Research Data Repository of Saxon Universities

OPARA is the Open Access Repository and Archive for Research Data of Saxon Universities.


Researchers of Saxon Universities can either publish their research data on OPARA, or archive it here to comply with requirements of funding acencies and good scientic practice, without public access.

You can find the documentation of this service at the ZIH Data Compendium websites. If you need suppourt using OPARA please contact the Servicedesk of TU Dresden.

Please note: The OPARA service was recently upgraded to a new technical platform (this site). Previously stored data will not be available here immediately. It can be found at the still active old version of OPARA. These stock data will be migrated in near future and then the old version of OPARA will finally be shut down. Existing DOIs for data publications remain valid.

Artwork based on 1, 2, 3, 4  @pixabay
 

Communities in OPARA

Select a community to browse its collections.

Recent Submissions

Item
Open Access
Granule size and shape data from SOPAT for publication "Impact of Feed Rate and Binder Concentration on the Morphology of Spray Dried Alumina-Polymer Nano-composites"
(Technische Universität Bergakademie Freiberg, 2025-04-24) Mitra, Rahul
This dataset comprises in-line experimental SOPAT data from spray drying investigations of alumina and polyvinylpyrrolidone-30 composite granules. The spray drying process outcomes are documented through inline optical images of dried granules obtained via a SOPAT in-line imaging system providing maximum and minimum Feret diameter and aspect ratio information.
Item
Open Access
Micro-CT data for SPP 2364 "Model-based control of spray synthesis of structured granules with specified properties using transfer functions derived by multivariate stochastic models and machine learning"
(Technische Universität Bergakademie Freiberg, 2025-04-23) Mitra, Rahul
The data set comprises the micro-CT data from the 1st funding period of the SPP 2364, project number 504580586. The data published here contains the raw, reconstructed micro-CT images of the two spray dried samples (at two different feed rates) tested. The scans were done using the Zeiss Xradia Versa 510.
Item
Open Access
Predictive maintenance demonstrator dataset with individual load histories
(Technische Universität Dresden, 2025-04-14) Heindel, Leonhard; Hantschke, Peter; Kästner, Markus
Predictive maintenance aims to develop methods that are capable of predicting component failure before it occurs. Virtual sensing methods predict unmeasured physical quantities from available measurement data. These methods offer significant benefits to predictive maintenance, since virtual sensors can be used to estimate quantities that are difficult to measure. In many real applications, the time to failure is in the range of years, complicating the development and validation of predictive maintenance and virtual sensing approaches. This dataset provides a demonstrator example where failure occurs based on individual load histories. The sensor setup consists of simple notched steel specimens, which are clamped between two servo-hydraulic cylinders of a fatigue test bench. It is designed to provide a virtual sensor use case with independent training and testing data, so that the dataset can be used for algorithm development and benchmarking purposes.
Item
Open Access
Data corresponding to the publication: "SU(N) altermagnetism: Lattice models, magnon modes, and flavor-split bands" by P. M. Cônsoli and M. Vojta (2025)
(Technische Universität Dresden, 2025-04-11) Monteiro Consoli, Pedro; Vojta, Matthias
This dataset contains the scripts that generated the data and figures from the preprint P. M. Cônsoli and M. Vojta, "SU(N) altermagnetism: Lattice models, magnon modes, and flavor-split bands", arXiv:2402.18629, which has been accepted for publication in Physical Review Letters.
Item
Open Access
BiSID-5k: A Bimodal Image Dataset for Seed Classification from the Visible and Near-Infrared Spectrum
(Universität Leipzig, 2025-04-10) Kukushkin, Maksim; Bogdan, Martin; Goertz, Simon; Callsen, Jan-Ole; Oldenburg, Eric; Enders, Matthias; Schmid, Thomas
The success of deep learning in image classification has been largely underpinned by large-scale datasets, such as ImageNet, which have significantly advanced multi-class classification for RGB and grayscale images. However, datasets that capture spectral information beyond the visible spectrum remain scarce, despite their high potential, especially in agriculture, medicine, and remote sensing. To address this gap in the agricultural domain, we present BiSID-5k, a thoroughly curated bimodal seed image dataset comprising paired RGB and hyperspectral images for 10 plant species, making it one of the largest bimodal seed datasets available. We describe the methodology for data collection and preprocessing and benchmark several deep learning models on the dataset to evaluate their multi-class classification performance. By contributing a high-quality dataset, BiSID-5k offers a valuable resource for studying spectral, spatial, and morphological properties of seeds, opening new avenues for research and applications.