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

Statistical Framework not only for Precision Livestock Management and Ethology, but for performing Concordance Analysis and MSA for Scoring Data
(Universität Kassel, 2026-07-06) Kulig, Boris; Schäfer, Bertram; ADDO, SOWAH; Wilczek, Ulrike; Lange, Anita; Hensel, Oliver; Jung, Lisa
This repository contains a comprehensive, open-source statistical framework developed at the Department of Agricultural and Biosystems Engineering (University of Kassel). It is specifically designed to evaluate the reliability, consistency, and diagnostic accuracy of human observers and sensor systems in ethology, precision livestock farming, and beyond. Because human observation inevitably contains cognitive noise, this toolset provides robust mathematical methods to validate these "imperfect gold standards".

The framework is divided into two highly specialized modules

Module 1: Concordance Analysis Framework:
This framework is designed for the broad evaluation of inter-rater and intra-rater reliability for scoring data. It handles categorical, ordinal, and metric data, offering robust chance-corrected metrics (including Cohen’s Kappa, Gwet’s AC, Krippendorff’s Alpha, and PABAK), as well as metric equivalence tests (Deming Regression, Bland-Altman Analysis, and TOST). To ensure robust statistical inference, it calculates asymmetric confidence intervals via percentile bootstrapping. Beyond global omnibus metrics, the framework conducts detailed pairwise analyses to trace specific rater disagreements and classwise analyses to pinpoint exactly which score categories carry the highest uncertainty. For in-depth graphical exploration, it implements Bangdiwala’s B Agreement Plot, providing an intuitive visual approach to identifying systematic diagnostic deviations and marginal imbalances.
Module 2: Attributive Gage R&R and "Noisy Labels" Framework:
This framework performs a deep Measurement System Analysis (MSA) specifically tailored for ordinal scoring data. It mathematically partitions human measurement error into Repeatability (intra-rater cognitive noise) and Reproducibility (inter-rater variation). Furthermore, it identifies systematic bias using dynamic control limits (Variability Charts) and automated algorithm-based diagnostics (e.g., contrasting Kendall’s W against Cohen’s Kappa). Finally, the framework aggregates the filtered rater data to export a "Global Consensus" dataset. These mathematically purified "Noisy Labels" are optimized as ground-truth data for training Machine Learning algorithms or validating technical sensor systems.

Technical Implementation & Open Science

Championing the principles of Open Science, the entire framework is built on R, a free and open-source statistical programming language. It strictly separates backend calculation from user configuration, requiring no advanced programming skills from the end-user. Utilizing the power of Quarto and LaTeX, the toolchain automatically translates the statistical outputs into fully formatted, publication-ready PDF reports. These dynamic documents contain all relevant cross-tabulations, advanced visualizations, and an automatically generated methodological decision-making guide, ensuring maximum transparency and full reproducibility for the scientific community.

Invitation for Collaboration and Error Correction

To conclusively validate the reliability and mathematical limits of these heuristics, further research is imperative. In particular, the Gage R&R tool must be systematically stress-tested through comprehensive Monte Carlo simulations across broad parameter ranges. This entails stochastically securing the behavior of the control limits and bias algorithms under varying scale widths, extreme shifts in prevalence, and different rater team sizes. The data simulator already implemented within the scope of this project provides an initial foundation for this in the spirit of Open Science.

Pending full simulative validation, the diagnoses derived from the framework should therefore always be interpreted as exploratory decision-making aids. They possess the potential to significantly enrich the critical discourse between ethologists and data scientists during the generation of ground-truth data, but they do not replace a professional plausibility check of the resulting machine learning dataset.

The present framework is not intended as a static final product, but rather as a dynamic, iteratively growing tool in the spirit of Open Science. Despite careful algorithmic implementation and extensive test-driven validation via stochastic simulations, unforeseen edge cases can always arise when analyzing complex field data. In particular, experimental heuristics - such as the transfer of control limits to ordinal scales or automated bias diagnostics - benefit enormously from practical application and stress-testing across diverse diagnostic contexts.

Therefore, users, researchers, and data scientists are explicitly invited to critically review the provided scripts and Quarto templates and to adapt them to their own specific research questions. Feedback regarding methodological inaccuracies, programming errors (bugs), or suggestions for functional extensions - such as the integration of alternative concordance measures or specific weighting matrices for novel animal welfare indicators - is highly welcome. Through this open, interdisciplinary exchange, the framework will be continuously refined in order to sustainably and collaboratively bridge the methodological gap between practical ethology and robust data science.
Interaktionskompetenz in mündlichen DaF/DaZ-Prüfungen (A1–C2) – Aufgabenformate und Bewertungskriterien (Goethe-Institut, ÖSD, telc)
(Universität Kassel) Balker, Liza; Hummel, Maria
Der Datensatz enthält eine tabellarische Übersicht über analysierte standardisierte Prüfungen in Deutsch als Fremd- und Zweitsprache (DaF/DaZ). Er dokumentiert die Aufgabenformate und Bewertungskriterien der mündlichen Prüfungsteile ausgewählter standardisierter DaF-/DaZ-Prüfungen. Die Datengrundlage dient der Untersuchung, ob und in welcher Weise mündliche Interaktionskompetenz in diesen Prüfungen erfasst und bewertet wird. Im Fokus stehen dabei zwei Fragestellungen: (1) Ob und in welchem Umfang mündliche Interaktionskompetenz in den analysierten Prüfungen berücksichtigt wird und (2) welche Facetten der Interaktionskompetenz durch die jeweiligen Aufgabenformate und Bewertungskriterien elizitiert bzw. bewertet werden.
Supplementary Videos — Annular Optical MEMS Shutter Array (Ring Shutter) with Subfield Addressing for Angle-Tunable Illumination in High-Resolution Coherence Scanning Interferometry
(Universität Kassel) Kästner, Philipp
This repository contains three supplementary videos (SV) that supplement the doctoral thesis listed below. Each video is referenced at a specific place in the thesis via a printed QR code; scanning the code resolves to this repository, where all three files are stored together.

