DaKS - Datenrepository der Universität Kassel

DaKS ist das institutionelle Repositorium der Universität Kassel für Forschungsdaten. DaKS bietet die strukturierte Ablage von Forschungsdaten mit ihrer Beschreibung (Metadaten), die langfristige Archivierung von mindestens 10 Jahren und – falls gewünscht – die Veröffentlichung mit Angabe einer DOI.

DaKS wird von der Universitätsbibliothek und dem IT-Servicezentrum der Uni Kassel betreut und an der Philipps-Universität Marburg gehostet. Wir beraten Sie gern unter daks@uni-kassel.de.

Neuzugänge

Statistical Framework not only for Precision Livestock Management and Ethology, but for performing Sample Size Calculation for Binomially Distributed Traits, Incorporating Simple Sample Size Planning, Post-Hoc Analysis, Confidence Interval Inflation, Bayesian Integration of Prior Knowledge and Sampling Uncertainty
(Universität Kassel, 2026-10-15) Kulig, Boris; Bertram Schäfer; Ulrike Wilczek; 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). The collection of animal-related indicators as part of on-farm self-monitoring serves to ensure compliance with animal welfare standards and to inform evidence-based management decisions. Rigid specifications for sample sizes do not account for the stochastic nature of finite populations, varying baseline prevalences, and diagnostic uncertainties in practice. To address these limitations, the developed R software framework integrates deterministic, exact, and stochastic estimation methods into a modular architecture. The software package is divided into four specialized main tools (Tools A through D), which cover biometric experimental design, matrix generation, post-hoc precision control, and the modeling of diagnostic misclassifications, as well as additional help tools.

The framework is divided into four highly specialized modules

Tool A: Case-Specific Sample Size Calculation and Cluster Design
Tool A determines the minimum number of animals required for a specific survey design while simultaneously comparing four calculation methods (asymptotic normal approximation, Wilson score, exact binomial inversion, and Bayesian credibility interval).

Tool B: Matrix Generation for Experimental Design
Tool B transforms this logic into tabular reference matrices for variable inventory levels and accuracy requirements.

Tool C: Post-hoc Analysis with Fixed Assumptions
If discrepancies arise in the field between the assumed a priori prevalence and the observed frequency of findings, Tool C quantifies the resulting increase in the standard error due to quadratic variance linking.

Tool D: Modeling Epistemic Uncertainty and Auto-Tuning
Tool D addresses the epistemic measurement uncertainty of imperfect acquisition protocols (Se < 1.0, Sp < 1.0) using stochastic simulation algorithms.

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
Statistical mastery of attributive evaluation and measurement processes at the interface between animal ethology, veterinary medicine, and machine learning requires the continuous refinement of biometric algorithms. The R software framework presented here is intended as an open-source tool explicitly provided for critical review, methodological refinement, and collaborative expansion. The release of the source code under the CC BY-SA 4.0 license invites researchers in livestock management, biometrics, and data science to use the scripts in their own studies, adapt them, and contribute methodological improvements to the repository via the established peer-review process (pull requests).

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, there is a specific need for further development in the mathematical modeling of Tool D, which currently provides two computational approaches for capturing diagnostic uncertainties.

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.
A Competence Framework to Support Personnel-Oriented Factory Planning in Smart Factories [Repository]
(Universität Kassel) Wittine, Nicolas; Gliem, Deike; Sutherland, Robin; Wenzel, Sigrid
The associated paper explores the integration of qualitative personnel requirements into technical factory planning by providing a structured Q+KSAO (Qualifications, Knowledge, Skills, Abilities, and Other Characteristics) taxonomy and a systematic literature review (SLR) within the Smart Factory context. The review builds upon 1,304 sources, reduced to 91 relevant papers, to answer the following research questions:
1. How does work change in the Smart Factory?
2. Which competencies are relevant for the Smart Factory?
3. How is competence structured?
4. How is competence taught?

The provided file contains the researched literature and comprehensively documents the filter process and findings. The individual sheets detail the workflow, ranging from the initial search strings and raw data exports to the abstract screening and full-text analysis.
Biological data and model Matlab scripts from Zolmon, Trummel, et al., (2026): Disrupting the cockroach circadian clock through RNAi-mediated knockdown of clock proteins (Journal of Biological Rhythms).
(Universität Kassel) Zolmon, Huleg; Trummel, Tabea; Kräling, Lukas; Przybylla, Patrick; Schneider, Anna C.; Stursberg, Olaf; Stengl, Monika
# Biological and simulation data for Zolmon, Trummel, et al., 2026: Disrupting the cockroach circadian clock through RNAi-mediated knockdown of clock proteins (Journal of Biological Rhythms)

## File overview

This repository contains four files: One excel file with biological data and three Matlab files that contain the model code.

## Description Excel file with biological data

This excel file contains the data that is presented in Zolmon, Trummel, et al., 2026: Disrupting the cockroach circadian clock through RNAi-mediated knockdown of clock proteins (Journal of Biological Rhythms)

The data is organized in two sheets:
1. running wheel data,
2. qPCR data.

