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

Infrared and visual in-situ measurements of components made from AISI 316L in Laser Beam Direct Energy Deposition
(Universität Kassel) Sommerlade, Lars; Hilberg, Alec; Stredak, Florian; Schmoll, Robert; Altenburg, Simon J.; Böhm, Stefan; Kroll, Andreas
The presented dataset comprises a measurement campaign conducted on the laser beam direct energy depositio (DED-LB/M) system LMD² of the University of Kassel. In DED-LB/M, metal powder is blown into a laser beam, where it is melted and deposited to build up material. Within this campaign, three specimens made of AISI 316L Powder were manufactured. Each specimen consists of ten adjacent deposited tracks, produced under identical process conditions. For the measurements, the build table was tilted by 55° to provide the cameras with an improved viewing angle of the process zone. The manufacturing process was recorded in-situ using three camera systems: a visible RGB camera (Basler ace 2 R PRO) and two midwave infrared (MWIR) cameras (InfraTec ImageIR 8380 hp & Telops MS M3k). The first MWIR infrared camera (InfraTec ImageIR 8380 hp) captured HDR single-frame images covering a temperature range from 300 to 2000 °C (BlackBody radiation, emissivity = 1) using various optical filters and integration times. The second MWIR infrared camera (Telops MS M3k) captured eight-frame images from eight different wavelength areas using eight different rotating optical filters. In addition, the visible RGB camera acquired HDR single-frame images at three different exposure times to robustly capture both very bright and darker regions of the scene.
Modellfabrik und Reifegradmodell für Digitale Zwillinge in Produktion und Logistik
(Universität Kassel) Gliem, Deike; Wenzel, Sigrid
Die Dokumentation umfasst zum einen eine systematische Literaturrecherche zur Identifikation bestehender Modellfabriken für Digitale Zwillinge. Insgesamt werden 17 Modellfabriken herangezogen, um eine Handlungsempfehlung zur Anschaffung einer Modellfabrik für Digitale Zwillinge in Produktion und Logistik am Fachgebiet pfp der Universität Kassel auszusprechen. Die Ergebnisse entstanden in Zusammenarbeit mit Luca Rehs (Rehs, Luca-Joshua: Konzeptionierung einer Modellfabrik zur Untersuchung Digitaler Logistikzwillinge. Bachelorarbeit, Universität Kassel, Studiengang Maschinenbau, 05/2024 (Prof. Wenzel / Gliem, FB 15, Universität Kassel)). Die Dokumentation umfasst zum anderen eine weitere systematische Literaturrecherche zur Identifikation bestehender Reifegradmodelle für Digitale Zwillinge. Insgesamt werden 31 Modelle herangezogen, um ein eigenes Reifegradmodell für Digitale Zwillinge in Produktion und Logistik abzuleiten. Das entwickelte Modell ist speziell auf die Anforderungen und Rahmenbedingungen von kleinen und mittleren Unternehmen (KMU) zugeschnitten. Die Struktur des Reifegradmodells wird vollständig beschrieben und umfasst vier Dimensionen mit jeweils vier Indikatoren, anhand derer die Reife eines Digitalen Zwillings bewertet werden kann. Zusätzlich wird die praktische Anwendung des Modells anhand von drei Use Cases demonstriert, die mögliche Einsatzszenarien in industriellen Umgebungen abbilden.
