin; Lemke, Steffen; Blasco, Eva; Wittbrodt, Joachim, 2024, "Minimal-Invasive 3D Laser Printing of Microimplants in Organismo [data]", https://doi.org/10.11588/data/MVBFOY, heiDATA, V1
Multi-photon 3D laser printing has gathered much attention in recent years as a means of manufacturing biocompatible scaffolds that can modify and guide cellular behavior in vitro. However, in vivo tissue engineering efforts have been limited so far to the implantation of beforeh...
Jun 5, 2024 - Bunz Group
Elter, Maximilian; Ahrens, Lukas; Luo, Stella M.; Rominger, Frank; Freudenberg, Jan; Cao, Dennis D.; Bunz, Uwe H. F., 2024, "Cata-Annulated Azaacene Bisimides [data]", https://doi.org/10.11588/data/EVMJNN, heiDATA, V1
Ultra-electron-deficient azaacenes were synthesized via Buchwald-Hartwig coupling of ortho-diaminoarenes with chlorinated mellophanic diimide followed by oxidation of the intermediate N,N’-dihydro compounds with MnO2 or PbO2. The resulting cata-annulated bisimide azaacenes have u...
May 27, 2024 - Bunz Group
Ahrens, Lukas; Tverskoy, Olena; Weigold, Svenja; Ganschow, Michael; Rominger, Frank; Freudenberg, Jan; Bunz, Uwe H. F., 2024, "(Aza)Pentacenes Clipped into a Ring: Stabilization of Large (Aza)Acenes [data]", https://doi.org/10.11588/data/5VN0H0, heiDATA, V1
A doubly alkylene bridged 6,13-diphenylpentacene and analogously bridged azapentacenes were prepared; they are persistent. The doubly bridged azapentacenes display superior photochemical, oxidative and thermal stabilities compared to azapentacenes protected by bis(TIPS-ethynyl)-s...
May 13, 2024 - 3D Spatial Data Processing
Vallejo-Orti, Miguel; Winiwarter, Lukas; Corral, Eva; Williams, Jack; Bubenzer, Olaf; Höfle, Bernhard, 2024, "An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]", https://doi.org/10.11588/data/WGAU4Q, heiDATA, V1
Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training...
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...
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...
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...
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