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Data for: Pros and cons of airlift and bubble column bioreactors: How internals improve performance
No full textThe DaRUS repository entails the simulation data for the publication "Pros and cons of airlift and bubble column bioreactors: How internals improve performance".
Gas fermentation is a promising technology of high commercial interest, particularly for capturing CO2 and CO from industrial off-gases to reduce greenhouse gas emissions and to replace fossil fuels for bulk chemical production. Therefore, the ad initio evaluation of promising bioreactor settings is a crucial step. Whereas alternate configurations may be tested in laborious scale up studies, the tedious procedure may be accelerated by in silico studies that accompany or even partially replace wet-lab work. In this context, the current study compares various pneumatically agitated reactor types - bubble column reactor (BCR), annulus- and center-rising internal-loop airlift reactor (AR-IL-ALR and CR-IL-ALR), and external-loop airlift reactor (EL-ALR) - to identify advantages and disadvantages for the given application based on computational fluid dynamic (CFD) models. Process performance is optimized by the introduction of internal structures to guide the flow. Despite a 41 % increase in the mass transfer coefficient (k_L a) through internal modifications, the CR-IL-ALR still exhibited the poorest performance. The optimized AR-IL-ALR demonstrated good mixing and, after introducing an open-cone shaped internal in the head part and a conical bottom, superior mass transfer, achieving a 14 % enhancement in mass transfer coefficient to 315 1/h. This study thereby outlines the potential of internal structures for process improvement, as well as the value of a priori in silico design of reactor configurations
Supplemental data for "Thermo-plastic Nonuniform Transformation Field Analysis for eigenstress analysis of materials undergoing laser melt injection"
No full textThis repository contains supplemental data for the article "Thermo-Plastic Nonuniform Transformation Field Analysis for Eigenstress Analysis of Laser Dispersed Materials," submitted to Computer Methods in Applied Mechanics and Engineering (CMAME) by Felix Fritzen, Julius Herb, and Shadi Sharba.
The data contained in this DaRUS repository act as an extension to the GitHub repository https://github.com/DataAnalyticsEngineering/ThermoNTFA.
Data is generated using thermo-elasto-plastic simulations on the microscale at select temperatures. Based on that, a reduced basis consisting of plastic modes is computed. Additional linear-elastic simulations are performed to determine self-equilibrated fields at select temperatures. Based on the generated data, we perform interpolation to arbitrarily many intermediate temperatures based on a method published in our recent paper "Reduced order homogenization of thermoelastic materials with strong temperature-dependence and comparison to a machine-learned model". Using the temperature-interpolated data, the NTFA system matrices are computed and stored as tabular data. This tabular data is the basis for our thermo-mechanical NTFA UMAT, which is published in the GitHub repository.
The microstructure is defined with a representative volume element (RVE) with periodic boundary conditions. Further details are provided in the README.md file of this dataset, in our manuscript and in our GitHub repository
Raw Data for Modeling Heat Plumes of Heat Pumps with varying Flow Directions
No full textRaw datasets for modeling orientational variation in heat plume prediction in groundwater.
Used with 1HP NN equivariance.
File name explanation:
4d
The dataset encompasses only cardinal flow directions.
rd
The dataset encompasses random flow directions in the 2D plane.
1000dp
The dataset consists of 1000 data points.
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Data for: Substitute yeast extract while maintaining performance: showcase amorpha-4,11-diene production
No full textThe DaRUS repository contains the raw data corresponding to the figures of the manuscript "Substitute yeast extract while maintaining performance: showcase amorpha-4,11-diene production". Yeast extract (YE) is a complex nutritional source associated with high performance on microbial production processes. However, its inherent compositional variability challenges its scalability. While prior efforts have focused on growth-associated products, the dynamics of growth-uncoupled production, which leads to higher production rates and conversion yields, still need to be explored. This production scenario is common in large-scale applications. This study presents a systematic approach to replace YE for the production of the terpene amorpha-4,11-diene in Escherichia coli. Sequential processing was successfully applied to identify glutamic acid, alanine, leucine, valine, isoleucine, and glycine as the key amino acids (AAs) under slow growth conditions. Thoroughly applying biomass retention as part of sequential processing increased production capacity by 45% using these AAs instead of YE. Further studies, including flux balance analyses, targeted pyruvate as the common AA precursor. The optimized fed-batch process feeding pyruvate with 0.09 gPyr h-1 enhanced amorpha-4,11-diene production by 37 %, although adding only 1% carbon via pyruvate. Flux balance analysis revealed the criteria for optimum pyruvate feeding, e.g., to prevent succinate secretion and maintain the NADH/NAD+ balance. These findings illustrate the interplay between media composition and metabolic activity and provide a successful guideline for identifying lean, best-performing media for industrial applications
Replication Data for: "Crystalline porous frameworks based on double extension of metal-organic and covalent organic linkages"
No full textAll primary data files of the journal article mentioned can be found here. The data is structured by materials. The characterization data of each material are located in the subfolders "MOCOF-1", "MOCOF-1-R2", "MOCOF-2", "MOCOF-3", "COF-366-Co", "[Co(tapp)]nXn", "Co(tdpp)", "Co(tapp)", "Co(tdpp)(pba)2(OTf)", and "Others". The characterization data are recorded experimentally. The visualization and theoretical pore calculation data are generated by computation and located in the subfolder "Materials Studio". The data can be used to replicate the experiments, to evaluate and compare the materials' properties with others, to investigate the materials' structures, and to predict properties by computation
Data related to Panzer: A Machine Learning Based Approach to Analyze Supersecondary Structures of Proteins
No full textThis entry contains the data used to implement the bachelor thesis.