Kartierungsergebnisse der krautigen Vegetation der Park-/Grünanlagen der Stadt Freiburg i. Br. [Daten]
(Universität Kassel, 2025) Barthelmes, Beatrice
Kartierungsergebnisse der krautigen Vegetation der Park-/Grünanlagen der Stadt Freiburg i. Br. Insgesamt wurden in elf untersuchten Park-/Grünanlagen der Stadt Freiburg i. Br. 127 Aufnahmequadrate für die Vegetationserfassung etabliert. Es wurden extensiv gepflegte (Wiesen und Langgrasflächen) und intensiv gepflegte Bereiche (Rasen) untersucht. Die Kategorien der untersuchten Flächen sind dabei folgende: Wiesen: Bereiche, die in der Regel mit 1-2 Mahdvorgängen im Jahr extensiv gepflegt werden. Das Mahdgut wird abgeräumt. Langgrasflächen: Bereiche, die in der Regel mit 1-2 Mahdvorgängen im Jahr extensiv gepflegt werden. Das Mahdgut verbleibt auf der Fläche. Gebrauchsrasen/Spielrasen: Bereiche, die in regelmäßigen Abständen während der Vegetationsperiode gemulcht werden. Zusätzlich zur Artenvielfalt wurden nach der international verwendeten Aufnahmen-Skala von Braun-Blanquet die Artmächtigkeiten (Menge) und die Mächtigkeiten der blühenden Arten erfasst. Hierbei wurde die ursprüngliche Schätzskala von Braun-Blanquet verwendet. Die Kartierungsjahre waren 2020 und 2021.
Mechanical Properties of Normal Concrete
(Universität Kassel) Rezazadeh, Farzad; Dürrbaum, Axel; Abrishambaf, Amin; Zimmermann, Gregor; Kroll, Andreas
Normal concrete is the most widely used form of concrete, and its mechanical properties can vary due to variations in raw-material quality, dosage errors, and changes in material storage, mixing, and curing conditions, even when a fixed reference mix design is used. This variability constitutes a reproducibility challenge for concrete production under fixed formulations. This dataset examines the effects of variations in raw-material condition and process parameters on the mechanical properties of normal concrete produced from a base mix design targeting a 65 MPa compressive strength. The dataset comprises 32 systematically designed experiments. Compressive strength was measured at 1 day (24 hours), 7 days, and 28 days after mixing. Where available, the reported values represent the average of three specimens per experiment. In addition, five fresh-state properties were measured immediately after each mixing process (temperature, electrical conductivity sensor reading, slump-flow, V-funnel flow time, and air content). All experiments were conducted in the laboratory of G.tecz Engineering GmbH under controlled conditions using the same mixer, mixing tools, and personnel. The dataset provides high-dimensional experimental data with a limited number of observations and is suitable for developing and evaluating regression models in sparse scenarios.
Mechanical Properties of Ultra-High Performance Concrete (UHPC)
(Universität Kassel) Rezazadeh, Farzad; Dürrbaum, Axel; Abrishambaf, Amin; Zimmermann, Gregor; Kroll, Andreas
Ultra-high-performance concrete (UHPC) possesses mechanical characteristics that significantly outperform traditional concrete. However, replicating these properties consistently across different production batches—even when using the same recipe—remains a challenge. This dataset examines how variations in raw materials, environmental conditions, dosage variation, and both mixing and curing practices affect the mechanical properties of UHPC produced from a single reference formulation. Designed according to a three-phase design of experiments methodology, the dataset comprises 150 systematically planned experiments, offering a comprehensive view of the multiple factors influencing UHPC quality. Measurements of compressive and flexural strengths are provided at 24 hours and after 28 days post-mixing. Beside the mechanical properties, the dataset includes five characteristics of the fresh state, measured directly after each mixing process. All experiments are conducted in the laboratory of G.tec Engineering GmbH under controlled conditions, using the same mixer, same mixing tool, and the same team of technicians. The environment is maintained at a constant temperature of 20 °C throughout the experimental process. From each experiment, three specimens are cured under the designed conditions. First, the flexural strength is measured by carefully halving each of the three specimens. Then, the resulting six halves are used to measure the compressive strength. Finally, after a careful analysis of the results from each specimen, the averages for flexural and compressive strengths are reported. The dataset also includes outliers. After analysis by UHPC experts and the data science team, 11 data points (numbered 5, 17, 30, 36, 41, 47, 57, 99, 101, 128, and 148) were identified and removed as outliers to assure data quality. By offering a structured collection of high-dimensional data and a relatively small data size, this dataset is particularly suitable for advanced regression analyses, notably those addressing sparse data scenarios.