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
S4F: Fuel Loads, Fuel Moisture, Fuel Consumption and Fire Emissions for the Cerrado Amazon Region
(Technische Universität Dresden, 2024-12-20) Forkel, Matthias; Kinalczyk, Daniel; Wessollek, Christine
This repository provides datasets of fuels and fire emissions for the Amazon and Cerrado that were derived and analysed in the publication by Forkel et al. (2024). This publication demonstrates the first application and benchmarking of the TUD.S4F and KNMI.S5p approaches to estimate fire emissions and to investigate the effects of woody debris on fire emissions.
TUD.S4F, developed at TUD Dresden University of Technology, is a satellite data-model fusion approach that combines several satellite products to estimate fuel loads, fuel moisture, fuel consumption, and fire emissions. Emission factors are computed dynamically depending on fuel type and fuel composition. TUD.S4F (version 0.2) is described in the Methods and Supplementary material of Forkel et al. (2024) and the related model code is available here: https://doi.org/10.5281/zenodo.14274230. TUD-S4F provides fuel loads, fuel consumption and fire emissions at a spatial resolution of 333 x 333 m and aggregated to 0.1 x 0.1° for the Amazon/Cerrado for the period 2014-01-01 to 2021-10-20 at 10-daily time step.
KNMI.S5p provides fire emission for carbon monoxide (CO) and nitrogen oxides (NO) derived from Sentinel-5p observations. The data is provided at 0.1 x 0.1° and at daily time step for the year 2020.
Both TUD.S4F and KNMI.S5p are products of the Sense4Fire projected, awarded from the European Space Agency to TU Dresden, the Royal Dutch Meteorological Institute (KNMI) and BeZero Carbon Ltd.
Item
Open Access
DeepExtremeCubes
(Universität Leipzig, 2024-12-19) Ji, Chaonan; Fincke, Tonio; Benson, Vitus; Camps-Valls, Gustau; Fernández-Torres, Miguel-Ángel; Gans, Fabian; Kraemer, Guido; Martinuzzi, Francesco; Montero, David; Mora, Karin; Pellicer-Valero, Oscar; Robin, Claire; Söchting, Maximilian; Weynants, Melanie; Mahecha, Miguel
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.
Item
Open Access
Robust Laser Cross Detection
(Technische Universität Bergakademie Freiberg, 2024-12-12) Kluwe, Moritz Niklas; Hardege, Robert
The source code repository contains the complete Python implementation of the laser cross detection algorithm, including example datasets. The repository includes synthetic image generation tools, evaluation scripts, and reference implementations of RANSAC and Probabilistic-Hough-Transform methods for comparison. Example datasets feature both synthetic and real-world calibration images, with documentation detailing their usage. All code is thoroughly commented and includes usage examples. The implementation requires common Python libraries (numpy, scipy, lmfit) and provides a straightforward API for integration into existing calibration workflows.
Item
Open Access
Influence of a pyrolysis at different temperatures on the mechanical recycling efficiency of Li-ion batteries
(Technische Universität Bergakademie Freiberg, 2024-12-12) Kaas, Alexandra; Wilke, Christian; Born, Jannik; Ahuis, Marco; Kwade, Arno; Peuker, Urs
The integration of a pyrolysis stage into the mechanical recycling process of Li-ion batteries is supposed to improve the decoating of the electrodes, the recovery of valuable components and the overall quality of the products. The effectiveness of the pyrolysis process depends on the applied temperature as the various components of a Li-ion battery melt, evaporate and decompose at different temperatures. The decomposition temperature of the cathode binder is a crucial factor in the thermal mechanical recycling process. Temperatures below this threshold have a negative impact on the recovery rates, as the melting of binder and plastics reduces the efficiency of the recovery process. Conversely, higher temperatures facilitate the recovery of the cathode coating metals (Ni, Co, Li) into the black mass. The majority of the metals (92-98%) are recovered and, following hydrometallurgical treatment, can be reused for cell production, thereby closing the loop and reducing the consumption of raw materials and enhancing the sustainability of batteries. Moreover, the decomposition of the organic components, including binders and plastics, improves the quality of the products, thereby reducing the necessity and extent of further treatment.
Item
Open Access
Research data for: “Forces during film drainage and detachment of NMC and spherical graphite in particle-bubble interactions quantified by CP-AFM and modeling to understand the salt flotation of battery black mass”
(Technische Universität Bergakademie Freiberg, 2024-12-09) Nicklas, Jan
This dataset characterizes the particle-bubble interaction for single battery black mass particles (NMC 622 and spherical graphite) in sodium chloride solutions (0.001 mol/L to 0.750 mol/L) for pH 3 to pH 10. The interaction of black mass particles with gas bubbles in the AFM-geometry gives information about the likeliness of particle-bubble-attachment and detachment in salt flotation.
The research data consists of two parts: A) the Experimental Atomic Force Microscopy data for the interaction of black mass particles (NMC 622 (NMC) and spherical graphite (SG)) with sessile gas bubbles in salt solutions and B) the Data for the key figures of “Forces during film drainage and detachment of NMC and spherical graphite in particle-bubble interactions quantified by CP-AFM and modeling to understand the salt flotation of battery black mass”.