Kaestner_RingShutter_SV1_LayoutGenerationAlgorithm.mp4: Animated visualization of the concentric tessellation algorithm generating the full ring-shutter MEMS layout: radial/azimuthal step sizes are computed and each subring is filled with MEMS shutters at optimized fill factor and homogeneity, built from the inner radius outward. Thesis reference: Section 3.1.2; Figure 3-6 (see also Appendix F, Algorithm F-1).

Kaestner_RingShutter_SV2_3DModelRotation.mp4: Rendered 3D model of the device with resolved MEMS shutter blades in a dynamic oblique view (camera circling the centre), emphasizing radial uniformity and azimuthal isotropy. Shown in an exemplary actuation state with subfield (D,1) closed. Thesis reference: Section 4.1; Figure 4-3 a).

Kaestner_RingShutter_SV3_SubfieldSwitchingDemo.mp4: Experimental demonstration of the fabricated proof-of-concept ring shutter performing dynamic spatial light modulation: robust, fully reversible switching of well-defined annular-sector subfields, captured with the CMOS camera in the optical actuation setup. Three-potential parallel actuation applied: HV ±35 V and GND 0 V, yielding a 70 V differential (+35/−35 V) across the selected subfield while non-selected subfields stay sub-threshold at ≤ 35 V vs. GND. Thesis reference: Section 4.3.2; Figure 4-20 d) (see also Appendix B).

Related thesis: P. Kästner, "Development and Characterization of an Annular Optical MEMS Shutter Array with Subfield Addressing for Angle-Tunable Illumination in High-Resolution Coherence Scanning Interferometry," Ph.D. dissertation, Fac. Elect. Eng. Comput. Sci. (FB 16), Univ. Kassel, Kassel, Germany, 2026. (submitted)
Etablierung urbaner Saatgutsysteme [Daten]
(Universität Kassel) Zollinger, Robert Theodor
Der Datensatz enthält den tabellarischen Anhang einer Dissertation zu informellen Saatgutsystemen, Sortenwahl und reproduktiven Praktiken in urbanen und periurbanen Gemeinschaftsgärten in der Schweiz. Das zugrunde liegende Forschungsprojekt untersucht, welche Bedeutung urbane Gartengemeinschaften für die Nutzung, Erhaltung und Weiterentwicklung pflanzengenetischer Ressourcen haben und wie diese Praktiken freiraumplanerisch eingeordnet werden können.

Die Daten wurden im Rahmen eines mehrjährigen empirischen Forschungsprozesses erhoben. Grundlage bilden pflanzensoziologische Vegetationsaufnahmen in ausgewählten Gemeinschaftsgärten, ergänzende Beobachtungen gärtnerischer Praxis sowie projektspezifische Erhebungen zu Sortenwahl, Akzessionsnutzung und Saatgutpraktiken. Die Vegetationsaufnahmen wurden zwischen Frühjahr 2018 und Herbst 2021 in zehn urbanen und periurbanen Gemeinschaftsgärten des Deutschschweizer Mittellands durchgeführt. Insgesamt wurden 302 Vegetationsaufnahmen erhoben, saisonal gegliedert und in synthetischer wie differenzierter Tabellenform dokumentiert. Ergänzend enthält der Datensatz Angaben zur Herkunft, Auswahl und gruppenspezifischen Nutzung von insgesamt 173 Akzessionen im Projekt Samengemeinschaftszucht (Sagezu).

Die Tabellen ordnen und verdichten die erhobenen Daten nach saisonalen, räumlichen und typologischen Gesichtspunkten. Sie dienen als empirische Grundlage für die Analyse gärtnerischer Nutzungsmuster, die Identifikation informeller Saatgutsysteme und die Beschreibung reproduktiver Praktiken im urbanen Raum. Der Datensatz umfasst eine Gesamt-PDF-Datei des tabellarischen Anhangs sowie die zugehörigen Tabellen zusätzlich als einzelne Excel-Dateien. Die Excel-Dateien ermöglichen eine eigenständige Nachvollziehbarkeit der tabellarischen Auswertungen und erleichtern die gezielte wissenschaftliche Nutzung der Daten.
Micromagnetic Sensor Dataset for Non-Destructive Characterization of Residual Stress and Hardness in Hard-Turned 51CrV4 Steel
(Universität Kassel) Wittich, Felix; Kroll, Andreas; Krochmal, Marcel; Liehr, Alexander; Bolender, Artjom; Degener, Sebastian; Niendorf, Thomas
This data set contains measurement data from a micromagnetic (MM) sensor that allows for non-destructive, in-process material characterization of the mechanical properties of ferromagnetic components. It contains the raw sensor signals and 82 features that were extracted from these signals as input variables and corresponding reference measurements of the residual stress and hardness as corresponding outputs. The data set contains two subsets: data set 1 (DS1) with 81 data points and data set 2 (DS2) with 60 data points.