KU Leuven Research Data Repository
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Replication Data for: Age-related changes in proprioception are of limited size, outcome-dependent and task-dependent.
No full textOur ability to sense of the position and movement of our limbs is essential for all activities of daily living. This ability arises from the signal sent by muscle spindles to the brain. While there is clear evidence for age-related changes in the quantity of muscle spindles and in their sensitivity, behavioral assessment of age-related changes in position sense have produced mixed findings. Yet, study results are difficult to compare since there is no golden standard for assessment of proprioception. Therefore, we measured upper limb proprioception across several standard proprioceptive tasks together with key factors that could influence behavioral results such as touch, motor function, and cognition in 37 young (19-32 years old) and 35 older (53-71 years old) adults. We tested age-related differences in behavioral outcomes and their associations across tasks. Our results showed that age-related effects were very variable across position sense tasks, ranging from tasks where older participants performed better to tasks where they exhibit large age-related differences in position sense. These results were confirmed by meta-analysis based on data from hundreds of participants tested in our laboratory on the exact same tasks. Even within a task, different outcomes exhibit opposite age-related effects. Associations between outcome variables across or within proprioceptive tasks were overall negligible to weak. In conclusion, age-related changes in proprioception are limited, task- and outcome- dependent, and current tasks used to assess proprioception do not provide consistent evidence of age- related impairment in upper limb proprioception
EV charging datasets
No full textThis dataset consists of measured data collected as part of a research entitled "Quantifying the System Efficiency of AC and DC fast Charging Technologies for Electric Vehicles'' submitted recently to IEEE Transaction on Transportation Electrification. It contains EV charging session values including battery parameters (voltage, current, temperature) and charger power values for a real car tested at EnergyVille research center. Some data are averaged while some files contain 1 second resolution raw data
Replication Data for: Strong Anonymity is not Enough: Introducing Fault Tolerance to Planet-Scale Anonymous Communication Systems
No full textPlanet-Scale Public Cloud Evaluation of FTMix, Vuvuzela, and Pung
Related publication: "Strong Anonymity is not Enough: Introducing Fault Tolerance to Planet-Scale Anonymous Communication Systems".
This repository lists the Anonymous Communication Systems (ACS), used configurations, and obtained results from our 2019 evaluation of FTMix (formerly zeno), Vuvuzela, and Pung.
Current Anonymous Communication Systems (ACS) lack fault tolerance and thus risk becoming unavailable when failures occur, forcing users offline or to less private messengers. In this work, we evaluate end-to-end message transmission latencies and resource demands of state-of-the-art mixnet Vuvuzela and CPIR system Pung under different network failure scenarios on an ACS test bed across four continents. We compare Vuvuzela and Pung to proof-of-concept mixnet FTMix that we equip with simple fault tolerance measures. Our analysis shows that FTMix maintains the smallest divergence of end-to-end latencies under failures from their respective baseline among all three ACS, while also achieving a balanced resource consumption trade-off. Thus, we consider fault tolerance effective in ensuring service availability and a crucial design principle for future ACS proposals
Verilog dataset from "A Deep Learning Framework for Verilog Autocompletion Towards Design and Verification Automation"
No full textDataset from "A Deep Learning Framework for Verilog Autocompletion Towards Design and Verification Automation", which was first presented as a WiP paper at DAC 2023 and now accepted to the IEEE SOCC 2025 special session on "AI-Enhanced Semiconductor Manufacturing: Intelligent Solutions for Next-Generation Fabrication".
To address the scarcity of publicly available Verilog code for training machine learning models, this study introduces a novel dataset specifically curated for Verilog autocompletion tasks. The dataset comprises over 100k Verilog files and 140k code snippets sourced from open-source repositories with permissive licenses (a list of which is available in permissive_all_deduplicated_repos.csv). It includes three subsets: file-level data, snippet-level data, and labeled definition-body pairs, each split into training, validation, and test sets. The dataset was meticulously filtered to remove autogenerated content, non-compliant licenses, and near-duplicate files, ensuring high-quality and diverse training material. Snippets were extracted using regular expressions, and additional quality control was applied by selecting files from repositories with at least one GitHub star for evaluation splits. This dataset serves as the foundation for fine-tuning pretrained language models toward Verilog code generation, enabling more effective automation in electronic design and verification workflows.
More details about the dataset process can be found in the related research paper. A zipped copy of the github repository (https://github.com/99EnriqueD/verilog_autocompletion) containing code to replicate the dataset creation process has also been included in this dataset
Replication Data for: MATWI: A Multimodal Automatic Tool Wear Inspection dataset
No full textMATWI, a new publicly available multimodal dataset for automatic tool wear inspection containing both images and captured sensor data during CNC milling processes. This dataset contains 17 tools ran to failure with 100 measurements over the lifespan, resulting in a large and unique dataset for tool wear estimation. It is also the first dataset that combines accelerometer, microphone and force measurements with image data captured along the tool life
Scholars@OldLouvain
No full textThe list aims at a complete overview by list of the scholars at the University of Louvain (1425-1797)
Replication Data for: Public Perception and Willingness to Accept Somatic Gene Therapy: A Belgian Survey Study
No full textThis dataset and supplementary materials are related to Public Perception and Willingness to Accept Somatic Gene Therapy: A Belgian Survey Study, which is to be submitted to Open Research Europe. The questionnaire assessed participants’ knowledge of gene therapy and their willingness to accept treatment using vignettes that varied in treatment characteristics, patient age, and symptomatic status. The supplementary materials include the cleaned survey data, the questionnaire in Word format, and the educational video, while the readme.txt file provides an overview of the contents of each file
Dataset for System Layout and Grasp Efficiency Optimization for a Multi-Robot Waste Sorting System.
No full textThis dataset contains instances used to validate a novel scheduling algorithm for solving a multi-robot, multi-gripper TD-PnP-TW problem. It includes 10,000 synthetically generated items, modeled with characteristics derived from a real-world sample
Replication Data for: Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
No full textIn the paper:
"Aghabagherloo, Alireza, Aydin Abadi, Sumanta Sarkar, Vishnu Asutosh Dasu, and Bart Preneel. 'Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models.' 2025 IEEE Security and Privacy Workshops (SPW), pp. 177-183. IEEE, 2025,"
We evaluated the effect of data duplication on standard and adversarially trained models using the widely used benchmark CIFAR-10 dataset. CIFAR-10 contains 60,000 color images across 10 classes for object recognition tasks. Duplicated data was randomly selected from CIFAR-10 and repeated during training. We also explored the effect of duplicating data points selected from a Gaussian distribution
Replication Data for: Implementation of a data-driven model for mesh-induced error corrections in CFD simulations of stirred tanks
No full textThis repository contains (i) CFD simulation results for mesh independence validation, post-processing data of (ii) 8mm mesh size cases and (iii) 10mm mesh size cases, and (iv) prediction results from the trained ML model. The data consist of the three velocity components and turbulence quantities associated with the point ID of the numerical meshes. The aim of providing this data is to enable training of other machine learning models for error correction in coarse mesh CFD and their comparison with the presented approach