It was investigated how embeddings can be used to analyze supersecondary structures.
Abstract of the thesis:
This thesis analyzes the behavior of supersecondary structures in the context of embeddings. For this purpose, data from the Protein Topology Graph Library was provided
with embeddings. This resulted in a structured graph database, which will be used
for future work and analyses. In addition, different projections were made into the
two-dimensional space to analyze how the embeddings behave there.
In the Jupyter Notebook 1_data_retrival.ipynb the download process of the graph files from the Protein Topology Graph Library (https://ptgl.uni-frankfurt.de) can be found. The downloaded .gml files can also be found in graph_files.zip. These form graphs that represent the relationships of supersecondary structures in the proteins. These form the data basis for further analyses.
These graph files are then processed in the Jupyter Notebook 2_data_storage_and_embeddings.ipynb and entered into a graph database. The sequences of the supersecondary and secondary structures from the PTGL can be found in fastas.zip.
The embeddings were also calculated using the ESM model of the Facebook Research Group (huggingface.co/facebook/esm2_t12_35M_UR50D), which can be found in three .h5 files. These are then added there subsequently.
The whole process in this notebook serves to build up the database, which can then be searched using Cypher querys.
In the Jupyter Notebook 3_data_science.ipynb different visualizations and analyses are then carried out, which were made with the help of UMAP.
For the installation of all dependencies, it is recommended to create a Conda environment and then install all packages there.
To use the project, PyEED should be installed using the snapshot of the original repository (source repository: https://github.com/PyEED/pyeed).
The best way to install PyEED is to execute the pip install -e . command in the pyeed_BT folder. The dependencies can also be installed by using poetry and the .toml file.
In addition, seaborn, h5py and umap-learn are required.
These can be installed using the following commands:
pip install h5py==3.12.1
pip install seaborn==0.13.2
umap-learn==0.5.7
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Replication Data for: Olefin Metathesis and Stereoselective Ring-Opening Metathesis Polymerization with Neutral and Cationic Molybdenum(VI) Imido and Tungsten(VI) Oxo Alkylidene Complexes Containing N-Chelating N-Heterocyclic Carbenes
No full textAll primary data files associated with this publication, experimental procedures, reaction conditions and used analytical equipment can be found in detail in the experimental section or in the supporting information of the paper. Synthesized liagands (L1, L2 and L3) and complexes (W1, W2, Mo1-Mo5) and polymers were characterized via nuclear magnetic resonance (NMR) spectroscopy and the spectra can be found in NMR folder. . NMR Spectra are specified according to the numbering in the publication. Crystal data of novel complexes (W1, W2, Mo2 and Mo4) is deposited in the Cambridge Structural Database (CSD) of the Cambridge Crystallographic Data Centre (CCDC)
Data for: Feather keratin in Pavo cristatus: A tentative structure
No full textSupplementary materials for the scientific study on “Feather keratin in Pavo cristatus: A tentative structure”.