The sheet "running wheel data" contains 9 lines of metadata, an empty line, one line with column headers followed by the data. The first column is the day relative to dsRNA injection; the second column is the minute of that day, and subsequent columns contain wheel turns in 1 minute bins. Metadata contains (top to bottom): Unique animal identifier (animal ID); light condition as either constant darkness (DD) or constant light (LD); experiment category as either CYC only (CYC), CLK only (CLK), double knockdown (DKD), triple knockdown (TKD); knockdown treatment with the following targets GFP (as control), CLK, CYC, double knockdown (DKD: CLK + CYC), triple knockdown (TKD: PER, TIM1, CRY2) or WT (wild-type, no treatment); experimenter who performed the dsRNA injection and animal care in the running wheels; start date (DD.MM.YYYY) on which the running wheel assay was started; injection date (DD.MM.YYYY) on which dsRNA was injected; indication in which Figure the data from one particular animal was used as an illustrative example; indication as part of which Figure the data is displayed.

The sheet "qPCR data" contains 8 columns. The first column is the experiment ID; second column is the light condition in which the animals were kept, either constant darkness (DD), constant light (LL), or 12:12 hours light:dark (LD); third column is experiment category as either untreated wildtype randomly sampled from the breeding colony (WT), untreated wildtype with age-controlled sampling (WT_age_controlled), CLK knockdown (CLK_KD), CYC knockdown (CYC_KD); fourth column is knockdown treatment with the following targets: none (WT), GFP (as injection control), CLK, CYC, triple knockdown (TKD: PER, TIM1, CRY2); fifth column lists the day or zeitgeber time (ZT) or projected ZT of sacrifice for the qPCR; sixth column lists the target mRNA of the qPCR, seventh column lists the cycle threshold of the qPCR (red values were excluded from their triplets mean calculation); eighth column indicates in which figure the data point was used.

## Description Matlab files

This MATLAB code implements the discussed networks of the computational model to describe the cockroach circadian clock.

The code is divided into three different MATLAB scripts:

1. *clock\_network\_DD\_het.m*: This code simulates the cockroach clock, which is modeled by a heterarchical network. Effects of knockdown are investigated for constant darkness (DD).
2. *clock\_network\_DD\_hier.m*: The cockroach clock is modeled by a hierarchical network in constant darkness (DD).
3. *clock\_network\_LL\_het.m*: To examine the effects of knockdown in constant light (LL), the heterarchical network is adjusted according to LL conditions.

Each script can be run individually. The corresponding networks are represented by matrices using time-varying coupling gains. The parameters of the planar switching affine systems (PSAS), modeling the subsystems' dynamics, are given. The networked system can then be simulated. Finally, the simulation results of the computational model are visualized graphically.

For more details on modeling and discussion, see the corresponding paper.

### Software

* Version R2024b of MATLAB was used.
* Toolbox *Communications Toolbox* is requiered.
Agent Home Investment Decisions Stages (AHOIS): Baseline runs of variants DEZ, KWN, WWN, GPJ for Decision Theatre 2 (research project WAERMER)
(Universität Kassel, 2025-04-04) Holzhauer, Sascha; Digel, Ivan; Krebs, Friedrich
The challenge of climate change demands active political interventions. Private building energy retrofitting is one of the main fields such interventions focus on, as in the European Union households consume around a quarter of total produced energy. Finding an efficient way to propose and evaluate policies aiming to stimulate private energy retrofitting is a promising direction approached from different methodical perspectives, among which agent-based modelling is a yet an underdeveloped one.
This dataset is obtained from an agent-based model applying a psychologically grounded multi-stage algorithm to simulate houseowners’ decision-making related to heating system replacements. The model is applied to the district Kiel-Oppendorf to investigate fulfillment for four variants of heat supply (DEZ - decentral supply; KWN - cold local network; WWN - hot local network, GPJ - special hot local network). Plots and slides derived from these data were presented at a so-called decision theatre held on April 4th 2025 in Kiel involving relevant stakeholders such as intallers, house owners, energy advisers, grid operators and city officials.
This dataset contains baseline scenarios for the four variants. A second dataset contain data about application of interventions to increase fulfillment.
Numerical Simulations for ‘Autonomous Circadian Oscillators Intrinsic to Cell Membranes’
(Universität Kassel) Forlino, Mauro Ariel; Garcia, Martin Ezequiel; Piro, Oreste; Stengl, Monika
This repository contains the computational models and simulation code for the membrane-based circadian oscillator described in:

**"Autonomous circadian oscillators intrinsic to cell membranes"**
Mauro A. Forlino, Oreste Piro, Monika Stengl & Martin E. Garcia
https://doi.org/10.21203/rs.3.rs-8788655/v1

Numerical models for "Autonomous circadian oscillators intrinsic to cell membranes" (Forlino, Piro, Stengl, Garcia). Computational models supporting the hypothesis that cell membranes can host autonomous post-translational feedback loops (PTFLs) capable of generating circadian (~24 h) oscillations independently of nuclear transcription-translation feedback. Two cell types are modeled: anucleate red blood cells, using a Goodwin-type feedback architecture, and neurons, using a Hodgkin-Huxley framework with coupled fast positive and delayed negative feedback. Both models produce self-sustained ~24 h oscillations without nuclear input.