Types of repositories
Schuh, Sonja
2025-12-05 Image Restricted
Here should be a totally terrific description of my data set.
anonymized
Uploaded on December 5, 2025
Welcome to TU Wien Research Data (Test Instance)
TU Wien Research Data (Test Instance) is an institutional repository of TU Wien to enable storing, sharing and publishing of digital objects, in particular research data. It facilitates the funders' requirements for open access to research data and the FAIR principles by making research output findable, accessible, interoperable and re-usable. It is developed by the TU Wien Center for Research Data Management and hosted by Campus IT on servers in Vienna, Austria.
The repository uses persistent identifiers and synchronises with data hubs such as DataCite, OpenAIRE, BASE, and search services like Google Dataset Search. Thus, it maximizes the visibility of the uploaded content. TU Wien Research Data (Test Instance) is also listed in FAIRsharing and re3data - registries many funders refer to.
Deposit research outputs openly, restricted or closed. A landing page provides information on your digital objects, download links, license and persistent identifier.
Search datasets published by other users.
For any questions regarding this service, please refer to our FAQ and our Upload Guide.
To get in touch with our team, please visit our 'Contact' page.
Recent Uploads
Wine Dataset for Machine Learning Classification Experiment
Bhardwaj, Aman
2025-11-30 (v1.0) Dataset Open
Dataset Description These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. Context and methodology This dataset...
Computer and information sciences
Uploaded on December 2, 2025
Topologically Accurate Segmentation and Brain MRI Generation
Faroudy, Rim
2025-11-30 (v1) Dataset Open
Topologically accurate brain cancer MRI generation Context and methodology This project investigates whether it is possible to generate topologically accurate magnetic resonance images (MRIs) of brain tumors using a two-stage neural network pipeline. The research question is : can a machine...
Uploaded on December 1, 2025
ADR Sign Sample Dataset for Drones See Signs
Lee, Chung-Hsuan Ryan
2025-12-01 Image Open
DMP DOI: https://doi.org/10.5281/zenodo.17769823 Context and Methodology This dataset was created as part of the Drones See Signs project, an in-progress research project focused on detecting ADR (hazard) signs during drone flight using YOLO-based object detection methods. The purpose of this...
Uploaded on December 1, 2025
WatermarkNN Evaluating Black-Box Watermarking in DNN
Hamm, Stefan
2025-11-30 Other Open
Context and methodology The datasets were created within the project WatermakNN: Evaluating Black-Box Watermarking Robustness in Deep Learning. The aim is to study how robust neural watermarking techniques remain under black-box access by training and evaluating image-classification models...
data science
adversarial-ML
triggerset
black-box
squeezenet
mnist
fashionmnist
watermarking
Uploaded on December 1, 2025