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
Source code, scripts, and data used in the PhD thesis "Fine-grained OS Control over High-performance Networking"
(Technische Universität Dresden, 2024-05-27) Planeta, Maksym
The dataset comprises the data from three projects: MigrOS, CoRD, and Fastcalls. The MigrOS project is a system for live migration of high-performance networking applications. The CoRD project is a system for enabling fine-grained control over the RDMA network data plane. The Fastcalls project is a system for enabling fast system calls in the Linux kernel. This project includes both privileged and unprivileged fastcalls.
Item
Open Access
2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results
(Technische Universität Bergakademie Freiberg, 2024-05-14) Buchwald, Thomas; Ditscherlein, Ralf
2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains three distinct parts: original particle data, Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. The particle data comes as STLs that have been converted from VTK particle data as provided by the PARROT database (https://parrot.tu-freiberg.de/). The STL files are *not* identical with the STLs provided by PARROT as of May 2024! The particle data folder is provided as separate archive because of its large size. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.
Item
Open Access
PGT Prompt Gamma Timing PETsys data for publication at IKTP (PGT)
(Technische Universität Dresden, 2024-05-14) Novgorodova, Olga; Straessner, Arno; Hentges, Rainer; Glatte, Andreas; Lutz, Benjamin; Roemer, Katja; Teichmann, Thobias; Koegler, Toni
Proton therapy requires range verification in order to exploit its full potential. One of the most promising approaches is to monitor prompt gamma-rays produced by nuclear interactions of the therapeutic particles in the patient tissues. In our paper, we test PETsys electronics with a detector with a wide energy range from 100 keV to 15 MeV. We tested what time resolution we could achieve as a high time resolution is required to achieve millimetric precision in the proton range. PETsys should survive high count rates and the fraction of pile-up events should be low or separatable. We are investigating a full acceptance approach with increased granularity in order to reduce the size of the scintillators and consequently the count rate per channel. Ideally, we want to stack the scintillators in matrices that require suitable multi-channel photo-multipliers and a fitting acquisition system. Here, we present two geometries of CeBr3 crystals 5 × 5 × 20 mm3 and 10 × 10 × 30 mm3, together with modern silicon photo-multipliers (SiPM) adapted to work with the PETsys TOFPET2 ASIC. The TOFPET2 ASIC was developed for Time-of-Flight Positron Emission Tomography (TOF-PET) applications. Here are our data measured for the publication for time resolution and coincidence time resolution, energy resolution with AmBe source, and dead time studies.
Item
Open Access
Traffic Light Data of Hamburg collected in September and October 2023 (Observations from SensorThings API)
(Technische Universität Dresden, 2024-05-03) Jeschor, Daniel Maik; Matthes, Philipp; Springer, Thomas; Pape, Sebastian; Fröhlich, Sven
Real-time traffic light data collected from the Traffic Lights Data system in Hamburg from September to October 2023. The collected data was recorded under application of the Data license Germany (Attribution: Version 2.0 (https://www.govdata.de/dl-de/by-2-0 (retrieved on January 24, 2024)). It is provided by the the Free and Hanseatic City of Hamburg (LSBG). The data source can be found under: https://metaver.de/trefferanzeige?docuuid=AB32CF78-389A-4579-9C5E-867EF31CA225 (retrieved on January 24, 2024).
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.