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Data for Hydrogen Spillover through Hydride Transfer: The Reaction of ZnO and ZrO2 with Strong Hydride Donors
No full textThe data set contains TEM images, XAS data, EPR data, MAS NMR data and NMR data of zinc oxide and zirconium oxide samples before and after treatment with molecular metal hydrides
Computational design and robotic fabrication for high environmental quality timber constructions: the livMatS Biomimetic Shell case study
No full textThe dataset includes the raw data and the corresponding report for the life cycle assessment of the building demonstrator 'livMatS Biomimetic Shell' (Website).
The pressure on the construction industry to reduce its environmental impact is leading practitioners to investigate the use of more sustainable materials, such as timber. Still, due to its limited availability, it is questioned to which degree timber could substitute steel and concrete, and strategies to reduce its consumption are necessary. The Cluster of Excellence “IntCDC” investigates novel approaches
to sustainable architecture. These exploit integrative computational design and automatic fabrication. These have been showcased in the livMatS Biomimetic Shell, for which a hollow timber cassette has been realized. In this study, the Lifecycle Assessment (LCA) analysis evaluated the developed cassette's environmental profile compared with other functionally equivalent systems. The analyses showed that the livMatS Biomimetic Shell reduced material consumption by 51% and a Global Warming Potential (GWP) 39% lower than conventional timber construction. Optimized fabrication processes allowed for emissions reduction by 60% in comparison with a solid cross-laminated timber box
COLife_05: Gigamap and Game Introduction
No full textThe dataset presents two files: A game design introduction and the gigamap that led to its design. The design introduction has led to the implementation of the game on the following Website. The game is to engage the urban citizens to support their local biodiversity by making DIY recipes or sharing photos of how they support it. The gigamap is a process tool that led the project to the competition and represents its codesigning processes across multiple stakeholders
Optimum Blue Light Exposure: A Means to Increase Cell-Specific Productivity in Chinese Hamster Ovary Cells
No full textRaw Data for manuscript titled Optimum Blue Light Exposure: A Means to Increase Cell-Specific Productivity in Chinese Hamster Ovary Cells
This dataset was generated from experiments conducted in mini bioreactor cultures to investigate the effects of blue light illumination on mammalian cells. The study demonstrated an increase in cell-specific productivity (csp) of monoclonal antibodies under blue light exposure.
The dataset includes:
Growth data: Detailed cell growth metrics.
Monoclonal antibody titers: Measurements of antibody concentration.
Intracellular ROS measurements: Data on reactive oxygen species levels.
Cell cycle analysis: Distribution of cells across different phases.
QTOF measurements: Proteomic and metabolomic profiling to explore mechanisms behind csp enhancement.
All data are presented in tabular format, including means and standard deviations, providing precise numerical values. The dataset is organized by light intensity (W/m²) and light intervals (light/dark cycles) to facilitate analysis of the observed effects.
This repository offers a comprehensive resource for further exploration of light-mediated productivity enhancement in bioprocesses
Models and Prepared Datasets for LG-CNN
No full textModels trained with Heat Plume Prediction
and datasets prepared with Heat Plume Prediction into reasonable format, reduced set of in/outputs, 2D, normalization used to train these models. Last relevant git commit: cae87d68faf96b2bd8dab935.
Based on raw data from doi:darus-4156
Datasets: 6 Heat Pumps, Simulation - Raw + Prepared
No full textThis dataset serves as training data for modeling the temperature field emanating from several open loop groundwater heat pumps (six heat pumps, randomly placed). It is simulated with Pflotran and stored in h5 format.
The two datasets contain 1000 and additional 4000 data points. Each data point consists of one simulation run until a near steady state is reached. Each data point measures 3.2 x 3.2 km with 640 x 640 cells.
The varying parameter of the dataset is the heterogeneous permeability with a fixed minimum and maximum. Other parameters that define the dataset, such as porosity, are chosen to be as close as possible to reality.
Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122.
Generated with scripts from Dataset generation with Pflotran (commit 78e8095) with arguments given in inputs/args.yaml.
The dataset is prepared with Heat Plume Prediction into reasonable format, reduced set of in/outputs, 2D, normalization. Last relevant git commit: cae87d68faf96b2bd8dab935
AFEM-by-Equilibration
No full textThis repository showcases how adaptive finite element solvers using equilibration based a posteriori error estimates can be build. Therefore, FEniCSx [1] alongside with dolfinx_eqlb [2], an extension for efficient flux equilibration are used. Classical benchmarks for the Poisson problem and linear elasticity are shown. The here presented code can be used to reproduce the results in the related publication.
[1] Baratta, I. A. et al.: DOLFINx: The next generation FEniCS problem solving environment. preprint. (2023) doi: 10.5281/zenodo.10447666
[2] Brodbeck, M., Bertrand, F. and Ricken, T.: dolfinx_eqlb v1.2.0. DaRUS (2024) doi: 10.18419/darus-4498<br
Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials
No full textThe data in this repository support the findings presented in the article "Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials" by Ou et al. The repository contains the training sets, the fitted machine-learning interatomic potentials (MTPs), and the relaxed bulk and grain boundary structures. An automated script to perform the proposed quality-level-based active learning scheme is also provided
Models and Prepared Datasets for Convolutional Long Short-Term Memory (ConvLSTM) Networks
No full textThe dataset contains trained ConvLSTM models for heat plume extension and the prepared dataset for training and testing.
In this repo the code for model training and dataset preparation is published.
The last relevant git commit is 0a148e6131b98260.
The prepared dataset for training is called ep_medium_1000dp_only_vary_dist inputs_ks.
It consists of 1000 simulation runs from darus-3652 that are prepared for the ConvLSTM network.
Each data point measures 320 m x 6400 m with 64 x 1280 cells. All parameters are constant except for the permeability, which varies across the domain and from data point to data point
BayesValidRox 1.1.0
No full textRelease 1.1.0 of BayesValidRox. BayesValidRox is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison. (2024-07-18