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

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.
[Software] Optimising Constant Matrix Multiplication Circuits is NP-complete
(Universität Kassel) Fiege, Nicolai; Lange, Martin
This is the Lean 4 code used to mechanically verify the proof of NP-completeness for the Constant Matrix Multiplication (CMM) problem. The project is under active development under https://gitlab.uni-kassel.de/uk025743/leancmm. The original paper "Optimising Constant Matrix Multiplication Circuits is NP-complete" was presented at the Formal Methods in Computer-Aided Design (FMCAD) conference in 2026, Graz, Austria.
Forschungsdaten zur Dissertation: Das Recht auf Bildung - rumänische Roma zwischen Anspruch und Realität Eine Studie zu Diskriminierung als Ursache für die persistente Bildungsarmut von Roma in Rumänien
(Universität Kassel) Druschel, Julia
Familieninterviews von Roma-Familien zum Schulerleben sowie Gruppendiskussionen mit Lehrkräften zur Beschulung von Roma-Kindern in Rumänien