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
Data corresponding to the publication "Optical and acoustic plasmons in the layered material Sr2RuO4"
(Technische Universität Dresden, 2025-03-21) Schultz, Johannes; Lubk, Axel; Jerzembeck, Fabian; Kikugawa, Naoki; Knupfer, Martin; Wolf, Daniel; Büchner, Bernd; Fink, Jörg
We use momentum-dependent electron energy-loss spectroscopy in transmission to study collective charge excitations in the layer metal Sr2RuO4. This metal has a transition from a perfect Fermi liquid below T~30 K into a "strange" metal phase above T~800 K. We cover a complete range between in-phase and out-of-phase oscillations. Outside the classical range of electron-hole excitations, leading to a Landau damping, we observe well-defined plasmons. The optical (acoustic) plasmon due to an in-phase (out-of-phase) charge oscillation of neighbouring layers exhibits a quadratic (linear) positive dispersion. Using a model for the Coulomb interaction of the charges in a layered system, it is possible to describe the range of optical plasmon excitations at high energies in a mean-field random phase approximation without taking correlation effects into account. In contrast, resonant inelastic X-ray scattering data show at low energies an enhancement of the acoustic plasmon velocity due to correlation effects. This difference can be explained by an energy dependent effective mass which changes from ~ 3.5 at low energy to 1 at high energy near the optical plasmon energy. There are no signs of over-damped plasmons predicted by holographic theories.
Item
Open Access
Research data for: "Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets"
(Technische Universität Bergakademie Freiberg, 2025-03-20) Seyffer, Judith Miriam; Fuchs, Lukas
The data set comprises the micro-CT data for the publication Brands et al. (2025). In a joint work within the 1st funding period of the SPP2364, the three-dimensional distribution of the active ingredient in pharmaceutical tablets is investigated using a segmented CT image data set of a tablet. The data published here contains the raw, reconstructed micro-CT image data after acquisition using the Zeiss Xradia Versa 510. Imaging was performed by stitching five individual, overlapping region of interest scans over the full width of the tablet. Additionally, the data set of the segmented active ingredient particles is included in a downscaled version. Further information can be found in the corresponding publication "Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets".
Item
Open Access
Data for Simon Nogo_NFL_nCREANN
(Technische Universität Dresden, 2025-03-13) Elmers, Julia Kristina; Mückschel, Moritz; Akgün, Katja; Ziemssen, Tjalf; Beste, Christian
The data set includes raw behavioral data (logfiles) from the Simon Nogo task, raw preprocessed EEG data, and NFL data of 55 healthy participants. Further, all customized scripts for the analyses are provided.
Item
Open Access
Influence of shredder and mill settings on the material recoveries and product qualities of a two-stage mechanical recycling process of automotive lithium-ion batteries
(Technische Universität Bergakademie Freiberg, 2025-03-12) Kaas, Alexandra; Wilke, Christian; Peuker, Urs
For a two stage shredding and milling process the yield of black mass and the elemental recovery of critical elements Ni and Li s investigated. I can be shown that the quality of the products resulting from the mechanical recycling of lithium-ion batteries significantly depends upon the parameters employed during the shredding process. Modifications to the settings have the potential to exert a considerable impact on the particle size, liberation of composites and de-coating of electrodes. The discharge grid size employed during the first shredding step shows a significant influence on the downstream separation behaviour of the casing material and separator foil. The mill speed utilised during the second comminution step determines the separation achieved between the cathode and anode. A reduction in grid size employed during the first shredding stage results in an increase in black mass yield, although the recovery of the casing is diminished. In total Ni recovery for all setting combinations is similar, a lower recovery in the first shredding step is compensated by a higher recovery after the second comminution. It was observed that an overall increase in the mill speed above 1750 rpm resulted in elevated levels of copper contamination within the black mass. The influence of eleven distinct combinations of shredder and mill settings on the black mass yield and its composition, the recovery of the separator foil and the casing, as well as the separation behaviour of the anode and cathode, were investigated.
Item
Open Access
OC_Identifier - Programm to use OC trained AI for the assessment of their state of maturation
(Technische Universität Dresden, 2025-03-11) Lv, Guofan; Kruppke, Benjamin
This programm is part of the project, that has successfully trained an AI that can classify and calculate four cell types. This AI is practical and has a bright future in terms of cell identification. YOLOv5m was used for this AI training. With a better computer configuration, YOLOv5x can be used for training to obtain an even better AI model. Ultralytics is the software company that developed YOLOv5. It released the latest YOLO series version, YOLOv8, in January 2023. This new framework can lead to better training results. In addition to known improvement methods, AI technology has greater prospects. YOLOv5 can be combined with other AI frameworks to predict cell growth trends. AI can also make suggestions about cell culture conditions (temperature, media, frequency of media exchange, etc.) based on growth trends. The AI framework can also be combined with other devices, for example, to control robotic arms and microscopes to automatically complete image acquisition. With the help of this AI, laboratory staff can be freed from the tedious task of counting cells and do much more.