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

Research data from field trials and data analysis, comparing recurrent haploid selection, mass selection, full-sib selection, S2 selection and doubled haploid line selection in a sweet corn population
(Universität Kassel) Aichholz, Charlotte; Becker, Heiko C.; Horneburg, Bernd; Backes, Gunter
This dataset contains the research data underlying the dissertation “Comparison of selection methods in a sweet corn population”. The dissertation investigated the effects of different selection methods on marketable yield, plant development traits, ear quality traits and related agronomic traits in an open-pollinated sweet corn population.

The data were generated in field trials and selection experiments conducted over several years. The dataset includes raw data, cleaned datasets, R scripts, statistical outputs, graphical data and additional photographic documentation. The selection methods covered include recurrent haploid selection, positive mass selection, full-sib selection, S2 line selection, selection of doubled haploid lines produced via colchicine-induced chromosome doubling, and selection of spontaneously doubled haploid lines.

For Chapter 2, the dataset contains data from five cycles of recurrent haploid selection and five cycles of positive mass selection. The initial population and the selected cycles were evaluated in multiple environments under organic and conventional management conditions. Recorded traits include plant development traits such as plant number and vigour, ear quality traits such as ear length, ear diameter, tip fill, ear shape, colour and row number, as well as yield traits such as total yield, marketable yield, total number of ears and number of marketable ears.

For Chapters 3 and 4, the dataset contains data from the evaluation of one cycle of full-sib selection, one cycle of S2 line selection, and selection based on doubled haploid and spontaneously doubled haploid lines. The data include selection-year data, evaluation-year data, cleaned datasets from different trial locations, adjusted datasets, means used for graphical outputs, and R scripts used for data preparation, data cleaning and statistical analysis.

The analyses were carried out mainly in R. Statistical methods included analysis of variance, estimation of variance components, Tukey tests, calculation of heritability, adjustment of data for experimental design or environmental effects where applicable, and identification of influential outliers using Cook’s distance. The R scripts included in the archive document the main steps of data preparation and analysis and indicate which data files were used for the respective analyses.

The dataset is intended to make the results of the dissertation transparent, reproducible and verifiable. It provides the basis for the tables, figures and conclusions presented in the dissertation and associated publications. No personal or sensitive data are included.
[Dataset] Maturity model for assessing the sustainability of a production network
(Universität Kassel) Sutherland, Robin; Bohn, Sebastian; Wenzel, Sigrid
This dataset contains the supplementary material for the paper "Maturity model for assessing the sustainability of a production network".
In response to increasing resource scarcity and global societal trends, this research develops a feature-based maturity model to holistically assess the environmental, economic, and social sustainability of geographically dispersed production networks. The model provides a practical method for companies to determine their current maturity level, identify strategic weaknesses, and derive targeted recommendations to achieve long-term sustainability goals.

Contents:
- The complete maturity model, including dimensions, indicators, and levels (English and German versions).
- Detailed documentation of the selection process of the 24 specific indicators used in the model.
- The questionnaire used for the expert-based evaluation (available in German only).
- Anonymized data and results from the empirical evaluation (available in German only).
Georeferenced dataset on agroecological practices (2011–2020), NDVI, and rainfall in the Maradi Region, Niger (2001–2025)
(Universität Kassel) Abdoulkader, Djibo Amadou; Fastner, Kira; Wiehle, Martin; DIOUF, Abdoulaye
This dataset compiles georeferenced information on agroecological practices implemented in the Maradi Region of Niger between 2011 and 2020. It includes the geographic coordinates of intervention sites, types of agroecological practices, and implementation years. It also contains maximum Normalized Difference Vegetation Index (NDVImax) and Rainfall mean values calculated for the three years before and the three years after interventions for intervention sites and control sites.
The dataset also includes annual regional time series of NDVImax and rainfall for the Maradi Region, covering the period 2001–2025.

