Universität Leipzig Data Publications

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Data publications from research of Leipzig University.

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Browsing Universität Leipzig Data Publications by Title

Now showing 1 - 11 of 11
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    Open Access
    A New Risk Assessment Model for Unexploded underwater Military Munitions: Application Data
    (Universität Leipzig, 2025-03-03) Frey, Torsten
    This file contains application demonstration data from the following doctoral thesis: Frey, Torsten. A New Risk Assessment Model for Unexploded underwater Military Munitions. Dissertation. Leipzig University. Leipzig. 2025. https://doi.org/10.5281/zenodo.14620239. For detailed information, the interested party is referred to section 6.3 of the dissertation.
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    Open Access
    A New Risk Assessment Model for Unexploded underwater Military Munitions: Monte Carlo Simulation
    (Universität Leipzig, 2025-02-25) Frey, Torsten
    This file contains monte carlo simulation data from the following doctoral thesis: Frey, Torsten. A New Risk Assessment Model for Unexploded underwater Military Munitions. Dissertation. Leipzig University. Leipzig. 2025. https://doi.org/10.5281/zenodo.14620239. For detailed information, the interested party is referred to section 6.2 and Appendix 10 of the dissertation.
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    Open Access
    A New Risk Assessment Model for Unexploded underwater Military Munitions: Sensitivity Analysis
    (Universität Leipzig, 2025-02-04) Frey, Torsten
    This file contains sensitivity analysis data to the following doctoral thesis: Frey, Torsten. A New Risk Assessment Model for Unexploded underwater Military Munitions. Dissertation. Leipzig University. Leipzig. 2025. DOI: 10.5281/zenodo.14620239. For detailed information, the interested party is referred to section 6.1 and Appendix 9 of the dissertation.
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    Open Access
    A New Risk Assessment Model for Unexploded underwater Military Munitions: Workshop 1 Poll Results
    (Universität Leipzig, 2025-03-03) Frey, Torsten
    This file contains workshop poll data from the following doctoral thesis: Frey, Torsten. A New Risk Assessment Model for Unexploded underwater Military Munitions. Dissertation. Leipzig University. Leipzig. 2025. https://doi.org/10.5281/zenodo.14620239. For detailed information, the interested party is referred to Appendices 3 and 4 of the dissertation.
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    Open Access
    A New Risk Assessment Model for Unexploded underwater Military Munitions: Workshop 2 Poll Results
    (Universität Leipzig, 2025-03-03) Frey, Torsten
    This file contains workshop poll data from the following doctoral thesis: Frey, Torsten. A New Risk Assessment Model for Unexploded underwater Military Munitions. Dissertation. Leipzig University. Leipzig. 2025. https://doi.org/10.5281/zenodo.14620239. For detailed information, the interested party is referred to Appendices 6, 7, and 8 of the dissertation.
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    Open Access
    Adipic acid concentrations from biotransformation by rec. Pseudomonas taiwanensis
    (Universität Leipzig, 2025-02-05) Franz, Alexander
    The dataset belongs to a publication in Green Chemistry from 2023 (DOI: 10.1039/d3gc01105d). In the publication a two-step process is demonstrated, in which phenolic compounds are electrochemically hydrogenated and further converted to adipic acid by a biotransformation of recombinant Pseudomonas taiwanensis. The dataset shows the data from Fig. 3c and 4c in the publication. The two tables show the adipic acid concentration over time in the 500 mL-bubble column reactors, in which the biocatalyst was fed with the substrate solution.
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    Open Access
    BiSID-5k: A Bimodal Image Dataset for Seed Classification from the Visible and Near-Infrared Spectrum
    (Universität Leipzig, 2025-04-10) Kukushkin, Maksim; Bogdan, Martin; Goertz, Simon; Callsen, Jan-Ole; Oldenburg, Eric; Enders, Matthias; Schmid, Thomas
    The success of deep learning in image classification has been largely underpinned by large-scale datasets, such as ImageNet, which have significantly advanced multi-class classification for RGB and grayscale images. However, datasets that capture spectral information beyond the visible spectrum remain scarce, despite their high potential, especially in agriculture, medicine, and remote sensing. To address this gap in the agricultural domain, we present BiSID-5k, a thoroughly curated bimodal seed image dataset comprising paired RGB and hyperspectral images for 10 plant species, making it one of the largest bimodal seed datasets available. We describe the methodology for data collection and preprocessing and benchmark several deep learning models on the dataset to evaluate their multi-class classification performance. By contributing a high-quality dataset, BiSID-5k offers a valuable resource for studying spectral, spatial, and morphological properties of seeds, opening new avenues for research and applications.
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    Open Access
    Dataset Validation Study ILEA-Basis-T
    (Universität Leipzig, 2025-03-05) Liebers, Katrin; Siegemund-Johannsen, Steffen; Viernickel, Susanne; Redersborg, Helke; von Seeler, Isabelle; Richter, Virginia; Hartke, Sara; Kauffert, Matteo
    The validation study was conducted to statistically test the validity of the developed DBS. Accordingly, the materials that had been developed for the DBS over the course of the project and had already been tested were used. The parents of all participating children gave their consent. The research ethical standards were adhered to. Likewise, a positive vote of the ethics committee of the University of Leipzig was available. At the same time, further domain-specific test procedures were used to determine convergent and discriminant validity in the areas of early literacy and early mathematics (e.g. MBK 0, Krajewski, 2018; GISC-EL, Koch, Euker & Kuhl, 2016). In addition, a comprehensive rating-based evaluation form was developed for the Early Mathematics Observation Form, with a focus on suitability for everyday use and acceptance in practice. The observation procedures for well-being and the children's questionnaire on well-being were used by project staff, students, and preschool teachers. Convergent discrimination for the habitual well-being rating procedure was determined using a selection of parallel assessment methods (excerpts from KOMPIK; Mayr, Bauer, Krause, Kruse, & Schnirch, 2014; Leiden Inventory for the Child's Wellbeing in Day Care/LICW-D; De Schipper, Van IJzendoorn, & Tavecchio, 2004).
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    Open Access
    DeepExtremeCubes
    (Universität Leipzig, 2024-12-19) Ji, Chaonan; Fincke, Tonio; Benson, Vitus; Camps-Valls, Gustau; Fernández-Torres, Miguel-Ángel; Gans, Fabian; Kraemer, Guido; Martinuzzi, Francesco; Montero, David; Mora, Karin; Pellicer-Valero, Oscar; Robin, Claire; Söchting, Maximilian; Weynants, Melanie; Mahecha, Miguel
    With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.
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    Open Access
    Earthquakes in Saxony (Germany) and surroundings from 2006 up to 2023 -- onsets and locations
    (Universität Leipzig, 2025-04-04) Wendt, Siegfried
    This archive contains complete results of earthquake locations for Saxony (Germany) and neighbourhood of the years from 2006 up to 2023. Approximately 26.000 events were detected and localized, most of them with epicenters in Northwestern Bohemia near Nový Kostel and Luby (Czech Republic) neighboured to the Saxon border.
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    Open Access
    Guidelines for quantitative survey on perception of urban green spaces during COVID-19 pandemic
    (Universität Leipzig, 2024-03-22) Hübscher, Marcus
    This is the survey guideline used for the quantitative survey.