KU Leuven Research Data Repository
Not a member yet
662 research outputs found
Sort by
Replication Data for Altruistic motivation and monetary incentives for blood donation
No full textData for the PhD dissertation "Selling the gift of life? Altruistic motivation and monetary incentives for blood donation". The excel files include data and analyses of 4 surveys in Flanders and Germany, two randomized field experiments at Belgian Red Cross-Flanders and one meta-analysis about cost-effectiveness of monetary incentives for blood donation. The studies were conducted between 2020 and 2024
An In-Depth Security Evaluation of the Nintendo DSi Gaming Console
No full textThis repository contains the source code written as part of the research into the Nintendo DSi. It mainly consists of C code that can be compiled using the supplied Makefiles, though you need an arm-none-eabi toolchain to compile it (preferably devkitARM r61). The `dumphi` folder contains C and assembly payload code used to dump the ARM7 and ARM9 boot ROMs. The `unicorn` folder contains Python scripts that use the Unicorn engine to test the behavior of certain snippets of the ARM9 boot ROM code. The `haxboardfw` folder, finally, contains all code related to the modchip exploit: RP2040 microcontroller code in the `rp2040` subfolder, DSi payload (incl. ROP chain) in `payload`, and auxiliary test code in `faketwl`
Replication Data for: Better Alone Than in Bad Company: Addressing the risks of companion chatbots through data protection by design
No full textThis dataset contains the (i) the full text of the complaint filed before the Belgian data protection authority against Chai Research Corp., (ii) an English machine translation of that complaint, and (iii) all the evidence submitted along the complaint, ranging from E1 to E10
A reference database for the analysis of wind-farm wake recovery mechanisms in a large-eddy simulation framework
No full textNote: we recommend switching the view from 'Table' to 'Tree' when exploring the dataset. Further, we refer to https://www.kuleuven.be/rdm/en/rdr/large-downloads for efficient download options.
The dataset contains a suite of large-eddy simulation results of a wind farm operating in conventionally neutral boundary layers, in which atmospheric conditions are varied to study the wind-farm wake behaviour and its recovery mechanisms. A 1.6GW offshore wind farm with a fixed layout, composed of 160 IEA 10MW turbines, is considered for 4 different atmospheric stratification conditions. In particular, we initialize the simulations with four capping-inversion heights (i.e. 150, 300, 500 and 1000 m) while keeping the capping-inversion strength and free-atmosphere lapse rate fixed to 5 K and 4 K/km, respectively. All simulations are performed by using a concurrent precursor method. Hence, the inflow conditions in the main domain (the one containing the turbines) are provided by the flow fields generated in the precursor domain. Appropriate spin-ups are used (first in the precursor domain, and subsequently in precursor and main domains) to generate fully developed turbulence in the boundary layer. The dataset is generated with the SP-Wind code, an in-house LES and DNS code developed at KU Leuven.
The dataset is structured as follows. The results from the 4 simulations are organized into 4 separate folders. Each folder contains results obtained on both the precursor (stat_precursor_**.h5) and main (stat_main_**.h5) domains. There are 17 time-averaged flow fields per domain, which are categorized in first- and second-order statistics, further divided into resolved and sub-grid scale components. The flow fields have dimensions of Nx x Ny x Nz, where Nx, Ny and Nz are the number of grid points in the streamwise, spanwise and vertical directions used in the respective domain. Note that these flow fields are time-averaged over the last 2 hours of the simulation. Finally, the turbine_data.h5 file contains information about the thrust, power and orientation of all turbines in the farm. For more information, we refer to the readme.txt file located in the dataset.
Acknowledgements
The authors acknowledge support from the Research Foundation Flanders (FWO, Grant No. G0B1518N), from the project FREEWIND, funded by the Energy Transition Fund of the Belgian Federal Public Service for Economy, SMEs, and Energy (FOD Economie, K.M.O., Middenstand en Energie) and from the European Union Horizon Europe Framework programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205. The computational resources and services in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government department EWI.
References
Lanzilao, L. & Meyers, J. (2024), Wind-farm wake recovery mechanisms in conventionally neutral boundary layers. Manuscript submitted to the J. Fluid Mech. and currently available on arXiv, https://arxiv.org/abs/2407.17198.</p
UNSILENCE Teaching material dataset
No full textThis datasete include a game destined to high-school and university students. This dataset is linked to Public Engagement Work Package 4 of the MSCA project "UNSILENCE: Trafficking of girls and Catholic missionary networks in the South China Sea (18th-19th centuries): a transnational approach". The creation of thIs game serves to raise awareness about human trafficking, decolonisation in Asia and the importance of history in dealing with it. The dataset include the following files: 1. The rules and narration of UNSILENCE game. 2. A map that will help the players and storyteller to know the locations; and 3. the character sheets that will help the players to play UNSILENCE game. It also includes the README file to introduce and explain the project and the information required to understand this dataset
Longitudinal data of muscle impairments in boys with Duchenne muscular dystrophy
No full textThis data contains longitudinal data on muscle impairments, consisting of muscle strength, range of motion and muscle cross-sectional area, of boys with Duchenne muscular dystroph
Replication Data for: "From biological data to oscillator models using SINDy"
No full textArchived version of data needed to reproduce result figures shown in the manuscript: "From biological data to oscillator models using SINDy". The data contains used experimental data which has been taken from literature (BZ reaction, glycolytic oscillations) and self-generated (simple pendulum). The other data contains generated results for different generic oscillator models (Fitzhugh, Goodwin, Mass Action) and SINDy extensions (SINDy-PI). More details can be found in the ReadMe file
Replication Data for: A spatially-explicit sensitivity analysis of urban definitions: uncovering implicit assumptions in the Degree of Urbanisation
No full textThis repository contains the R code and data needed to replicate the analysis in our paper "A spatially-explicit sensitivity analysis of urban definitions: uncovering implicit assumptions in the Degree of Urbanisation". All code is organized in computational notebooks, arranged sequentially in the code folder. The analyses mainly rely on the open-source data products provided by the Global Human Settlement Layer. Other data sets needed to reproduce the analyses are included in the data folder. The resulting grid classifications of the 1458 alternative realisations of the Degree of Urbanisation are included in the results/grid_classifications folder. The metadata (e.g. criterion settings) associated with each realisation is included in the data/criterion_ID.csv file. The final sensitivity map and all grid classifications are also visualised on this web-platform. For further details, please refer to the enclosed README file and the original publication
Optimization for the region- and variable-specific adjustments to the Lamb Weather Type Classification
No full textThese python scripts allow to determine the region-and variable-specific threshold for the weak-strong separation in the Lamb Weather Type classification. Included are the function that includes the optimization metric (Function_CHI), the function that determines the optimization metric for a given threshold (Function_Determining_ws_separation) and the script to perform the optimization (Optimizing_ws_separation). To determine the threshold for the weak-strong separation, only "Optimizing_ws_separation" should be run
Replication Data for: EDSAF: Event-Driven Surrogate-Assisted Framework for Real-time Optimization.
No full textThe input configuration and resulting metrics from several optimization algorithms applied to synthetic data generator. The configuration is described in config.yaml and setup.yaml, making it fully reproducibly through the shared code. The resulting metrics are dependent on these used config but also the machine used for the experiment