Dataset objectives
This dataset was compiled to:
- Assess annual rainfall and NDVImax trends in the Maradi Region between 2001 and 2025;
- Evaluate the contribution of agroecological practices to vegetation recovery using a Before–After Control–Impact (BACI) approach;
- Quantify the relative and interactive contributions of rainfall variability and agroecological interventions to NDVImax using regression-based attribution analysis.
Main variables
- Geographic coordinates of intervention sites.
- Year of implementation of agroecological interventions.
- Type of agroecological practice (Farmer Managed Natural Regeneration (FMNR), Zaï, half-moons, benches, dune fixation, development of grazing land, forage seeding)
- NDVImax mean values calculated for the three years before and the three years after interventions for intervention sites and control sites.
- Rainfall mean values calculated for the three years before and the three years after interventions for intervention sites and control sites.
- Annual regional NDVImax time series for the Maradi Region (2001–2025).
- Annual regional rainfall time series for the Maradi Region (2001–2025).

Data collection
Data on agroecological practices were obtained from records and databases provided by the Regional Directorate of Environment and the Regional Directorate of Agriculture of Maradi (Niger). These data were complemented with remote sensing data from Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat satellites for NDVImax derivation, as well as rainfall data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product extracted using Google Earth Engine.

Data analysis
Analyses were conducted in the R environment to assess vegetation and rainfall trends. The Before–After Control–Impact (BACI) approach was applied to evaluate the effects of agroecological practices on vegetation dynamics. Multiple regression analyses were subsequently performed to quantify the relationships between vegetation dynamics, rainfall variability, and agroecological interventions.

Data notes and limitations
The technical characteristics associated with agroecological practices (e.g., structure dimensions, spacing, or other implementation parameters) correspond to general specifications reported in the scientific literature. They do not necessarily represent site-specific measurements, as detailed implementation parameters were not systematically recorded for all intervention sites.
Repository content
The repository includes:
- Georeferenced data on agroecological practices;
- NDVI max mean and rainfall mean datasets for intervention and control sites, as well as annual regional NDVI max and rainfall time series;
- Datasets prepared for trend analyses and multiple regression analyses, BACI results (including NDVI before and after intervention, NDVI, relative NDVI increase, BACI index, and p-value)
- Files required for understanding the agroecological practices and analysis codes.

Data availability on request: The agroecological intervention data used in this study were compiled from information provided by the Regional Directorate of Environment and the Regional Directorate of Agriculture of Maradi (Niger) for scientific research purposes, complemented by data from the IRD/DataSuds dataset on degraded land reclamation actions (Sadda et al., 2024; DOI: 10.23708/ACJTAW). The compiled dataset is not publicly available. Access to the data may be possible upon reasonable request to Djibo Amadou Abdoulkader, subject to the applicable conditions established by the Regional Directorate of Environment and the Regional Directorate of Agriculture of Maradi (Niger), as well as the terms of use applicable to the IRD/DataSuds dataset.
Mapping and Classification of Field Margin Vegetation using High Resolution Satellite Imagery and Deep Learning Models in a Tropical Landscape
(Universität Kassel) PRAKASH, SATYA; Wachendorf, Michael; Nautiyal, Sunil; Wijesingha, Jayan
This data set is a supplementary material to the publication ''Mapping and Classification of Field Margin Vegetation using High Resolution Satellite Imagery and Deep Learning Models in a Tropical Landscape" (Prakash et al., 2026). This dataset contains the training, validation, and testing data used for the deep-learning-based classification of Field Margin Vegetation (FMV) using high-resolution WorldView-3 satellite imagery. The dataset was prepared for mapping field margin vegetation in agricultural landscape. The original Worldview-3 satellite imagery was obtained under a commercial data license and therefore cannot be redistributed publicly. The datasets provided here contain only the derived image chips and corresponding reference labels that are permitted to be shared.

The data are organized in three subsets:
Training dataset: Used for training the deep learning model (U-Net and Deep LabV3+)
Validation dataset: Used for monitoring model performance and model selection during training.
Testing dataset: Used independently to evaluate the performance and generalisation capability of the trained model.

Data Preparation:
The input data were derived from WorldView-3 multispectral imagery and divided into smaller chips for model development. Corresponding reference masks were prepared for the classification of FMV.

The datasets were used together with the source code provided in the associated repository to ensure transparency and reproducibility of the model-development workflow.

For further information, please refer to the Readme.txt file.
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.