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 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.
Item
Open Access
Learning Gentle Grasping Using Vision, Sound, and Touch
(Technische Universität Dresden, 2025-03-11) Nakahara, Ken; Calandra, Roberto
This dataset contains 1,500 robotic grasps collected for the paper of Learning Gentle Grasping Using Vision, Sound, and Touch. Additionally, we provide a description of this dataset and Python scripts to visualize the data and process raw data into a training dataset for a PyTorch model. The robotics system used consists of a multi-fingered robotic hand (16-DoF, Allegro Hand v4.0), 7-DoF robotic arms (xArm7), DIGIT tactile sensors, an RGB-D camera (Intel RealSense D435i), and a commodity microphone. The target object is a toy that emits sound when grasped strongly.
Item
Open Access
Raw data of the paper "Brillouin spectroscopic investigation of corneal hydration and the impact of cross-linking therapy on water retention"
(Technische Universität Dresden, 2025-03-06) Rix, Jan; Steuer, Svea
Raw Brillouin, Raman and PS-OCT data acquired within the project and paper "Brillouin spectroscopic investigation of corneal hydration and the impact of cross-linking therapy on water retention"