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.
The repository uses persistent identifiers and synchronises with data hubs: DataCite, OpenAIRE, and BASE.
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.
Recent Uploads
Yamamoto, Taiga
2025-07-15
Audio
Open
Audio recording of a lute piece from the E-LAUTE projectOverviewThis dataset contains an audio recording of the piece "Tróstlicher lieb", a 16th century lute music piece originally notated in lute tablature, created as part of the E-LAUTE project (https://e-laute.info/). The recording preserves...
Uploaded on July 15, 2025
Moser, Maximilian
;
Miksa, Tomasz
2025-07-01
Other
Metadata-only
A primer on your dataset's description (to be edited) The influence of proper documentation on the reusability for research data should not be underestimated!In order to help others understand how to interpret and reuse your data, we provide you with a few questions to help you structure your...
Computer and information sciences
Uploaded on July 1, 2025
Moser, Maximilian
2025-06-03
Dataset
Open
This is a drawing of Meret-Neith, according to photos from Google images.
Boltz, Joachim
2025-04-12 (1.0.0)
Dataset
Open
We use the https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis dataset to predict whether customers buy in web, store or by catalog.
kaggle
machine learning
Uploaded on May 20, 2025
Bouhamidi, Hachem
2025-04-28 (1.0)
Dataset
Open
Context and Methodology: This dataset was created as part of a sentiment analysis project using enriched Twitter data. The objective was to train and test a machine learning model to automatically classify the sentiment of tweets (e.g., Positive, Negative, Neutral).The data was generated using...
Sentiment Analysis, Twitter Data, Machine Learning, NLP, Classification, HistGradientBoostingClassifier, Text Mining
Uploaded on May 20, 2025