The Dataset consists of .pdb files obtained by AlphaFold. Avian F-keratin sequences of Pavo, Gallus and Larus spec. were comparatively analyzed for prediction and visualization of representative 3D structures of N-block and C-block F-keratin precursors as recently proposed by P. Russ, H.O.K. Kirchner, H. Peterlik & I.M. Weiss. Corresponding Ramachandran analyses of calculated Pavo F-keratin N-block and C-block, included here as graphical plots and CSV files of Phi (φ) and Psi (ψ) angles, agree with previously published experimental data. Tables are included for comparison of experimental SAXS and calculated structural features of previous and new F-keratin models, with a focus on X-ray diffraction data and density calculations. The Dataset contains relevant computational settings and describes the methodological workflow, especially for GROMACS simulations. A MPEG-4 Video file is included which shows a truncated version of Pavo F-keratin in water. This result confirms that the proposed protein folding and assembly mechanism would, in principle, be compatibel with tilting and stability of amorphous F-keratin filament precursors, a mandatory prerequisite for solidification into a biogenic super-alloy
Data for 'Adjustable polystyrene nanoparticle templates for the production of mesoporous foams and ZnO inverse opals'
No full textAll processed data files of the journal article "Adjustable polystyrene nanoparticle templates for the production of mesoporous foams and ZnO inverse opals" from the Sottmann group
Shaded relief WebMercator 'slippy map' tiles based on NASA Shuttle Radar Topography Mission Global 1 arc second V003 topographic height data
No full text
This dataset contains WebMercator tiles which contain gray-scale shaded relief (hill shades), and nothing else.
The tiles have a resolution of 256×256px, suitable for web mapping libraries such as <a
href="https://leafletjs.com/">Leaflet.
The hill shades are generated from SRTM altitude data,
which cover the land area between 60° northern and 58° southern latitude, and which lies in the public domain.
Map material without political or infrastructural features can be desirable, for example, in use cases where
historical data is visualized on a map.
The concrete motivation for generating this map material was the Dhimmis & Muslims project (<a
href="https://portal.volkswagenstiftung.de/search/projectDetails.do?ref=93252">project page, <a
href="https://damast.geschichte.hu-berlin.de/">home page, <a
href="https://github.com/UniStuttgart-VISUS/damast">GitHub, <a
href="https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2318">DaRUS dataset), which
analyzed peaceful coexistence of religious groups in the medieval Middle East.
A particular goal with creating the dataset was to have map material available under a permissive license for
screenshots and publications, instead of relying on proprietary mapping services such as <a
href="https://www.mapbox.com/">Mapbox.
Teaser image: The hillshades of Cyprus on zoom level 9.
This image is hosted externally by GitHub, but is also present in the repository as teaser.png.
Coverage.
The dataset covers zoom level 0 (entire world in one tile) to 12 (entire world in 4096×4096 tiles).
The total size of the dataset is 22,369,621 tiles.
However, of those, 19,753,304 tiles (88.3%) are empty, either because the landscape there is fully flat (i.e., on
water), or because they lie fully outside the latitude range covered by the SRTM altitude data.
The empty tiles are not stored.
Instead, a singular placeholder file is
stored in the repository, alongside a list of
the empty tiles.
During extraction, the placeholder empty tile can be symbolically linked in the file system to all the places
where it is needed.
The total size of the non-empty tiles is about 103GB.
Files.
Besides the placeholder file and the list of empty tiles, the repository also contains a <a
href="https://darus.uni-stuttgart.de/file.xhtml?fileId=276042">manifest file.
This file lists all non-empty tiles by the ZIP file they are contained in.
The tiles themselves are grouped into ZIP files by the following schema:
All tiles from levels 0 to 5 are contained in one
ZIP file.
All tiles of level N, N≥6 are contained in a ZIP file which is named after the tile of level
N-6 (block level) that contains the tile in question, named
tiles__<level>__<block level>_<block index i>_<block index j>.zip.
Hence, all tiles of level 6 are contained in a singular ZIP file named tiles__6__0_0_0.zip.
The tiles of level 7 are split up into four group ZIP files named tiles__7__1_{0,1}_{0,1}.zip, the
tiles of level 8 into 16 group ZIP files named tiles__8__2_{0..3}_{0..3}.zip, and so on.
Both the manifest file and the commands to generate the distribution of tiles on ZIP files can be generated using
the linked software repository.
Usage.
The tile ZIP files can be downloaded and extracted.
By serving the extracted directory structure in a web server, a slippy map tile server can be created.
The linked software repository also contains a command-line
utility that generates the required shell commands to download the ZIP files, extract them, and softlink
(ln -s) the empty tiles to the appropriate places.
This command-line utility can also optionally read in a GeoJSON file of an area of interest.
In this case, only tiles within that area are downloaded in a higher zoom level, whereas tiles completely outside
the area are only downloaded to a lower zoom level;
both zoom levels are also configurable.
See the <a
href="https://github.com/UniStuttgart-VISUS/generate-mercator-tiles-from-srtm#generate-download">documentation
in the repository and the command-line utility’s help (-h) output for more details.
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