DaKS - University of Kassel's research data repository

DaKS is the institutional repository of the University of Kassel for research data. It offers structured storage of research data alongside with descriptive metadata, long-term archiving for at least 10 years and – if requested – the publication of the dataset with a DOI.

DaKS is managed by the university library and the IT Service Centre of the University of Kassel. It is hosted at Philipps-Universität Marburg. We are happy to advise you via daks@uni-kassel.de.

Recent Submissions

Volumetric Heating Capacity (VHC) Regression Parameters for 48 Refrigerants
(Universität Kassel) Jesper, Mateo; Hoffmann, Chris René; Pag, Felix; Vajen, Klaus; Jordan, Ulrike
This repository provides the fourth-order Hamilton-Allen regression parameters for calculating the volumetric heating capacity (VHC) of 48 refrigerants as a function of heat source and sink temperatures. The parameters are derived from the study:
Large-Scale Heat Pumps: Development of a Broadly Applicable, Easy-to-Use Heating Capacity Model

Jesper, Mateo; Hoffmann, Chris René; Pag, Felix; Vajen, Klaus; Jordan, Ulrike

Preprint available at: http://dx.doi.org/10.2139/ssrn.5082708

The regression model enables simple, accurate, and broadly applicable estimates of VHC for large-scale heat pump applications, including district heating and industrial processes. The parameters are validated with a coefficient of determination (R²) exceeding 0.998 for all refrigerants, ensuring high reliability for preliminary design and feasibility studies.

Key Features:
- Coefficients for fourth-order Hamilton-Allen polynomial models (Eq. 9 in the paper) for each refrigerant.
Parameters cover a wide range of operating conditions, defined by heat source and sink temperatures within the constraints outlined in the study.
- Data for 48 refrigerants, including conventional and low-GWP (Global Warming Potential) options.

Applications:
- Suitable for preliminary design, feasibility assessments, and comparative analyses of heat pump systems.
- Complements COP (Coefficient of Performance) regression models for complete heat pump modeling.

Assumptions and Limitations:
- The model assumes fixed superheating (7 K) and subcooling (1 K).
- Isentropic compressor efficiency is assumed to be ideal for simplicity.
- Errors may arise if real-world conditions deviate significantly from these assumptions (see Section 4.5 of the paper for sensitivity analysis).
MATLAB Code for Optimizing Heterogeneous Coupling Gains in Networks of Nonlinear Systems using Leader–Follower Topologies
(Universität Kassel) Kräling, Lukas; Liu, Zonglin; Stursberg, Olaf
Using identical coupling gains in a network of dynamical systems is a common approach in studying synchronization. We consider networks with leader-follower topologies and use heterogeneous coupling gains. This approach allows optimizing the different coupling gains while still guaranteeing synchronization, provided that certain assumptions are met (for further details, see the referenced work).

The code demonstrates the effectiveness of our approach. It shows that the sum of all coupling gains can be reduced significantly. This optimization can be graphically represented. Furthermore, code for a numerical example of the networked dynamics is provided.
Lineage Tracking and Asymmetric Inheritance Patterns of the Diatom Seminavis robusta [DiatomTrack source code]
(Universität Kassel, 2026-03-13) Ziebarth, Jonas
This repository belongs to the observation experiments for tracking lineages of Seminavis robusta. The connected work reconstructed the lineages and obtained individuals' timing of division and motility. Cells were entrained for 5 days prior under white light with 25 µmol phot / s in a 12:12 hour rhythm. The images were recorded with a modified Leica microscope (DMIL) and 770 nm light source and a band pass filter. Entrainment continued for the time of recording.
The software analyzes images to identify cells within each frame and extract their trajectories. It constructs cell lineages from these trajectories and allows for their subsequent editing. The processed results can be visualized and modified through a user interface. The workflow consists of three main steps: segmentation of raw images, cell tracking, and manual editing. Progress can be saved and reloaded at any stage. Upon completion of the final step, the software outputs at least a video file and a CSV file. The CSV file contains extracted data points organized by frame, along with unique identifiers that assign each point to a cell of a lineage tree.
The corresponding work defined the cell cycle as beginning at the first frame in which a cell was detected and concluding at the frame in which a child was identified.
A full documentation of the software is available under documentation.zip.
Use: Run main.py. For details see User_manual.
License: The tracking implementation adapts the SORT (Simple Online and Realtime Tracking) algorithm of Alex Bewley et al. Accordingly, this repository is distributed under the GPL-3.0 License. See LICENSE for details.
Lineage Tracking and Asymmetric Inheritance Patterns of the Diatom Seminavis robusta [U-Net Training source code]
(Universität Kassel, 2026-03-13) Ziebarth, Jonas
This repository belongs to the observation experiments for tracking lineages of Seminavis robusta. The connected work reconstructed the lineages and obtained individuals' timing of division and motility. Cells were entrained for 5 days prior under white light with 25 µmol phot / s in a 12:12 hour rhythm. The images were recorded with a modified Leica microscope (DMIL) and 770 nm light source and a band pass filter. Entrainment continued for the time of recording.
This repository contains code for training a U-Net model to segment microscopy images into three classes: cell aggregates, single cells, and background. The implementation is adapted from Lee et al. (DOI: 10.1038/s41598-022-12532-7), originally published under the MIT License. During training, the code computes standard and custom segmentation metrics at user-defined intervals (in a separate process to avoid I/O bottlenecks). After the final epoch, these metrics are automatically plotted and saved to the output directory. The provided version includes image resizing functionality and uses segmentation masks generated by tools developed for this work. Furthermore, code for testing on unseen data, all (pickled) metrics and training results, and image data are provided.
Use: Configure paths (training data/output directory), learning rate, epochs, and metric calculation frequency in config.py. Set the random seed and CUDA devices in main.py. Run main.py from IDE.
License: This repository is licensed under the European Union Public Licence v. 1.2 (EUPL-1.2). Portions of the code originating from Lee et al. are licensed under the MIT License.
In accordance with the respective licensing requirements, both license texts are included in this repository. See the LICENCE (EUPL-1.2) and LICENCE_MIT files for details.
Lineage Tracking and Asymmetric Inheritance Patterns of the Diatom Seminavis robusta [Training image mask generation source code]
(Universität Kassel, 2026-03-13) Ziebarth, Jonas
This repository belongs to the observation experiments for tracking lineages of Seminavis robusta. The connected work reconstructed the lineages and obtained individuals' timing of division and motility. Cells were entrained for 5 days prior under white light with 25 µmol phot / s in a 12:12 hour rhythm. The images were recorded with a modified Leica microscope (DMIL) and 770 nm light source and a band pass filter. Entrainment continued for the time of recording.
The provided code automates the creation of training and test datasets for the training of U-Net. It generates the required directory structure at the location of the raw data and produces binary masks as PNG. The masks encode a coarse and fine label for the two object classes (cell aggregates and single cell).
Use: Specify the path to the raw data in main.py. Optionally, adjust the parameters of TrainingImageGenerator to suit your data; the default settings already yield satisfactory results for the example dataset. Run main.py.
License: This code is licensed under the European Union Public Licence v. 1.2. See the LICENSE file for details.