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.
Communities in OPARA
Select a community to browse its collections.
Recent Submissions
Item
Open Access
TUD Industrial Database (v1.1)
(Technische Universität Dresden, 2025-06-03) Scharf, Hendrik
This database supports modeling transformation pathways in Germany’s process industry sector by providing techno-economic data for processes associated with at least one conventional or low carbon production system having as main output alumina, ammonia, bricks, chlorine, clinker, container glass, corrugated board, float glass, high value chemicals, methanol, newspaper, other graphic paper, primary aluminum, primary copper, primary steel, quicklime, refractories, secondary aluminum, secondary steel, soda ash, tissue, or urea. The techno-economic data comprise availability factors and lifetimes of industrial process facilities, normalized investments for both new conventional process facilities and for the defossilization of existing process facilities, energy and raw material inputs, by-product outputs, and market entry years of processes. Additionally, the database provides both statistics and scenario assumptions on annual industrial production volumes, purchase prices for inputs, sales prices for by-products, European Emission Allowance prices, and carbon dioxide intensities of the inputs used in the processes. To reflect variability in literature values, especially regarding energy and material flows, investments, and other techno-economic assumptions, the dataset provides intervals consisting of a reference value (REF) representing the median of the available data, as well as an upper bound (UB) reflecting the less optimistic and a lower bound (LB) representing the more optimistic range of specifications regarding costs available for a process.
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Open Access
Human Expertise vs. Artificial Intelligence: New evidence on building age estimation - Experts survey and ChatGPT answers
(Technische Universität Dresden, 2025-06-03) Soot, Matthias; Kretzschmar, Daniel; Eberwein, Johannes; Zaddach, Sebastian; Teuber, Andreas; Weitkamp, Alexandra
The construction, operation, and demolition of buildings account for a significant portion of global energy demand and CO2 emissions. Preserving existing buildings and encouraging renovation is crucial for sustainability. However, detailed information about the construction age of buildings is sparse. This study explores the potential of large pre-trained visual language models (VLMs) like ChatGPT to estimate building age, comparing their accuracy to that of real estate experts. The research involves a comprehensive survey of experts and the application of ChatGPT promts to a dataset of building images. The results show that ChatGPT makes more accurate statements about the age of buildings than individual experts. Only the collective intelligence of many experts provides better results than the VLM. This suggests that VLMs should be used more extensively than before to determine the age of buildings. Using this knowledge, urban planning can be tailored more specifically to necessary renovations in order to extend the life cycles of buildings and close loops in line with the principle of circularity.
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Open Access
Survey of Active Learning Hyperparameters
(Technische Universität Dresden, 2025-06-02) Gonsior, Julius
Annotating data is a time-consuming and costly task, but it is inherently required for supervised machine learning.
Active Learning (AL) is an established method that minimizes human labeling effort by iteratively selecting the most informative unlabeled samples for expert annotation, thereby improving the overall classification performance. Even though AL has been known for decades [1], AL is still rarely used in real-world applications. As indicated in the two community web surveys among the NLP community about AL [2], [3], two main reasons continue to hold practitioners back from using AL: first, the complexity of setting AL up, and second, a lack of trust in its effectiveness. We hypothesize that both reasons share the same culprit: the large hyperparameter space of AL. This mostly unexplored hyperparameter space often leads to misleading and irreproducible AL experiment results. In this study, we first compiled a large hyperparameter grid of over 4.6 million hyperparameter combinations, second, recorded the performance of all combinations in the so-far biggest conducted AL study, and third, analyzed the impact of each hyperparameter in the experiment results. Besides our publication we are making our raw experiment results publically available for other researchers to built upon.
[1] B. Settles, “Active Learning Literature Survey,” University of Wisconsin-Madison Department of Computer Sciences, Technical Report, 2009.
[2] K. Tomanek and F. Olsson, “A web survey on the use of active learning to support annotation of text data,” in Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, E. Ringger, R. Haertel, and K. Tomanek, Eds. Boulder, Colorado: Association for Computational Linguistics, 2009, pp. 45–48.
[3] J. Romberg, C. Schröder, J. Gonsior, K. Tomanek, and F. Olsson, “Have LLMs Made Active Learning Obsolete? Surveying the NLP Community,” 2025.
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Open Access
Mesenchymal-epithelial transition reduces proliferation but increases immune evasion in tumor spheroids
(Technische Universität Dresden, 2025-05-23) Dimari, Gina; Hu, Yueyuan; Frenzel, Annika; Fuchs, Anke; Wurm, Alexander A.; Fischer-Friedrich, Elisabeth
The data corresponds to experiments performed with MET-inducible models of MDA-MB-231 and ES-2 mesenchymal cell lines. These were modified to contain an inducible plasmid that codes for miR-200c and miR-141 that upon overexpression trigger the expression of epithelial features. In the first place, the collection contains transcriptomic and proteomic data suchs as RNA seq data, q-PCR data, western-blot data, and immunofluorescence of EMT markers, that were all used to characterize our models. Moving forwards, with these two models we performed proliferation assays in adherent and 3D cultures of different nature. In adherent cultures, we looked at the effect of MET on proliferation focusing on the regulation exhorted by mechanical signals such as cell-cell junctions, spread area and their influence on contact inhibition of proliferation. Most of the data acquired from these experiments were obtained by image analysis of immunofluorescent microscopy. To measure proliferation changes in 3D cultures, we cultured cells in PEG-heparin hydrogels at both degradable and non-degradable forms. This gave rise to tumor spheroids were we quantified different parameters to assess proliferation levels. Moreover, we used pharmacologycal inhibitors to study the effect of focal adhesion and actomyosin cytoskeletal signaling in the proliferation of these tumor spheroids, culture in control and MET-inducing conditions. The data from these experiments was as well obtained from image analysis from fluorescent microscopy. Furthermore, we performed co-cultures of tumor spheroids with peripheral blood mononuclear cells and quantified the changes in apoptosis in the tumor cells, both by image analysis from immunofluorescent microscopy and flow cytometry.
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Open Access
Supplementary information to the publication “Influence of foam composite in lithium-ion battery packs on their mechanical recycling”
(Technische Universität Bergakademie Freiberg, 2025-05-23) Rademacher, Paul; Kaas, Alexandra; Wilke, Christian; Peuker, Urs A.
Supplementary data to the following paper.
Lithium-ion batteries (LIB) make an important contribution to the energy transition as energy storage devices for mobile and stationary applications. The recovery of the valuable materials contained in the lithium-ion batteries after their end of life is of central importance for the development of a circular economy in line with the concept of sustainability. Mechanical recycling is to be seen as a first step in this process.
With processes for the mechanical recycling of LIB that have already been successfully developed and implemented, it is possible to recover most components of a LIB i.e., the materials of the anode, cathode and separator foils as well as the casing. The concentrate of the coating of the electrode foils, which is called black mass, becomes an intermediate product for hydrometallurgical recycling processes for the recovery of lithium, among other materials.
Some OEM of the automotive industry are about to introduce cell-to-pack-technologies, in which individual LIB-cells are fixed and stabilised in their position inside the large battery pack with the aid of a foam material, thereby adding further materials to the battery pack. The effects of the foam on the recycling are not known yet. Within the scope of this experimental work, several technological variants to enrich and separate the foam as an individual material fraction were investigated. The holistic aim is to minimise contamination from the foam in the valuable fractions. Two different types of foam and their effect on the purity of the recycling products were analysed.