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
Supplementary data of publication: "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation"
(Technische Universität Bergakademie Freiberg, 2024-04-26) Daus, Sarah
This dataset contains the supplementary data of the open-access publication "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation". It provides all the necessary data and scripts to replicate the results of the study and to further use the synthetic data generation to train one's own deep learning image segmentation model. The original scripts have been slightly modified to make them more suitable for general use, e.g. parameters have been passed to an argument parser instead of hard-coding them whenever possible. Abstract of Paper: Monitoring particle properties directly in the process using in-situ microscopy can provide valuable input to control loops, improve process understanding and facilitate process optimization. However, obtaining reliable information from these images remains a challenge, especially for higher solids concentrations or agglomerating systems. Recent studies have successfully applied deep learning models to extract particle characteristics from in-situ image data. Despite these advances, the problem of generating training data has not been properly addressed. Manual annotation is time-consuming and prone to bias due to high particle counts, particle overlap, and out-of-focus objects. This paper presents a new approach to generate training data for segmentation models by combining conventional segmentation methods with copy-paste augmentation. A case study was conducted in which depth filtration experiments were performed on irregularly shaped alumina particles. An instance segmentation model trained on data generated using the proposed approach successfully detected and characterized particles, even at high solids concentrations.
Item
Open Access
Extended Phase-Field Method (XPFM) - Data corresponding to publication: "An enriched phase-field method for the efficient simulation of fracture processes" by Loehnert et al. (2023)
(Technische Universität Dresden, 2024-04-22) Curosu, Verena
This dataset contains the numerical values corresponding to the graphs within the scientific (open-access) contribution "An enriched phase-field method for the efficient simulation of fracture processes" by Loehnert et al. (2023) (https://doi.org/10.1007/s00466-023-02285-z). Abstract of Paper: The efficient simulation of complex fracture processes is still a challenging task. In this contribution, an enriched phase-field method for the simulation of 2D fracture processes is presented. It has the potential to drastically reduce computational cost compared to the classical phase-field method (PFM). The method is based on the combination of a phase-field approach with an ansatz transformation for the simulation of fracture processes and an enrichment technique for the displacement field as it is used in the extended finite element method (XFEM) or generalised finite element method (GFEM). This combination allows for the application of significantly coarser meshes than it is possible in PFM while still obtaining accurate solutions. In contrast to classical XFEM / GFEM, the presented method does not require level set techniques or explicit representations of crack geometries, considerably simplifying the simulation of crack initiation, propagation, and coalescence. The efficiency and accuracy of this new method is shown in 2D simulations.
Item
Open Access
Guidelines for quantitative survey on perception of urban green spaces during COVID-19 pandemic
(Universität Leipzig, 2024-03-22) Hübscher, Marcus
This is the survey guideline used for the quantitative survey.
Item
Open Access
STABEEL - Messkampagne 2023/Q1 - Experimentelle Verifikation von Umrichterinteraktionen Q(U)-geregelter Erzeugungsanlagen am Dynamischen Netzmodell (physikalische 220-kV-Netznachbildung) des TUD - IEEH
(Technische Universität Dresden, 2024-03-19) Krahmer, Sebastian; Ecklebe, Stefan
Dieses Datenset enthält die erstellten Netz- und Anlagenmodelle, die Beschreibung der durchgeführten Experimente sowie die aufgezeichneten Strom- und Spannungsverläufe (Messdaten) zur Verifikation und Weiterentwicklung der im STABELL-Projekt aufgestellten Stabilitätskriterien. Die Versuche wurden am Dynamischen Netzmodell des Instituts für Elektroenergieversorgung und Hochspannungstechnik durchgeführt. Dieses Labornetz bildet ein 220-kV-Netz in einem Modellmaßstab von 440 V ab. In dem Versuch wurden die Interaktionen von Q(U)-geregelten Erzeugeranlagen untersucht, in dem diese mit Hilfe der freiparametrierbaren Regelungsplattform (https://tu-dresden.de/ing/elektrotechnik/eti/le/die-professur/Einrichtungen) des Lehrstuhls für Leistungselektronik nachgebildet wurden. Die Arbeiten entstanden im Rahmen des Projektes STABEEL, gefördert durch die Deutsche Forschungsgemeinschaft (DFG, doi: 10.13039/501100001659) – Projektnummer 442893506.
Item
Open Access
Strain curves measured with different DFOS types and varying spatial resolution by an ODiSI 6108
(Technische Universität Dresden, 2024-03-15) Herbers, Max
Based on the principle of coherent optical frequency domain reflectometery (c-OFDR), cracks in concrete structures can be precisely localized and their widths determined due to the high spatial resolution in the sub-millimeter range. However, with longer distributed fiber optic sensors (DFOS) lengths, as is common in structural health monitoring, the spatial resolution is reduced. Experimental studies were carried out to investigate the influence of the spatial resolution on the measurement quality in the area of large strain gradients. Four different DFOS, two robust and two filigree DFOS types, were subsequently installed on a 4 m long reinforced concrete beam. In a displacement controlled 4-point bending test, crack widths of up to 0.5 mm were measured with four different gage pitch (gp).